Birth and Death Process under the Influence of Catastrophes
Authors:- M. Reni Sagayaraj, R. Roja, S. Bhuvaneswari
Abstract- Birth and death process have been studied very extensively in the past (see kendall (1948), bartlett (1955), feller (1957), harris (1963) and bailey (1964)). recently such processes have been studied allowing disasters to occur randomly over time decrementing the population size (see brockwell et al (1982), pakes (1987), bartoszynski et al(1989), buhelr and puri (1989) and peng et al (1993)). the motivation to study these processes stem from the fact several biological populations (for example, ungulate populations on sub-artic islands and populations of grizzy bears in yellowstone park) exhibit this type of behaviour (for detailed account of such examples, see hanson and trckwell (1987)). catastrophes are instantaneous events, each killing some of the members of the population who are present at the time of occurrence of the disaster.
A Comparative Analysis of Weather Forecasting Techniques
Authors:- Prashant Shivhare, Shivank Soni
Abstract- The annual rainfall of India has three seasons per year accounting for about 11% each in the pre-monsoon (January-May) and the northeast monsoon (October-December)and 78% in the southwest monsoon season also known as summer monsoon (June-September). The maximum amount of the rainfall occurs during southwest monsoon (SWM), which governs the agricultural economy of India and hence for administrative purposes. While the season recurs annually, the variation about the long term expected value can be as high as 40-50% in some parts of the country. Variability during SWM season is an uncertain quantity which India faces every year. This uncertainty cans be year to year, season to season (within year), month to month (within season and with inyear) and so on depending on the requirement in the practical purposes. The hugevariation in the rainfall causes droughts and floods. The distress caused by droughts and floods due to extreme variations of the monsoon can be mitigated to some extent if the rainfall time series can be modeled efficiently for simulation and forecasting of SWM data. Hence this becomes the primary reason to develop new models for Indian monsoon rainfall. Rainfall data is a strongly non-Gaussian time series exhibiting non-stationarity.The main objective of the present paper is to compare new statistical approaches to model and forecast Indian monsoon rainfall data. The prediction of earthquakes, floods, rainfalls are predicted by linear data using least square methods. However, in reality this data is non-linear and varies over a period of time, therefore these models failed to give exact results. To overcome this disadvantage the researcher has considered the models based on time series together with data mining techniques for effective prediction. Most of the weather data contains hidden patterns, therefore data mining techniques help to identify these hidden patterns more accurately. Therefore it is necessary to predict weather changes more significantly. The proposed work is highlighted in this direction. In this paper, an attempt is made compare weather prediction models based on the spatial and temporal dependencies among the climatic variables together with forecasting analysis.
Breast cancer Prediction using Deep Learning Technique
Authors:- M. Tech. Scholar Adarsh Gupta, Prof. Sachin Mahajan
Abstract- Breast cancer is the second most frequent form of the disease, behind lung cancer. The most prevalent kind of cancer is that of the lung. Women of reproductive age are more likely to be diagnosed with breast cancer than men. Early detection of breast cancer is essential for reducing the death rate; this is due to the fact that the actual cause of breast cancer is unclear. Early detection of cancer may increase the likelihood of survival by up to 8%. This includes X-rays, mammograms, and even MRIs in certain cases. What’s the matter even the most skilled radiologists have difficulty recognizing minute lumps, bumps, and masses, which results in a large number of false positives and false negatives? This is a really bad sign. A great number of people have the goal of creating more effective apps to diagnose breast cancer at an earlier stage. Photos may now be analyzed by new technology, which can then learn from the results. We used a Deep Convolutional Neural Network (CNN) in this investigation to differentiate between calcifications, masses, asymmetry, and carcinomas. Earlier studies made use of fundamental algorithms to accomplish this goal. The cancer was categorized as either benign or malignant, which made it possible to provide more effective treatment. An earlier training session had been completed for the model. To begin, we put this approach to use in order to successfully complete transfer learning. ResNet50. In a similar vein, we enhanced our model for deep learning. During the process of neural network training, the importance of its learning rate cannot be overstated. The learning rate may be adapted to changes using the method that we provide. When one is first being educated, they will make several mistakes.
A Review of Breast Cancer using Machine Learning
Authors:- M.Tech. Scholar Adarsh Gupta, Prof. Sachin Mahajan
Abstract- Breast cancer is, after lung cancer, the most prevalent form of the disease in the globe. Women are the demographic most likely to be affected by this condition. Breast cancer is the most common kind of cancer to result in a woman’s death if she is of childbearing age. Because there is always more to learn and there is room for improvement in every line of work, medical imaging is not an exception to this rule. It is expected that the death rate associated with cancer would decrease if it is discovered early and effectively treated. The diagnosis accuracy of persons working in the health care profession may be improved via the use of machine learning techniques. The technique known as deep learning has the potential to differentiate between breasts that are healthy and those that have cancer (also known as neural networking). This method might be used to differentiate between healthy breast tissue and breast tissue affected by illness. Long-term research on the topic aimed, among other things, to examine breast cancer and screening practices among Indian women. This was one of the primary goals of the inquiry. A literature study was carried out with the assistance of several databases along with additional sources. Participants in the study were instructed to use phrases linked to breast cancer such as “breast carcinoma” and “breast cancer awareness,” in addition to terms such as “knowledge” and “attitude,” as well as the gender neutral term “women.” In addition, India had a role in the study that was done. This search does not look for articles that have been published in the English language in the last 12 years.
A Comparison of Social Security Agency’s Efficiency in Indonesia: Pre and During Covid-19
Authors:- Krisna Winda Putri , Muhammad Firdaus, Syamsul Hidayat Pasaribu
Abstract- The Covid-19 outbreak have brought detrimental effect for social and economic sectors. Many workers get laid-off, and firms get bankruptcy. As the impact, the rate of unemployment becomes higher globally, including Indonesia. This issue has some impact to the operational of social security agency for employment. To be compared with 2019, some performance indicators like number of participants experienced declining 2020, and it was resulted to contribution revenue. Efficiency measurement should be performed in order to analyse whether social security agencies had operated efficiently. This research used 30 branch offices to be the samples. To calculate the efficiency value, Data Envelopment Analysis (DEA) method had be functioned. Based on the findings, branch offices become more efficient in pandemic situation than previous year. In 2020, there were 17 efficient branch offices, meanwhile its last year, only 12 branch offices which operated perfectly significant. Suggestion for the institution were optimizing the usage of inputs, strengthening the role of external agent, collaborating with the government and law enforcement, and doing some publication to get people’ awareness.
Regional Sustainability Of Pension System In Indonesia
Authors:- Lahvem Alginda, Yeti Lis Purnamadewi, Sahara
Abstract- As of 2015, BPJS Employment manages pension social insurance for Indonesian citizens. The age of this pension system is still relatively new and continuous improvements still need to be made. The financial management technique used is Pay As You Go (PAYG). There are many factors that affect the sustainability of PAYG pension system, starting from demographic aging factors to macroeconomic factors. This study will use the life expectancy variable as a demographic aging parameter; GDP Per Capita and Unemployment rate as macroeconomic parameters and emigration as one of the labor market related factors. Because Indonesia is a very large country, this sustainability assessment is carried out at the regional level. This study aims to conduct an assessment of the sustainability of pension seystem in 11 BPJS Employment regional offices which cover 34 provinces. The analysis method used is Importance – Performance Analysis (IPA). It was found that there are several regions that are in quadrants I and II, namely Quadrant I: GDP Per Capita (Regions 10 and 11); Life Expectancy (Regions 10 and 11); Unemployment Rate (Regions 7 and 11); Emigration: Region 7, 10 and 11. Meanwhile for Quadrant II: GDP per Capita (Region 3 and 7); Life Expectancy (Regions 3 and 7); Unemployment Rate (Region 3 and 10) and Emigration (Region 3). Pension administrators together with the Indonesian government can focus on variables and regions that are in quadrants I and II to maintain the sustainability of pension system.
“Analysis & Prediction of Heart Attack using Machine Learning”
Authors:- Kumar Saurav, Hritwiz Yash, Affan
Abstract- Heart-related sicknesses or Cardiovascular Diseases (CVDs) are the fundamental justification behind countless demise on the planet throughout recent many years and have arisen as the most perilous infection, in India as well as in the entire world. In this way, there is a need for a solid, precise, and practical framework to analyze such infections in time for legitimate treatment. AI calculations and strategies have been applied to different clinical datasets to computerize the examination of enormous and complex information. Numerous scientists, lately, have been utilizing a few AI strategies to assist the well-being with the caring industry and the experts in the determination of heat-related sicknesses. The heart is the following significant organ contrasting with the mind which has a greater need in the Human body. It siphons the blood and supplies it to all organs of the entire body. The expectation of events of heart illnesses in the clinical field is huge work. Information examination is valuable for forecasting from more data and it assists the clinical focus with anticipating different illnesses. An enormous measure of patient- related information is kept up with on a month-to-month premise. Put-away information can be helpful for the wellspring of foreseeing the event of future infections. A portion of the information mining and AI procedures are utilized to anticipate heart infections, like Artificial Neural Network (ANN), Random Forest, and Support Vector Machine (SVM). Prediction and diagnosing of coronary illness become a difficult variable looked by specialists and clinics both in India and abroad. To decrease the enormous size of passing from heart illnesses, a speedy and proficient recognition strategy is to be found. Information mining strategies and AI calculations assume a vital part around here. The scientists speeding up their examination attempts to foster programming with the help of AI calculations which can assist specialists with choosing both expectations and diagnosing coronary illness. The fundamental goal of this examination project is to foresee the coronary illness of a patient utilizing AI calculations.
A Review on Design Optimisation and Structural Analysis Of Piston
Authors:- M.Tech. Scholar Ajay Shrivas, Prof. Prakash Kumar Pandey
Abstract- Heart-related sicknesses or Cardiovascular Diseases (CVDs) are the fundamental justification behind countless demise on the planet throughout recent many years and have arisen as the most perilous infection, in India as well as in the entire world. In this way, there is a need for a solid, precise, and practical framework to analyze such infections in time for legitimate treatment. AI calculations and strategies have been applied to different clinical datasets to computerize the examination of enormous and complex information. Numerous scientists, lately, have been utilizing a few AI strategies to assist the well-being with the caring industry and the experts in the determination of heat-related sicknesses. The heart is the following significant organ contrasting with the mind which has a greater need in the Human body. It siphons the blood and supplies it to all organs of the entire body. The expectation of events of heart illnesses in the clinical field is huge work. Information examination is valuable for forecasting from more data and it assists the clinical focus with anticipating different illnesses. An enormous measure of patient- related information is kept up with on a month-to-month premise. Put-away information can be helpful for the wellspring of foreseeing the event of future infections. A portion of the information mining and AI procedures are utilized to anticipate heart infections, like Artificial Neural Network (ANN), Random Forest, and Support Vector Machine (SVM). Prediction and diagnosing of coronary illness become a difficult variable looked by specialists and clinics both in India and abroad. To decrease the enormous size of passing from heart illnesses, a speedy and proficient recognition strategy is to be found. Information mining strategies and AI calculations assume a vital part around here. The scientists speeding up their examination attempts to foster programming with the help of AI calculations which can assist specialists with choosing both expectations and diagnosing coronary illness. The fundamental goal of this examination project is to foresee the coronary illness of a patient utilizing AI calculations.
A Review on Design Optimisation of Connecting Rod
Authors:- M.Tech.Scholar Arvind Kumar Lodhi, Prof. Prakash Kumar Pandey
Abstract- Connecting rod is a component inside of an internal combustion engine. The piston is connected to the crank by connecting rod and it is the principal part to transmit power from the piston to the crankshaft. In terms of structural stability and performance, it is considered a critical factor. The main effort in reducing weight has been to optimize the form and remove materials, which is not often possible. In order to manufacture lightweight connecting rod. Furthermore, the connecting rod is a vital component of high volume production output. The reciprocal piston is connected to the rotating shaft and the piston thrust is sent to the shaft. Each motor that uses an inner combustion engine contains, based on the engine number of cylinders, at least one connecting rod. It is only rational to optimize the connecting rod design. The goal may also be met to lower the engine part weight and thereby reduce inertia loads, reduce motor weight, and improve motor efficiency and save power.
How Do The Employee Competencies, Product Innovation, Benefits, And Pricing Affect Service Quality: A Case Study Of BPJS Ketenagakerjaan
Authors:- Mochamad Azkha Rinaldhy, Ma’mun Sarma , Heti Mulyati
Abstract- BPJS Ketenagakerjaan has challenges in maintaining active participation in the self-employed sector, although this is mandatory according to the Regulation of the Minister of Manpower of the Republic of Indonesia Number 1 of 2016, due to the nature of registration based on the awareness of each individual and there is no obligation to pay fines if they do not pay contributions, making many self-employed participants not committed to paying contributions. This research aims to determine whether employee competencies, product innovation, benefits, and price affect service quality. The study used a questionnaire to collect the data from 200 participants of BPJS Ketenagakerjaan in the West Nusa Tenggara area. The analytical method used Logistic Regression and SEM analysis. The results showed that only product innovation had no significant effect on service quality.
Structural Analysis Of Rcc T-Girder Bridge With Different Loading Condition Using Staad Pro
Authors:- PG Student Pooja Sharma, Asst.Prof. Aslam Hussain
Abstract- In order to facilitate access across physical impediments like a water ways, valley, or highways, bridges are those constructions that are created to span them without blocking the way underneath. It is possible to create a prediction model that is capable of predicting structural behaviour of RCC T-girder bridges in terms of effectiveness using various span conditions, T girder shows better outcomes when compared to other beam deck which is economical for shorter spans, and with increasing the length of span dead load also increase . This is due to researchers’ growing interest in bridge modelling by using different span condition to check effectiveness of girder. On increasing the length of span, the requirements of cross girders (diaphragms) will also increases as to get desired effectiveness between main girders. For this a database from previous literature is collected and model has been developed by using staad Pro. This model can be used for determining the bending moment, shear, torsion and displacement of RCC-T girder by considering various loads, span condition simultaneously. The main objective of this paper is to check whether the nature of girder on different span is significant or notand best suited configuration and location of displacement on RCC T girder is analysed. The present analyses are carried out in stadd pro software. There are four of them: IRC 21-2000, IRC 5-2015, IRC 6-2016, and IRC 112-2011.
An Improvisation of Strength Parameters of Rigid Pavements by Using Industrial Wastes: A Review
Authors:- Assistant Professor Pusa Sai Sudha, Associate Professor Dr. Srikanth Ramvath
Abstract- Pervious cement is an extraordinary high porosity concrete utilized for flatwork applications that permits water from precipitation and different sources to go through, in this way lessening the overflow from a site and re-energizing ground water levels. Its void substance goes from 18 to 35% with compressive qualities of 2.74 to 27.56 MPa . Regularly, pervious cement has practically zero fine total and has barely sufficient cementitious glue to cover the coarse total particles while protecting the interconnectivity of the voids. Pervious cement is generally utilized in stopping regions, regions with light traffic, person on foot walkways, and nurseries and adds to supportable construction.In this venture we are utilizing scrap marble to make pervious cement and furthermore checking different boundaries like porousness and compressive strength concerning various kinds of total like precise, adjusted, and flaky sort. 3D squares produced using a wide range of total where projected and compressive strength test (at 7 and 28 days) alongside invasion test (at 28 days) where done.
Machine Learning Based Approach for Brain Tumor Detection
Authors:- Dr.E.Shanmugapriya , O.Rajasekar
Abstract- Automated defect detection in medical imaging has become the emergent field in several medical diagnostic applications. Automated detection of tumor in Magnetic Resonance Imaging (MRI) is very crucial as it provides information about abnormal tissues which is necessary for planning treatment. The objective of this project is to analysis the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. The availability of an automated computer analysis tool that is more objective than human readers can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. A computer-assisted classification method combining conventional MRI and perfusion MRI is developed and used for differential diagnosis. The proposed scheme consists of several steps including ROI definition, feature extraction, feature selection and classification. The extracted features include tumor shape and intensity characteristics as well as rotation invariant texture features. Feature subset selection is performed using Support Vector Machines (SVMs) with recursive feature elimination. The Convolution neural network method for defect detection in magnetic resonance brain images is human inspection. This method is impractical for large amount of data. So, automated tumor detection methods are developed as it would save radiologist time. The MRI brain tumor detection is complicated task due to complexity and variance of tumors. In this paper, tumor is detected in brain MRI using convolution neural network algorithm. The proposed work is divided into three parts: preprocessing Segmentation and classification steps are applied on brain MRI images, texture features are extracted using Gray Level Co-occurrence Matrix (GLCM),DWT and then classification is done using svm algorithm.
The Effect of Investment on Youth Unemployment Rate in Indonesia
Authors:- Fatkhu Rokhim, Tanti Novianti, Lukytawati Anggraeni
Abstract- This study aims to analyze the effect of investment (domestic and foreign investment) as well as other factors on youth unemployment in Indonesia. This study uses secondary data obtained from the Central Statistics Agency (BPS) and the Coordination and Investment Agency (BKPM). The data used is panel data from time series data for 2015 – 2021 and cross sections covering 34 provinces in Indonesia. The results of the descriptive analysis show that there are provinces that have high investment but also have high youth unemployment, such as the provinces of South Sumatra, West Java, Banten, Central Sulawesi and North Maluku. The results of the panel data regression analysis show that the domestic investment has a positive and significant influence on youth unemployment in Indonesia. The government through the Coordination and Investment Agency (BKPM) is expected to encourage large companies entering Indonesia to collaborate with local companies and Micro Small Medium Enterprises (MSMEs) to focus more on labor-intensive industries.
Inter laminar Fracture of Aerospace Composites Materials
Authors:- Research Scholar Imran Abdul Munaf Saundatti, Dr. G R Selokar
Abstract- The interlaminar fracture toughness is a measure of the capacity of material to oppose delamination. The experimental assurance of the protection from delamination is significant in aviation applications. Distinctive sort of examples and experimental methods are utilized to measure the interlaminar fracture toughness of composite materials. The point of the present research is to pick up a superior comprehension of interlaminar facture of polymer framework composites in various modes, and to create scientific model to anticipate the critical strain energy discharge rates. Accentuation has been set on the root revolution at the crack tip which was accepted to be a critical factor which influences the delamination fracture toughness, and critical burden. A joined experimental and hypothetical investigation has been directed to decide the job of root revolution on critical burden.
Enterprises Social Security Employment Contributions During Covid-19 Pandemic
Authors:- Setyo Ardy Gunawan, Sahara, Yeti Lis Purnamadewi
Abstract- The implementation of social restrictions during the COVID-19 pandemic caused an economic slowdown and made it difficult for many enterprises to keep running, including the obligation to pay social security contributions for employment. To overcome the issue, the government provides policy to ease the burden on enterprises and avoid the occurrence of labor layoffs. However, there are still many companies that are laying off their workers during the pandemic and cause the unemployment rate increased resulting in a decrease in the number of contributions paid by enterprises for employment social security participation. If this problem persists, the sustainability of social security funds will be threatened and payment of benefits to participants will be disrupted. This study aims to analyse the changes on the contributions, registered labor, and reported wages of enterprises toward social security participation before and during the pandemic. The objective will be addressed by analysing contribution paid, number of registered workers, and total wages reported by enterprises before and during the COVID-19 pandemic with a tabular descriptive analysis using a paired t-test. The result indicates that there is a significant decrease in contributions, registered labor, and reported wages for enterprises during the pandemic compared to before the pandemic.
IPL First Innings Score Prediction Using Machine Learning Techniques
Authors:-Mayank Agarwal, Prof. Dr. Archana Kumar
Abstract- In India, Cricket is one of the most watched and most played sports. India Cricket team calendar is action packed throughout the year and they don’t even get rest for even a single month like other countries. So, this huge popularity of cricket has resulted in introduction of Indian Premier League (IPL) by BCCI, India. Now it is conducted among 10 teams. It was started by having 8 teams in the tournament. Since the start of the tournament, it has become the largest and biggest event of cricket in the whole world. People really enjoy this tournament and different players from different playing countries are part of the IPL as well. In this paper we made the model for score prediction using different machine learning regression techniques. In this the different score prediction includes linear regression, lasso regression and ridge regression and then we have calculated the accuracy of each algorithm and chosen the best one. The model used the supervised machine learning algorithm to predict the IPL first Innings Score. In our model the linear regression gave the best result in comparison of the other algorithms so we are using it.
Examining Machine Learning’s Diagnostic Potential for Glaucoma
Authors:- M. Tech. Scholar Aarti Patidar, HoD & Prof. Kamlesh Patidar
Abstract-In order to give an automated diagnosis of glaucoma, the purpose of this review paper is to investigate the use of a variety of image processing methods. Glaucoma is a disease of the optic nerve that is caused by damage to the nervous system. It is possible for a person to progressively lose all or part of their eyesight if the condition is not addressed and is allowed to go unchecked. It is true that a sizeable number of persons living in the world’s rural and semi-urban regions suffer from eye problems; however, the same can be said for every other setting as well. The processing of pictures produced via the examination of photos of the fundus of the retina is now used almost entirely in the process of diagnosing retinal disorders. Image registration, picture fusion, image segmentation, feature extraction, image enhancement, morphology, pattern matching, image classification, analysis, and statistical measurements are some of the fundamental image processing techniques for diagnosing eye diseases. Other techniques include image enhancement, morphology, and pattern matching.
Strategic Framework for Managing Transformational Change Towards Sustainability in Ethiopian Banking Industry
Authors:- Abreham Tesfaye Abebe (Ph.D.)
Abstract-The study aims at developing strategic framework for managing transformational change towards sustainability in Ethiopian banking industry. The study was guided by five critical research questions so that it can be aligned to the core points of the study. To make it representative, the researcher made an attempt to include three private commercial banks in Ethiopia that entered to the industry in various periods. The samples were taken from the selected banks, most importantly, the senior executive leadership, middle level management and senior experts in the area. Following the development of the framework using the environmental, social and economic dimensions of sustainability, it was validated with fifteen professionals who have over 20 years of work experience in Ethiopia banking industry. Questionnaires and interview methodologies were employed in the study and it is recommended as sustainability shall be understood in a more holistic perspective having the three dimensions (environmental, economic and social) in to consideration. Besides, continuous training shall be conducted on the concept of sustainability in relation to banking business, performance management in that regard shall also be conducted, and the Bank’s community shall clearly know that where can they contribute towards the management of change initiatives towards sustainability.
Self-Repairable Multiplexer in Real Time for Fault Tolerant Systems
Authors:- T. Pavani Reddy, Assistant Professor D. Srikanth
Abstract- As a result of VLSI, more transistors can be packed onto a single chip. The system or chip is more likely to malfunction when the distance between transistors or circuits decreases. Fault-tolerant systems are crucial for preventing inaccurate conclusions. A multiplexer is an apparatus that selects one or more input signals based on another signal. Only self-verifying multiplexers have been the sole focus of prior writings. In this research, we present a 2:1 multiplexer that can fix both permanent and transient mistakes on its own. Two distinct architectures for a self-repairing multiplexer are introduced. The multiplexer mistake is corrected in the first design by means of supplementary circuitry. In the second design, the multiplexer’s construction blocks including OR and AND gates are self-repairing. These self-healing multiplexer layouts can recognize and fix both single- and multiplexer-level problems. These self-healing multiplexer layouts are able to identify and fix a wide variety of errors. All errors can be recovered in the proposed designs. The Cadence tool verifies the circuits’ functionality. Mentor graphics CMOS Technology at 45nm was used to verify the aforementioned project.
A Review Paper Presenting an Overview of Various Tests Conducted in the Field of Steel Fibre Reinforced Concrete
Authors:- Dr. Heleena Sengupta*, Nayana Tatyasaheb Mairal, Taniya Basu, Saurabh Raj, Aditya Kumar Jha, Sneha Kaveri, Vishwajeet Pratap Singh
Abstract- Concrete has a high compressive strength but a low tensile strength, which is well known in the civil engineering community. This is the main cause of sudden/brittle failure in concrete. The material is unable to slowly stretch out and give sufficient warning and time for evacuation before failing. This is the main reason why steel is widely used in the tensile zone of reinforced concrete sections to make up for its lack of tensile strength. In recent years, the concept of composite materials came into being, and fibre-reinforced concrete (FRC) was one of the topics of interest1. It showed fascinating advantages when compared to plain and reinforced concrete, thus leading to increased research regarding it. The purpose of this paper is to review and summarise open-source papers published since 2011 presenting various tests conducted on steel fibre reinforced concrete, conduct a gap analysis on the results if possible, and identify the future scope of further research in the field.
Requirement from Unsupervised Machine Learning to Prediction of Academic Performance of Students
Authors:- M. Tech. Scholar Simran Aliwal, Assistant Professor Abhay Mundra
Abstract- The ability to monitor the progress of kids’ academic performance is an important factor. An essential problem with the academic community’s claim to a larger percentage of taking in. It is possible to depict a system for analyzing the results of students’ work that is based on the analysis of groups of students’ work and that makes use of standard quantifiable calculations to organize the students’ test scores and information according to the level at which they performed. In this study, we also implemented the k-mean grouping technique in order to analyze the information about the students’ consequences. It’s possible that those models were consolidated for the deterministic models, in which case those models should analyze the impacts of a private foundation on those kids. Iberia, which is a great benchmark with screen the progression of academic execution about people for higher institutional to the reason for making a successful choice by those academic organizers Iberia is a great benchmark with screen the progression of academic execution about people for higher institutional.
An Examination of the Data Collected on Twitter Regarding Food Using a Machine Learning Classification Method
Authors:- M.Tech.Scholar Sakshi Patidar, Prof. Kamlesh Patidar
Abstract- Most individuals use Facebook and Twitter to communicate globally. Twitter illustrates. Daily live news, ratings for brands, items, businesses, and locations, and user reviews develop community. This project removes bogus news from Kaggle’s Twitter data sets and analyzes Twitter API sentiment. Why? Tokenize and remove stop words from Twitter data before processing. Feature extraction follows. These mechanisms evaluate each word. Testing several noisy data-trained models. Twitter sentiment analysis machine learning classifiers are tested. KFC and McDonald’s provide data sets with over 14,000 tweets and more popular themes. Testing has 4,000 tweets and training 10,000. Our method analyzed these models’ outcomes after modifying their parameters. Performance evaluations improve sentiment analysis.
Job Satisfaction of Employees Working in FMCG Sector
Authors:- Asst. Prof. Dr. Bijal Shah, Dolly Tailor, Hasmita Rathod
Abstract- Job satisfaction is a the most important thing for improving the performance of employees and maintaining the relationship between employers and employees. It is very important because a significant amount of person’s life is spent at their workplace. Through the research work we propose to measure the level of satisfaction and factors influencing the level of job satisfaction among the employees of the FMCG sector. For undergoing the research work we will be using both primary data and secondary data. For the analysis part we have selected the employees of beverages manufacturing company. The need for the study is arises considering the HR theories that improves job satisfaction result into higher level of self- satisfaction which get reflected integration of individual goals to organizational goals.
A Review On Multistoried Earthquake Resistant Building
Authors:- M.Tech. Scholar Shyam Kumar, Prof. Afzal Khan
Abstract- The economic growth and rapid urbanization in hilly region has accelerated the real estate development and resulted in increase in population density in the hilly region enormously. Therefore, there is popular and pressing demand for the construction of multi-storey buildings in that region. A scarcity of plain ground in hilly area compels the construction activity on sloping ground. Hill buildings behave different from those in plains when subjected to lateral loads due to earthquake. Such buildings have mass and stiffness varying along the vertical and horizontal planes, resulting the centre of mass and centre of rigidity do not coincide on various floors. Also due to hilly slope these buildings step back towards the hill slope and at the same time they may have setback also, having unequal heights at the same floor level the column of hill building rests at different levels on the slope.
A Review Paper Presenting an Overview of Various Tests Conducted in the Field of Steel Fibre Reinforced Concrete
Authors:- Dr. Heleena Sengupta, Nayana Tatyasaheb Mairal, Taniya Basu, Saurabh Raj, Aditya Kumar Jha, Sneha Kaveri, Vishwajeet Pratap Singh
Abstract- Concrete has a high compressive strength but a low tensile strength, which is well known in the civil engineering community. This is the main cause of sudden/brittle failure in concrete. The material is unable to slowly stretch out and give sufficient warning and time for evacuation before failing. This is the main reason why steel is widely used in the tensile zone of reinforced concrete sections to make up for its lack of tensile strength. In recent years, the concept of composite materials came into being, and fibre-reinforced concrete (FRC) was one of the topics of interest1. It showed fascinating advantages when compared to plain and reinforced concrete, thus leading to increased research regarding it. The purpose of this paper is to review and summarise open-source papers published since 2011 presenting various tests conducted on steel fibre reinforced concrete, conduct a gap analysis on the results if possible, and identify the future scope of further research in the field.
Dynamic Voltage Restorer for Power Quality Enhancement of Three Phase Grid-Tied Solar- PV System
Authors:-M.Tech. Scholar Sunita Khairwar, Assistant Professor Achie Malviya
Abstract- The consumption of power is more due to high invention and more number loads. The most of the loads are nonlinear loads, causes the harmonic currents in the system. These harmonic currents in turn create system resonance, capacitor overloading, decrease in efficiency, voltage magnitude changes. Power quality has become an increasing concern to utilities and customers. The power transmitting in a distribution line is needed to be of high quality. One of the major power quality issues is considered in the distribution system called Voltage sag and can mitigate with the help of dynamic voltage restorer. In this paper, Focusing on the novel integration of solar PV-Battery based Dynamic Voltage Restorer is implementing in the distribution system to meet the necessary power and for power quality improvement. Solar photovoltaic is integrated on the dc side of the inverter for handling the excessive load demand. The performance of solar photovoltaic, Battery with Dynamic Voltage restorer is simulated under dynamic conditions of the load in MATLAB-SIMULINK software.
A Review on Custom Power Devices for Voltage Quality Improvement
Authors:-M.Tech. Scholar Sunita Khairwar, Assistant Professor Achie Malviya
Abstract- Power quality is a pressing concern and of the utmost importance for advanced and high-tech equipment in particular, whose performance relies heavily on the supply’s quality. Power quality issues like voltage sags/swells, harmonics, interruptions, etc. are defined as any deviations in current, voltage, or frequency that result in end-use equipment damage or failure. Sensitive loads like medical equipment in hospitals and health clinics, schools, prisons, etc. malfunction for the outages and interruptions, thereby causing substantial economic losses. For enhancing power quality, custom power devices (CPDs) are recommended, among which the Dynamic Voltage Restorer (DVR) is considered as the best and cost-effective solution. DVR is a power electronic-based solution to mitigate and compensate voltage sags. This paper provides a thorough discussion and comprehensive review of DVR topologies based on operations, power converters and voltage quality issues.
Determinants of Government External Debt: Assessing Government Revenues from Tax Amnesty
Authors:-Shofiyah Salsabila, Hermanto Siregar, Dedi Budiman Hakim
Abstract- The expansive economic policy is applied in Indonesia as seen from the greater expenditure than revenue. Accordingly, the government took steps to make external debt to fund the expenditure. This decision is taken to catch up with the overseas economic growth. Therefore, the government external debt become essential to be monitored since this decision has an impact on the economy of the recipient state. The problem of this research focused on analyzing the factors that influence government external debt and reviewing government revenues through Tax Amnesty on Indonesian external debt in the short and long term. This research uses secondary data from 1981-2021 concerning the relationship between government expenditure, currency rate, rupiah exchange rate, inflation, tax, government securities (SBN), and tax amnesty policy toward government external debt. Using the ARDL bound test with structural break as the econometric approach and Dummy Test Tax Amnesty. The result of this study explains that government expenditure lag 1, BI rate, exchange rate, exchange rate lag 1, inflation, tax, tax lag1, government securities, government securities lag 1, and Tax Amnesty are significant for short-term government expenditure. Besides, only inflation, tax, and government security are significant for the long term.
Dynamic Analysis of Thermal Stresses in a Semi-Infinite Solid Circular Cylinder
Authors:-J. J. Tripathi
Abstract- This paper presents an analysis of the thermoelastic response of a semi-infinite solid circular cylinder subjected to an arbitrary initial heat input on its lower surface, while the curved surface is thermally insulated. The study employs a dynamic approach based on potential functions to model the system. The resulting expressions for temperature distribution and thermal stresses are derived using Bessel’s functions. To demonstrate the applicability of the model, copper (pure) is selected as the material, and the outcomes are visualized graphically, highlighting the thermal and mechanical behavior under dynamic conditions.
DOI: 10.61137/ijsret.vol.9.issue1.260
Multilevel Authentication System Based on Periocular Features Using Deep Learning Algorithm
Authors:-Nivetha L, Mohan P, Thanga Thamizh/strong>
Abstract- The iris recognition biometric technique faces limitations primarily due to the high costs associated with optical equipment and the inconvenience experienced by users. As an alternative, periocular-based methods offer a viable solution for biometric authentication, as they do not necessitate costly devices. Furthermore, the data obtained from these methods are valuable for biometrics since they capture features such as eyelashes, eyebrows, and eyelids. However, traditional periocular-based biometric authentication techniques rely on restricted sets of features based on the chosen feature extraction method, leading to comparatively subpar results. Consequently, we introduce a deep-learning approach that makes full use of the diverse features present in periocular images. This method preserves the mid-level features from the convolutional layers and selectively incorporates those that are most beneficial for classification. We evaluated the proposed approach against prior methods using both publicly available and self-gathered datasets. The results of the experiments indicate an equal error rate of less than 1%, outperforming earlier techniques. Additionally, we present a novel methodology to assess whether mid-stage features have been effectively utilized. As a result, it was demonstrated that this strategy, which leverages mid-level features, significantly enhances the performance of feature extraction within the network.
DOI: 10.61137/ijsret.vol.9.issue1.132
Adaptive Server Hardening in Mission-Critical Biomedical Systems
Authors: Ekaterina Morozova, Ivan Petrov, Natalia Smirnova, Alexey Volkov
Abstract: Biomedical computing environments face a unique set of challenges in securing critical infrastructure while maintaining the high availability, performance, and regulatory compliance required for sensitive healthcare and research workloads. From electronic medical record (EMR) systems and genomics data pipelines to real-time telemedicine platforms, these systems demand adaptive and resilient security architectures. Traditional static hardening techniques—based on fixed baselines, manual patching, and predefined firewall rules are increasingly insufficient in the face of dynamic threat landscapes, complex workloads, and ever-evolving compliance mandates like HIPAA, HITECH, and 21 CFR Part 11. This review explores the concept of adaptive server hardening, a modern, behavior-driven approach that dynamically adjusts server configurations, access controls, and security policies based on real-time telemetry, system state, and threat intelligence. It examines OS-specific strategies across Red Hat, Solaris, and AIX platforms, highlighting tools like SELinux, SMF, Trusted AIX, ZFS ACLs, and live patching utilities. Key technologies include behavior-based anomaly detection, AI-assisted rule tuning, and integration with SIEM and EDR platforms such as Tripwire, Splunk, and OSSEC. Furthermore, the paper addresses runtime configuration drift, automated remediation, privilege management, and audit automation for compliance readiness. Through detailed technical analysis and real-world case studies, the review demonstrates how adaptive hardening improves security posture, supports continuous compliance, and ensures operational continuity in biomedical settings. It also considers challenges such as overhead management, multi-platform complexity, and tuning of dynamic policies. Finally, the article discusses future trends including autonomous compliance agents, AIOps integration, and adaptive security in hybrid and cloud-based biomedical infrastructures.
DOI: https://doi.org/10.5281/zenodo.15847766
Performance Profiling Of Large-Scale Puppet Deployments In UNIX Data Centers
Authors: Santhosh M.,, Keerthana R, Divya Prasad, Ajay Krishna
Abstract: As enterprise UNIX data centers scale to manage thousands of nodes, the performance of automation frameworks like Puppet becomes critical to ensure consistency, speed, and resilience. Puppet, a leading configuration management tool, plays a pivotal role in implementing infrastructure-as-code across Solaris, AIX, and Linux environments. However, large-scale deployments introduce performance challenges due to the complexity of resource catalogs, variable agent execution times, and infrastructure-induced latency. Performance profiling becomes essential to identify and resolve inefficiencies that affect convergence speed, system reliability, and orchestration throughput. This review explores the key dimensions of profiling Puppet in UNIX data centers, including catalog compilation time, agent runtime, resource evaluation delay, and infrastructure throughput. It outlines available profiling tools such as the Puppet profiler, Facter benchmarking, and external instrumentation using DTrace and perf, as well as real-time logging and observability integrations. By examining performance metrics and common bottlenecks—ranging from plugin synchronization delays to fact resolution issues—this article highlights optimization strategies including manifest refactoring, compile master pools, and External Node Classifier (ENC) tuning. Furthermore, it analyzes real-world deployment scenarios from financial, academic, and hybrid UNIX-cloud environments to contextualize challenges and solutions. The review also contrasts Puppet with other configuration management tools like Ansible and Chef, while addressing limitations such as visibility gaps in custom resources and version-specific regressions. Finally, future directions such as ML-based run prediction and integration with AIOps and observability platforms are proposed to advance performance-aware automation at scale. This article aims to provide system architects and automation engineers with practical insights for maintaining high-performing Puppet environments in mission-critical UNIX infrastructures.
DOI: https://doi.org/10.5281/zenodo.16157635
Implementing Virtualized Disaster Recovery Solutions To Ensure Business Continuity In Financial Institutions During System Failures And Crises
Authors: Arundhati Roy
Abstract: The exponential growth of data and the increasing complexity of enterprise networks have necessitated scalable, secure, and reliable file-sharing solutions. Samba, an open-source implementation of the SMB/CIFS protocol suite, has emerged as a widely adopted technology for enabling seamless file and print services across Unix/Linux and Windows systems. This article explores the architecture, operational principles, and scalability strategies associated with the Samba protocol, emphasizing its critical role in cross-platform network interoperability. With features such as domain integration, advanced authentication methods, and cluster-friendly designs, Samba allows organizations to centralize file storage while accommodating diverse client environments. The ability to configure Samba in standalone, domain member, or Active Directory-integrated modes also enhances its versatility and security posture. Additionally, this article examines performance optimization techniques such as load balancing, distributed file systems, and caching mechanisms that facilitate Samba’s deployment in large-scale infrastructures. Real-world use cases, including educational institutions, SMBs, and cloud-backed enterprise setups, illustrate the protocol's practical utility. The study further discusses the security and compliance challenges inherent to Samba-based systems and suggests mitigation strategies like access control lists, encrypted communications, and audit logging. As hybrid IT environments become more prevalent, Samba continues to evolve with better support for containerization, high availability, and cloud synchronization. This paper offers a comprehensive review of Samba’s capabilities, focusing on how to build a scalable network file-sharing architecture that aligns with modern IT standards and operational efficiency.
DOI: https://doi.org/10.5281/zenodo.16751756
Optimizing Load Distribution In Kubernetes Clusters Using Cloud-Native Load Balancing Techniques For Scalable And Resilient Deployments
Authors: Rohinton Mistry
Abstract: As enterprises increasingly shift toward cloud-native infrastructures, Kubernetes has become the de facto standard for orchestrating containerized applications. A fundamental challenge in this dynamic environment is ensuring efficient and reliable distribution of network traffic, commonly referred to as load balancing. Traditional load balancing approaches often fall short when applied to cloud-native architectures due to their lack of agility, scalability, and integration with dynamic workloads. Kubernetes addresses this gap by offering in-cluster load balancing mechanisms through Services, Ingress controllers, and external load balancers that adapt to application and infrastructure changes in real time. This article explores how Kubernetes enables cloud-native load balancing, discussing native components such as kube-proxy, CoreDNS, and Service types, alongside more advanced approaches involving Ingress controllers, service meshes, and cloud-provider integrations. It also investigates common architectural patterns and best practices that ensure high availability, scalability, and optimal resource utilization. Case studies from production environments and comparative analyses of tools like Traefik, NGINX, and HAProxy offer real-world insights into implementation trade-offs. Furthermore, the article delves into the challenges of multicluster load balancing, DNS propagation, and observability in dynamic workloads. As cloud-native adoption continues to grow, understanding and optimizing load balancing in Kubernetes environments becomes critical for developers, DevOps teams, and architects aiming to maintain performance and resilience. This review presents a comprehensive synthesis of cloud-native load balancing strategies, technologies, and practices within Kubernetes clusters, providing a detailed guide for those striving to master the complexities of modern distributed systems.
DOI: https://doi.org/10.5281/zenodo.16751782
Deploying Zero Trust Security Frameworks For Enhanced Protection Across Hybrid Cloud Infrastructures And Multi-Environment Architectures
Authors: Amitav Ghosh
Abstract: In today’s rapidly evolving threat landscape, organizations face unprecedented challenges in securing their digital environments. Traditional perimeter-based security models have become inadequate in the face of sophisticated cyberattacks, increased mobility, and widespread cloud adoption. Zero Trust Security (ZTS) has emerged as a robust cybersecurity model that assumes no implicit trust within or outside the network, requiring continuous verification of users, devices, and workloads. In hybrid cloud environments—where private and public cloud infrastructures coexist and interoperate—the implementation of Zero Trust principles becomes crucial yet complex. This paper explores the strategic integration of Zero Trust Security in hybrid cloud architectures, focusing on identity and access management (IAM), microsegmentation, continuous monitoring, and adaptive policy enforcement. It examines the challenges and solutions for implementing ZTS across heterogeneous platforms, including legacy systems and modern cloud-native services. Case studies and real-world implementations underscore best practices and demonstrate measurable outcomes in risk reduction and operational resilience. With the increasing regulatory requirements and the critical need for data privacy, Zero Trust in hybrid cloud environments is not just a security enhancement but a strategic imperative for enterprises. This comprehensive review provides guidance for CISOs, cloud architects, and security professionals aiming to deploy scalable, resilient, and compliant Zero Trust frameworks across their hybrid infrastructure.
DOI: https://doi.org/10.5281/zenodo.16751838
Analyzing And Comparing The Performance Of SMB And NFS Protocols For Efficient File Sharing In Linux Environments
Authors: Vikram Seth
Abstract: The Server Message Block (SMB) and Network File System (NFS) protocols serve as critical technologies for network file sharing in Linux environments. Both have evolved significantly, with SMB, predominantly championed by Microsoft, and NFS, natively supported in UNIX and Linux systems, each demonstrating unique strengths and use cases. With growing demand for efficient, reliable, and scalable file sharing across distributed environments, choosing the right protocol is essential for optimizing system performance. This article explores the comparative performance of SMB and NFS, examining throughput, latency, CPU usage, security integration, compatibility, and ease of configuration in Linux. Benchmarks, real-world use cases, and theoretical analysis converge to evaluate how each protocol behaves under different workloads and system configurations. The study also emphasizes tuning methods and kernel-level interactions that influence performance outcomes. Administrators often face challenges in determining the most effective protocol for specific network conditions or organizational goals. This review offers a comprehensive framework to assist in those decisions, incorporating both empirical data and architectural insights. We conclude by highlighting the contexts in which each protocol excels and offering guidance on best practices for deployment in hybrid Linux infrastructures
From Code Completion To Collaborative Intelligence: LLM-Enabled Developer Copilots For Java Code Understanding And Refactoring
Authors: Sriram Ghanta
Abstract: The increasing scale and architectural complexity of modern Java codebases often spanning millions of lines across microservices, legacy components, and heterogeneous frameworks has significantly amplified the demand for intelligent developer assistance tools capable of supporting deep program comprehension, efficient debugging, and safe, large-scale refactoring. Large Language Models (LLMs), trained on vast corpora of source code and natural language artifacts such as documentation, commit histories, and developer discussions, have emerged as a foundational technology enabling developer copilots that operate with contextual, semantic awareness rather than surface-level pattern matching. These copilots can interpret developer intent, reason about code behavior across method and class boundaries, and propose transformations that preserve functional correctness. This article examines the evolution of LLM-enabled developer copilots with a specific focus on Java code understanding and refactoring, synthesizing advances in transformer-based architectures, structure-aware code representations that incorporate abstract syntax and data-flow information, and neural program repair techniques that learn corrective patterns from real-world defects. We demonstrate how modern copilots transcend traditional syntactic completion by delivering semantic reasoning, automated bug fixes, refactoring recommendations, and even architecture-level guidance, while also discussing their broader implications for developer productivity, software quality, long-term maintainability, and the future of human–AI collaboration in enterprise software engineering.
DOI: http://doi.org/10.5281/zenodo.18081330
Operational Risk Assessment And Management In Distributed Wireless Cloud–IoT Systems
Authors: Devansh Rithala
Abstract: Distributed wireless cloud–IoT architectures are increasingly critical in enabling real-time monitoring, data analytics, and intelligent decision-making across various industries, including smart cities, healthcare, industrial automation, and agriculture. However, the complexity, heterogeneity, and geographic distribution of these systems introduce significant operational risks that can compromise performance, reliability, and security. This article provides a comprehensive analysis of operational risks in distributed wireless cloud–IoT architectures, including hardware failures, network disruptions, cybersecurity threats, data integrity issues, and cloud service outages. It examines risk assessment and analysis techniques, such as fault tree analysis, failure mode effects analysis, and probabilistic modeling, to identify and prioritize vulnerabilities. The article also presents mitigation strategies, including redundancy, edge computing, network optimization, real-time monitoring, predictive maintenance, and security measures, while discussing challenges in implementation, such as scalability, interoperability, cost, and performance trade-offs. Future directions, including the integration of artificial intelligence, blockchain, next-generation wireless networks, and standardized risk management frameworks, are explored to enhance system resilience. By adopting a proactive and systematic approach to operational risk management, organizations can ensure reliability, efficiency, and sustainability in complex distributed wireless cloud–IoT ecosystems.
DOI: http://doi.org/10.5281/zenodo.18169504
Reengineering IT Infrastructure And Foundations To Enable Scalable, Secure, And Efficient Cloud-Driven Wireless IoT Platforms
Authors: Kashvi Uprex
Abstract: The rapid expansion of wireless Internet of Things (IoT) devices has created unprecedented opportunities and challenges for modern IT infrastructures. Traditional systems often struggle to accommodate the massive data volumes, real-time processing demands, and heterogeneous device ecosystems that characterize IoT deployments. Cloud-driven platforms offer scalable, flexible, and centralized solutions, yet integrating them with wireless IoT networks requires careful reengineering of foundational IT infrastructure. This article explores strategies for designing scalable, secure, and efficient cloud-enabled wireless IoT platforms. Key principles such as microservices-based architectures, edge computing, dynamic resource allocation, and robust security frameworks are discussed in detail. The article also examines cloud infrastructure models, data management techniques, performance optimization, and emerging technologies that enhance IoT capabilities, including AI, 5G/6G, and blockchain. Challenges related to legacy integration, interoperability, security, and sustainability are addressed, alongside recommendations for building resilient and future-ready systems. By providing a comprehensive framework for reengineering IT infrastructure, this work aims to guide organizations in deploying efficient, secure, and scalable wireless IoT platforms that can support the next generation of intelligent, connected applications.
DOI: http://doi.org/10.5281/zenodo.18169506
Smart Monitoring Systems For Patient Care Using AI-Driven Analytics And SAP-Integrated Wearable Devices
Authors: Charvik Konda
Abstract: The rapid transformation of the global healthcare industry from a reactive, hospital-centric model to a proactive, continuous, and patient-centered paradigm is driven by the convergence of wearable technology, artificial intelligence, and enterprise-grade data management. This review article explores the development and implementation of smart monitoring systems that utilize AI-driven analytics integrated within the SAP ecosystem to provide high-fidelity, real-time patient care. By bridging the technical gap between medical-grade biosensors and the SAP Business Technology Platform, healthcare providers can now harness the in-memory computing power of SAP HANA to process massive streams of physiological data. The study investigates how advanced machine learning algorithms, including deep learning for predictive modeling and anomaly detection, transform raw sensor data into actionable clinical insights. These capabilities enable early detection of critical conditions such as sepsis or cardiac distress while minimizing false alerts through intelligent context-aware filtering. We examine diverse clinical applications ranging from post-operative recovery and chronic disease management to elderly care and clinical trials demonstrating significant improvements in patient outcomes and institutional resource optimization. Furthermore, the article addresses the multifaceted challenges of large-scale deployment, specifically focusing on data privacy under HIPAA and GDPR, the technical complexity of ERP integration, and the necessity of explainable AI for clinical trust. By discussing emerging trends such as edge intelligence and the integration of generative AI for enhanced patient engagement, this review provides a strategic framework for health systems. Ultimately, the synergy between wearable hardware and SAP-integrated analytics represents a cornerstone for a more accessible, personalized, and resilient digital healthcare infrastructure.
DOI: http://doi.org/10.5281/zenodo.18228874
An Exploratory Study Of Fog Computing Architectures For Reducing Latency In IoT-Based Healthcare Systems
Authors: Aarush Naidu
Abstract: The burgeoning growth of the Internet of Things (IoT) in healthcare has created a massive influx of data that traditional cloud-based architectures struggle to process with the required speed. Latency in medical monitoring can be catastrophic, leading to delayed responses in life-critical situations such as cardiac events or falls. This exploratory study investigates fog computing as a decentralized solution for reducing latency in IoT-based healthcare systems. We evaluate a three-tier architecture that positions a fog layer between medical sensors and the cloud to enable real-time data filtering, anomaly detection, and immediate localized alerting. The article explores key latency-reduction strategies, including dynamic resource allocation and intelligent computation offloading, which prioritize emergency traffic and minimize network congestion. Furthermore, we address the critical domains of security and privacy, highlighting the use of mutual authentication and local data anonymization to protect sensitive patient records. Through various case studies, we demonstrate that fog architectures can reduce response times by up to 95% compared to cloud-only models. The study concludes by identifying open research challenges in mobility management and interoperability, providing a strategic vision for the future of low-latency, resilient healthcare infrastructures.
DOI: http://doi.org/10.5281/zenodo.18228957
Engineering Distributed Enterprise Platforms In Cloud-Centric Environments
Authors: Malsha Rodrigo
Abstract: The rapid growth of digital services has compelled enterprises to transition from tightly coupled monolithic infrastructures to distributed platforms operating within cloud-centric environments. Traditional enterprise systems, designed for stable workloads and localized users, are no longer sufficient to meet modern expectations of global accessibility, uninterrupted availability, and continuous feature evolution. Cloud computing introduces elastic resource provisioning and on-demand scalability, while distributed architectural paradigms enable applications to be decomposed into independently deployable services that evolve without disrupting the overall system. Together, these paradigms enable organizations to deliver responsive and resilient services across geographically dispersed user bases. Despite these advantages, the migration to distributed cloud platforms introduces significant engineering complexity. Inter-service communication over unreliable networks requires robust coordination mechanisms, and maintaining data integrity across distributed databases demands carefully designed consistency strategies. Security boundaries expand due to exposed APIs and multi-tenant environments, necessitating identity-centric security models. Furthermore, observability becomes challenging because system behavior must be analyzed across numerous interacting services rather than single hosts, and operational overhead increases as infrastructure becomes highly dynamic and ephemeral. This review analyzes the foundational principles, architectural patterns, enabling technologies, and operational methodologies involved in engineering distributed enterprise platforms. It discusses microservices architecture, containerization and orchestration frameworks, distributed data management approaches, automated DevOps pipelines, observability practices, and zero-trust security models. Engineering trade-offs related to latency, reliability, fault tolerance, and cost efficiency are examined to provide a balanced perspective on system design decisions. The paper also explores emerging directions shaping next-generation enterprise computing, including serverless platforms that abstract infrastructure management, AI-driven operational analytics for predictive reliability, and edge–cloud integration for latency-sensitive workloads. By synthesizing current practices and research challenges, this review aims to provide a comprehensive conceptual framework that assists engineers, architects, and researchers in designing scalable, reliable, and maintainable enterprise systems in modern cloud ecosystems.
DOI: https://doi.org/10.5281/zenodo.18711797
System Architecture And Operations In Modern Distributed Enterprises
Authors: Farzana Akter
Abstract: Modern enterprises operate in an environment characterized by continuously growing user demand, global accessibility requirements, and expectations of uninterrupted digital services. To meet these conditions, organizations have progressively shifted from traditional monolithic software systems toward distributed computing environments capable of delivering scalability, resilience, and rapid deployment. In monolithic architectures, application components are tightly coupled and deployed as a single unit, making scaling inefficient and maintenance disruptive. The emergence of distributed architectures has allowed applications to be decomposed into independent services, enabling selective scaling, improved fault tolerance, and faster release cycles. This architectural transformation has been driven by the adoption of microservices, containerization technologies, and cloud-native platforms. Microservices allow applications to be structured around business capabilities, promoting modularity and development team autonomy. Containerization ensures consistent execution across heterogeneous environments by packaging applications together with their dependencies, while orchestration frameworks enable automated scaling, service discovery, and self-healing capabilities. Cloud-native infrastructure further enhances flexibility by providing elastic resources and managed services that reduce operational overhead and infrastructure maintenance complexity. Alongside architectural evolution, enterprise operational practices have undergone a significant transformation. The integration of development and operations through DevOps practices has enabled continuous integration and continuous deployment pipelines that accelerate software delivery while maintaining stability. Site Reliability Engineering introduces measurable reliability objectives, transforming system availability into a quantifiable engineering goal. Infrastructure as Code automates provisioning and configuration management, ensuring reproducibility and reducing configuration drift across environments. Continuous monitoring and observability frameworks provide real-time insight into system behavior, allowing proactive detection of anomalies and performance bottlenecks. Security and reliability considerations have also expanded in distributed environments. The increased number of services and communication channels requires embedded security practices such as identity-based access control, encryption, and automated vulnerability assessment integrated directly into deployment pipelines. Observability mechanisms combining metrics, logs, and distributed tracing enable organizations to understand complex inter-service dependencies and maintain operational stability at scale. Finally, the enterprise computing landscape continues to evolve with the emergence of serverless computing, edge computing, and artificial-intelligence-assisted operations. These paradigms aim to minimize infrastructure management effort, reduce latency, and enable predictive operational decision-making. Together, these developments indicate a shift toward autonomous, self-managing systems capable of adapting dynamically to workload fluctuations and operational risks. Understanding the interdependence between system architecture and operational strategy is therefore essential for designing robust, cost-efficient, and adaptive enterprise platforms capable of supporting future digital transformation initiatives.
DOI: https://doi.org/10.5281/zenodo.18711826
Digital Nervous Systems For Enterprises: Integrating IoT, Big Data, And Artificial Intelligence Across SAP SuccessFactors And Cloud HCM Landscapes
Authors: Sebastian Moreau, Yuki Matsumoto, Adrian Kovalenko, Matteo Ricci, Ananya Kulkarni
Abstract: Digital transformation in human capital management has created complex, distributed ecosystems in which employee data originates from connected devices, cloud platforms, transactional systems, and external intelligence services. Fragmented architectures limit the ability to sense patterns, contextualize signals, and coordinate timely action across SAP SuccessFactors and heterogeneous cloud HCM landscapes. This study introduces a digital nervous system architecture that integrates Internet of Things telemetry, scalable big data infrastructures, and artificial intelligence driven cognition into a unified sensing and response framework. The proposed model organizes system design into sensing layers for real time signal acquisition, transmission layers for streaming and synchronization, cognitive layers for predictive and prescriptive analytics, and response layers for coordinated orchestration across talent, payroll, performance, and compliance domains. A formal Enterprise Signal Latency Index is developed to quantify responsiveness across distributed platforms, alongside a Neural Stability Metric that measures adaptive coherence within the integrated HCM ecosystem. Through architectural modeling and scenario based evaluation, the research demonstrates reductions in signal propagation delay, improved anomaly detection accuracy, enhanced decision synchronization across platforms, and strengthened systemic resilience. The findings establish a scalable blueprint for constructing intelligent, continuously learning digital infrastructures that unify IoT, big data, and artificial intelligence within multi cloud human capital environments.
DOI: https://doi.org/10.5281/zenodo.19104930
AI-Powered Compliance Monitoring Systems
Authors: Kiran Das
Abstract: The global regulatory landscape is currently undergoing a period of unprecedented volatility, characterized by the introduction of complex frameworks such as GDPR, CCPA, HIPAA, and the evolving EU AI Act. For modern enterprises, manual compliance monitoring—once the standard for risk management—is no longer a viable strategy due to the sheer volume, variety, and velocity of data generated across distributed digital ecosystems. This review examines the paradigm shift toward AI-powered compliance monitoring systems, which leverage Natural Language Processing (NLP), Machine Learning (ML), and Computer Vision to provide real-time, continuous oversight. By automating the ingestion and interpretation of legal texts and cross-referencing them with internal operational telemetry, these systems identify "compliance gaps" before they manifest as legal liabilities. This article categorizes current methodologies, including the use of Large Language Models (LLMs) for semantic policy mapping and Deep Learning for detecting anomalous financial patterns indicative of money laundering or fraud. We explore how AI mitigates "regulatory fatigue" by filtering noise and highlighting high-priority risks, thereby allowing compliance officers to transition from administrative data processors to strategic advisors. Furthermore, the review addresses the critical challenges of algorithmic bias, the "black-box" nature of deep neural networks, and the necessity for Explainable AI (XAI) in regulatory reporting. By synthesizing recent academic research and industrial case studies, this paper provides a strategic roadmap for building "compliance-by-design" architectures. The findings suggest that AI-powered systems not only reduce the cost of adherence but also foster a culture of transparency and proactive ethical governance.
DOI: https://doi.org/10.5281/zenodo.19427276
Autonomous Cyber Defence Systems (ACDS) Using AI
Authors: Priya Sharma
Abstract: The modern cyber threat landscape has evolved into a high-velocity adversarial environment where automated botnets, polymorphic malware, and AI-driven exploits outpace human cognitive limits. Traditional reactive security models, which rely on manual intervention and static rule-based thresholds, are increasingly inadequate against multi-stage, stealthy campaigns. This review examines the paradigm shift toward Autonomous Cyber Defense Systems (ACDS) powered by Artificial Intelligence (AI) and Machine Learning (ML). Unlike conventional tools, ACDS are designed to operate within the "OODA loop" (Observe, Orient, Decide, Act) at machine speed, performing real-time threat discovery, risk-weighted decision-making, and automated remediation without human oversight. This article categorizes current ACDS methodologies, including Reinforcement Learning (RL) for dynamic policy optimization, Deep Learning (DL) for behavioral anomaly detection, and Graph Neural Networks (GNNs) for mapping lateral movement. We explore the transition from "Security Orchestration" to "Autonomous Orchestration," where the system self-configures its defensive posture based on shifting environmental variables. Furthermore, the review addresses critical challenges, such as the "Black Box" transparency problem, the risk of "automated cascading failures," and the emerging threat of adversarial machine learning. By synthesizing recent academic breakthroughs and industrial case studies, this paper provides a strategic roadmap for achieving "Self-Healing" infrastructures. The findings suggest that while human-in-the-loop models remain necessary for high-level strategic oversight, the tactical frontline of cyber defense must become fully autonomous to ensure resilience against the next generation of automated adversarial competition.
DOI: https://doi.org/10.5281/zenodo.19427289