IJSRET Volume 8 Issue 6, Nov-Dec-2022

Uncategorized

Brain Tumor Detection Based on Watershed Segmentation and Classification Using Deep Learning
Authors:- Shivam Tamrakar, Prof. Mahesh Prasad Parsai

Abstract- The computer-aided diagnostic-based that supports deep learning (DL) algorithms consists of several processing layers, which symbolize data with several stage of construct. In current years, the use of deep learning has increased speedily in almost all areas, especially in the field of medical imaging, medical image investigation or bioinformatics. Therefore, deep learning has effectively untouched or enhanced the methods of recognition, calculation or diagnosis in many medical and health areas such as pathology, brain tumors, lung cancer, stomach, heart or retina. Given wide application of deep learning, the purpose of this paper is to appraise the most important deep learning perception related to tumour analysis detection and classification In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical image. To overcome this difficulty, in the proposed method GLCM feature and Resnet-50 techniques used for feature extraction and watershed based segmentation is used for brain tumour detection and its classification. A significant, practical deep learning model is proposed which uses back propagation neural network feature to predict brain stroke through CT/MRI scan images. The performance and accuracy of the proposed model is evaluated and compared with existing models and it produces high sensitivity, specificity, precision and accuracy.

Study of Factors Affecting to Behavioural Intention on Adopt Mobile Payment
Authors:- P.K.C. Adeesha Rathnasinghe

Abstract- This paper provides an analysis and evaluation of the factors that influence mobile payment adoption in Sri Lanka, as well as an examination of the customer-driven characteristics of mobile payment solutions and their associated value proposition. The convenience feature of mobile payment has replaced interactions with actual currency and shortened transaction times, which better satisfies the convenience needs of modern people. As mobile payments play a major part in mobile business, gaining an understanding of the characteristics that attract consumers to mobile payment will provide mobile businesses with additional chances for growth and substantially increase their output value. Based on the core theoretical framework of the Theory of Acceptance and Use of Technology, this study investigates how to further affect customer behavioural intention in Sri Lanka (UTAUT2). In this investigation, data analysis is conducted to validate the research model and hypotheses. Social influence, facilitating conditions, hedonic motivation, compatibility, innovation, relative benefit, complexity, performance expectations, and observability have been identified as dependent variables that influence customer desire to use mobile payment. One hundred eighty samples will be chosen using a random sampling technique for the investigation. Utilizing statistical analysis and regression analysis, the impact of these nine parameters on mobile payment adoption was confirmed. Perceived danger, perceived cost, perceived advantage, perceived ease of use, perceived usefulness, perceived behaviour, social influence, credibility, and compatibility have a major impact on mobile payment uptake, according to the results of a study.

Detection of Glaucoma by the Use of Convolutional Neural Network
Authors:- M.Tech. Scholar Pankaj Goud, Asst. Prof. Miss Priyanshu Dhameniya

Abstract- Glaucoma is a disease that affects human eyes and makes it difficult for people to see clearly. In recent years, the prevalence of this condition has increased significantly. The result of this illness is a permanent impairment of vision that cannot be reversed once it has taken place. In the past, the diagnosis of glaucoma was carried out with the assistance of a number of different deep learning (DL) algorithms. The results of our research on recognising glaucoma illness are presented in this journal. For the purpose of recognising the ailment, we used a deep learning model known as a Convolutional neural network (CNN). The convolutional neural network provides us with a distinct pattern for both eyes afflicted by glaucoma and eyes that are not impacted by glaucoma. This pattern may be used by us to diagnose glaucoma. When CNN is used, a hierarchical framework is provided for distinguishing between images of glaucoma-affected eyes and photographs of eyes that are not affected by glaucoma. This facilitates more accurate categorization. Using the method that we offer, it is possible to do a review in a total of six phases. The dropout mechanism is used in the study that is advised in order to improve the overall efficiency of the performance. This is done in the context of glaucoma disease detection. In order to carry out an analysis of the work that was intended, this study made use of the datasets provided by SCES and ORIGA. The values acquired for the ORIGA dataset come in at 92.3, while the SCES dataset has values that come in at 94.2.

Load Balancing in Cloud Computing Through Multiple Gateways
Authors:- Research Scholar Rani Danavath, Asst. Prof. Dr. V. B. Narsimha

Abstract- Cloud computing is a structured model that defines computing services, in which data as well as resources are retrieved from cloud service provider via internet through some well formed web-based tool and application. As the numbers of users are increasing on the cloud, the load balancing has become the challenge for the cloud provider. As most of the traffic is oriented towards the Internet and may not be distributed evenly among different IGWs, some IGWs may suffer from bottleneck problem. To solve the IGW bottleneck problem, we propose an efficient scheme to balance the load among different IGWs within a WMN Our proposed load-balancing scheme consists of two parts: a traffic load calculation module and a traffic load migration algorithm. The IGW can judge whether the congestion has occurred or will occur by using a linear smoothing forecasting method. When the IGW detects that the congestion has occurred or will occur, it will firstly select another available IGW that has the lightest traffic load as the secondary IGW and then inform some mesh routers (MPs) which have been selected by using the Knapsack Algorithm to change to the secondary IGW. The MPs can return to their primary IGW by using a regression algorithm.

Blockchain and Its Use in Financial World
Authors:- Lokesh Yadav

Abstract- A Blockchain Is Essentially A Digital Ledger That Is Replicated And Distributed Across A Networkof Computer Systems On The Blockchain. Each Block On The Chain Contains A Set Oftransactions, And Each Time A New Transaction Occurs On The Blockchain, A Record Of Thattransaction Is Added To Each Participant’s Ledger. A Distributed Database Managed By Multipleparticipants Is Called Distributed Ledger Technology (Dlt).

Control Strategy for Bidirectional AC-DC Interlinking Converter in AC-DC Hybrid Microgrid Using PV System
Authors:- Vikram Sirohi, Asst. Prof. Somya Agarwal, Dr. Raghavendra Patidar

Abstract- In this article, a single-stage bidirectional converter that is connected to the grid is suggested. This converter would have a power conversion stage and an unfolding circuit. The power conversion stage would be a two-way DC-DC converter. The goal of this research is to get the most energy out of photovoltaic (PV) energy systems as possible. When the temperature, the amount of sunlight, or the load changes, so does the maximum amount of power that the photovoltaic module can produce. The photovoltaic system uses a maximum power point tracker (MPPT) to keep getting the most power out of the solar panel and send it to the load. This is done so that the system is as efficient as possible. The Maximum Power Point Tracking (MPPT) system is made up of a controller and a DC-DC converter, which are its two main parts. The DC-DC converter is a piece of electronic equipment that changes the voltage of DC energy from one level to another. MPPT uses a tracking algorithm so that it can find the place with the most power and keep working there even when the weather changes. Many different algorithms for MPPT have been made and talked about in published research, but most of these methods have problems with how well they work, how precise they are, and how well they can be changed. Conventional controllers can’t give the best response because the PV module’s current-voltage characteristics don’t behave in a linear way and switching makes the DC-DC converter behave in a non-linear way. This is especially true when the line parameters and transients change in a lot of different ways. The goal of this work is to make a maximum power point tracker and then use it. This will be done by using fuzzy logic control algorithms. When fuzzy logic is used, it is natural that a good controller will be made for nonlinear applications. This method also uses techniques from artificial intelligence, which can make modeling nonlinear systems easier and offer other benefits. Simulink was used to build an MPPT system with solar modules, DC-DC converters, batteries, and fuzzy logic controllers, and to simulate it. This had to be done so that the job could be done well. Characterize the buck, boost, and buck-boost converters to find out which topology is best for the PV system being used. In MATLAB, a model of the PV module, the indicated converter, and the battery were all put together to get the experience needed to build and tune the fuzzy logic controller. The results of the simulation show what happens when the parameters are changed.

Energy Optimization of Underwater WSN by Wolf Based Clustering
Authors:- M.Tech. Scholar Kush Paliwal, Asst. Prof. Sumit Sharma

Abstract- Communication is basic need of any age, although medium and technique is different. In this era wireless communication is common and acceptance of this in various applications is also wide. Out of different field of WSN (Wireless Sensor Network), underwater is highly desirable as study of such area may give new material or learning. This paper has developed a model that works for underwater WSN optimization by clustering and routing. Clustering of nodes were done by Wolf optimization technique, algorithm is able to provide solution dynamic situation. Cluster nodes selection done on the basis of device energy, distance from the base station. Routing of packet is also done from the nodes by means of cluster centers. In order to reduce the load of cluster nodes, shuffling of nodes were done time to time. Experiment was done on different environment of underwater and varying number of nodes. Model was compared with existing technique of underwater WSN network optimization.

An Analytical Study Using Dynamic Analysis on Buildings With and Without Expansion Joints
Authors:- Ashutosh Dabral , Rashmi Sakalle

Abstract- Vibration is effectively dampened by expansion joints, which also serve to keep individual building components together while allowing for their natural movement in response to things like ground settlement and earthquakes. In addition to protecting against moisture and water damage, this facilitates the transportation of live cargo. Expansion joints may be used to completely separate many different construction components, including ceilings, floors, roofs, walls, and facades. Additionally, they may be set up wall to wall, ceiling to ceiling, roof to roof, or roof to wall. They’re versatile enough to do more than one thing at once. These connections separate a frame into individual segments with sufficient breadth to accommodate the building’s thermal expansion and contraction. This thesis presents an experimental software analysis on the expansion joint of a hospital building to find: Displacement, Bending moment, Shear force and Axial force. Two samples were designed on STAAD PRO and a comparative study was made to find the expansion joint design with better performance.

A Comprehensive and Novel Approach to Design of Carbon Reinforced Alloy Wheel with Material Selection
Authors:- Anurag Tiwari, Prof. G.R. Kesheorey

Abstract-Main objective is to selection of material, analyze the reason of failures of the rim. Mainly the cracks on the surface, bending due to impact loading. Vibration and the hold pressure of the tire can damage the rim. The damage such as rust, dents, etc. which results in increased vibration while running, loss of air pressure and even sometimes the complete structural failure. This can damage the rims which could result in failure of the Rim during running conditions. Changes can be made to a rim and visible damage could lead to greater damage which can’t be seen by naked eye, so a repaired rim will never be structurally sound as original rim. There are some more causes of failure, this project will discuss about these failures which can arise in rim. This project is all about the design, analysis and calculation of von-mises stresses and deflections with the help of CATIA and ANOVA method. The part which is under maximum stress as well as respective deformation value can be easily detected.

Mitigating Shear Failure of Flexurally Strengthened Reinforced Concrete Beams Using Carbon Fibre Reinforced Polymer
Authors:- Dr. Muhammad Ashiqur Rahman, Dr. A. B. M. Saiful Islam, Prof. Ir. Dr. Mohd Zamin Bin Jumaat

Abstract- Shear failure is sudden, brittle and catastrophic in nature, which starts without advance warning of any distress. Hence, ensuring shear failure will not happen in reinforced concrete (r.c.) beams must be given due consideration in design. Practically beams can be allowed to take more loads if they are flexurally strengthened. Premature shear failure will occur when the shear reinforcements present can no longer take the increased shear loads due to flexural strengthening. Hence, when a r.c. beam is flexurally strengthened, care must be taken to ensure it does not fail under premature shear. Eight beams were prepared and tested in this research. Technical Report -55 (TR-55) was used to design the carbon fibre reinforced polymer (CFRP) plate for flexural strengthening. According to TR-55, the design strain for flexural plate is 0.006 for preventing intermediate crack (IC) debonding. Experimental data showed that the flexural CFRP plate strain reached 0.0072 without IC debonding. The CFRP strips for shear strengthening were designed using ACI 440-2R, 2008 and fib TG 9.3 2001. The key parameter for designing shear was the effective strain of the CFRP shear strips. Experimentally, CFRP shear strips experienced strain about half of the designed value according to ACI 440-2R, 2008 and fib TG 9.3 2001. The internal stirrups and external CFRP shear strips had almost the same strain values before failure. Overall, the strengthened beam capacity was increased by 160% compared with the control unstrengthened beam by mitigating the shear failure using CFRP.

Energy Optimization of Underwater WSN by Wolf Based Clustering
Authors:- Kushagra Paliwal, Asst. Prof. Sumit Sharma

Abstract- Communication is basic need of any age, although medium and technique is different. In this era wireless communication is common and acceptance of this in various applications is also wide. Out of different field of WSN (Wireless Sensor Network), underwater is highly desirable as study of such area may give new material or learning. This paper has developed a model that works for underwater WSN optimization by clustering and routing. Clustering of nodes were done by Wolf optimization technique, algorithm is able to provide solution dynamic situation. Cluster nodes selection done on the basis of device energy, distance from the base station. Routing of packet is also done from the nodes by means of cluster centers. In order to reduce the load of cluster nodes, shuffling of nodes were done time to time. Experiment was done on different environment of underwater and varying number of nodes. Model was compared with existing technique of underwater WSN network optimization.

Grade Recommendation Using Privacy Preserving Mining and Genetic Algorithm
Authors:- M.Tech. Scholar Priyanka Vishwakarma, Asst. Prof. Sumit Sharma

Abstract- Data analysis depends on quality of input data but this increase chance of privacy break of organization or individual or community. So reverse mining process is applied that performs both the data privacy preserving and knowledge extraction. In order to improve education quality student data analysis is more sensitive and needs good set of features for prediction. This paper has proposed a model that extracts features from the different city schools and trains a model for grade prediction. Proposed model has not shared student data to any third party, instead of this random features selected by the genetic algorithm were used for the training of model. These features were taken in form of presence and absence of student activities. Experiment was done on real dataset of Maharashtra Districts School Students. Comparisons result shows that proposed model has improved the prediction accuracy by % as compared to similar models of privacy preserving.

Multi-modal medical image analysis using Wavelet Fusion
Authors:-M.Tech. Scholar Khurshed Akhtar, Prof .Deepak Mishra

Abstract-Techniques for pixel-level image fusion have been the most important for remote sensing data processing and analysis up until this point. Typically based on empirical or heuristic rules, feature based fusion techniques are utilized for this purpose. Multimodal transport image registration and fusion technologies play an important role in routine screening, screening, screening and evaluation of chronic disease radiotherapy, surgical and radiotherapy programmes. Multimedia media algorithms and tools have made great strides in supporting the reliability of clinical decisions on medical imaging and will continue to make great strides. Combining the two types of information and mixing the two images. Image aggregation methods include simple methods (e.g. pixels) and complex methods (such as wavelet transforms). The advantage of using wavelet manipulation is it has a large part of each image. Its main objective is to improve the understanding of medical images through the use of discrete wavelet transformation technology. DWT uses mainly consolidation rules involving average pixels. The discrete wavelet transformation was carried out using fusion techniques designed specifically for integrated medical images. The fusion performance is calculated based on PSNR, MSE and whole progression moment.

Review on Renewable Energy Based Electric Vehicles Charging Technology
Authors:- Kuldeep Gautam, HOD Ravi Hada

Abstract-Many different types of electric vehicle (EV) charging technologies are described in literature and implemented in practical applications. This paper presents an overview of the existing and proposed EV charging technologies in terms of converter topologies, power levels, power flow directions and charging control strategies. An overview of the main charging methods is presented as well, particularly the goal is to highlight an effective and fast charging technique for lithium ions batteries concerning prolonging cell cycle life and retaining high charging efficiency. Once presented the main important aspects of charging technologies and strategies, in the last part of this paper, through the use of genetic algorithm, the optimal size of the charging systems is estimated and, on the base of a sensitive analysis, the possible future trends in this field are finally valued.

Effect of Environmental Factors on the Performance of Savonious Wind Rotor
Authors:- Associate Prof. P. Venkateswara Rao

Abstract- Savonious rotors continue to interest research investigators in view of its many advantageous features. The simple design of the rotor enables the achievement of a low cost and compact wind power device, although its efficiency may not be comparable with other vertical axis machines such as Darraeus rotor. In low wind velocity zones, one can adapt these rotors with success. Different configurations of the Savonious rotor have been proposed to overcome some of the limitations of the earlier Savonious rotors, which have very low tip speed ratios. Design guidelines have been enunciated for the design of the rotors, based on experience with field-installed rotors. Although a few CFD investigations have been reported earlier on the flow analysis of Savonious rotors, there appears to be no serious attempt made for analysis of flow distribution in these rotors at rarified atmospheric conditions to enable a more realistic understanding of the rotor performance. The rarified atmospheric conditions result from the ambient temperature occurring as per seasonal variations. In the present paper, an attempt is made to carry out a detailed two-dimensional CFD analysis of the basic configuration of the Savonious wind rotor with eccentricity to assess the performance at different atmospheric conditions. A parametric analysis is carried out to understand the pressure and velocity distribution of the rotor. The commercially available Fluent has been used extensively in the present analysis.

Analysis of RQD-RMR-GSI Geo-Mechanical Parameters of the Lithology Exposed In the Portion NE-SE of the City of La Paz, B.C.S., Mexico
Authors:- Joel Hirales Rochin

Abstract- Since ancient times, natural rocks have been used to improve the quality of life of populations, as base materials for the construction of infrastructure works in structural elements, cladding materials, as well as aesthetic finishes.Rock mass classification systems are a global communication system for explorers, designers and builders that facilitate the characterization, classification and knowledge of the rock mass properties.The applied methodology was the geotechnical tool of the Geomechanical classification of Bieniawski RMR, RQD Classification, GSI, as well as with the support of GIS (ArcGIS) where data and field information were worked.The objective of this study is to carry out a geo-mechanical characterization of different lithological zones of the city of La Paz, Baja California Sur., Mexico in its NE-SE portion.Geologically, the study area is based on Holocene deposits that correspond to alluvial material and outcrops of volcanic and volcanoclastic rocks (sandstones, volcanoclastic conglomerates, rhyolitic tuffs, andesitic lahars and lava flows) that are part of the Comondu Formation with an age between 30 and 12 Ma. The information will be the basis of a future comprehensive study to determine the quality indices with geotechnical parameters of the outcropping rocky massif and will allow a sustainable urban development of the improvement of the current construction regulations in the excavation and support criteria.

A Review of Load Balancing Technique in Cloud Computing
Authors:- M.Tech. Scholar Ms. Aarti Jaiswal, Assistant Professor Ms.Trapti Sharma

Abstract- Cloud registering shares information and give numerous assets to clients. Clients pay just for those assets as much they utilized. Cloud computing stores the information and disseminated assets in the open condition. The measure of information stockpiling increments rapidly in open condition. Along these lines, stack adjusting is a primary test in cloud condition. Load adjusting is dispersed the dynamic workload over various hubs to guarantee that no single hub is over-burden. It helps in legitimate usage of assets .It additionally enhance the execution of the framework. Many existing calculations give stack adjusting and better asset use. There are different composes stack are conceivable in Cloud computing like memory, CPU and system stack. Load adjusting is the way toward finding over-burden hubs and after that exchanging the additional heap to different hubs.

Robotic Patient Monitoring and Medicine Delivery
Authors:- Syed Mohammed Ali, Mohd Abdul Sattar, Shanila Mahreen

Abstract- In this project, I propose a robot with some functionality of providing medicine as well as to measure the vital parameters (Heart rate,Blood Pressure, Temperature) of the patient. We can attain the locomotion procedure of the robot using the principle of Radio-frequency identification (RFID) that automatically identifies and tracks tags attached to the objects. The movement and finding the path to patient location is done through a line follower and with RFID tag. Line following method is used to identify the path with help of two infrared sensors. The robot will move towards the patient’s room by following a non-reflective line and use RFID cards to identify the patient’s room number. Using the Medicine box, the medicine delivery is made possible to the patients. Relevant box will be open based on the RFID reader. All the measured parameters will be stored to the cloud using the application of the Internet of Thinking (IOT).If the read values varied from threshold, then an alert message will be sent to doctors through GSM Module.

Development of a Microcontroller-Based Water Fountain Control System
Authors:- Engr. Lyndon R. Bermoy, Vendy Von P. Salvan

Abstract- Entertainments are designed to attract or entice individuals. In some cities in the Philippines, there are only a few entertainment venues, making it difficult to attract people’s attention. The introduction of a new form of entertainment, such as a water fountain, can be a positive factor in the tourism industry’s expansion. The opportunity to observe water spurts of varying quantity and velocity at rhythmic intervals may reduce fatigue and aid in relaxation. People, especially children, would prefer the Water Fountain Show as a form of recreation and enjoyment, given that the Water Fountain is unlike any other form of entertainment available in the Philippines. This study’s sole objective is to design and develop an MCU-Based Water Fountain Control System. The system includes a control circuit that regulates the quantity of water released in a tube based on the pressure applied, thereby producing a sequence of water combinations. The project will feature a variety of lighting effects with corresponding colors and music that will make the overall display more colorful and enjoyable.

Performance Analysis of PID Controller for an Automatic Voltage Regulator System Using Simplified Particle Swarm Optimization
Authors:- Saleha, Vinay Pathak

Abstract- This paper presents the design and performance analysis of Proportional Integral Derivate (PID) controller for an Automatic Voltage Regulator (AVR) system using recently proposed simplified Particle Swarm Optimization (PSO) also called Many Optimizing Liaisons (MOL) algorithm. MOL simplifies the original PSO by randomly choosing the particle to update, instead of iterating over the entire swarm thus eliminates the particles best known position and making it easier to tune the behavioral parameters. The design problem of the proposed PID controller is formulated as an optimization problem and MOL algorithm is employed to search for the optimal controller parameters. For the performance analysis, different analysis methods such as transient response analysis, root locus analysis and bode analysis are performed. The superiority of the proposed approach is shown by comparing the results with some recently published modern heuristic optimization algorithms such as Artificial Bee Colony (ABC) algorithm, Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) algorithm. Further, robustness analysis of the AVR system tuned by MOL algorithm is performed by varying the time constants of amplifier, exciter, generator and sensor in the range of50% to50% in steps of 25%. The analysis results reveal that the proposed MOL based PID controller for the AVR system performs better than the other similar recently reported population-based optimization algorithms.The tuning performance of this algorithm and its contribution to the robustness of the control system are also extensively and comparatively investigated. In the performance analysis, Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) algorithm are used for the purpose of comparison. These analyses are realized by benefiting from different analysis methods such as transient response analysis, root locus analysis, bode analysis and statistically Receiver Operating Characteristic (ROC) analysis. Afterwards, the robustness analysis is applied to the AVR system, which is tuned by ABC algorithm in order to determine its response to changes in the system parameters. At the end of the study, it is shown that the ABC algorithm is successfully applied to the AVR system for improving the performance of the controller and shows a better tuning capability than the other similar population-based optimization algorithms for this control application.To solve these control problems, which are explained above, an Automatic Voltage Regulator (AVR) system is applied to power generation units. The AVR system is a closed loop control system that provides terminal voltage at the desired value. The configuration of this control system will be investigated.

A Review of 5G Architecture with Emphases on Security, Energy and wide Applications
Authors:- Riya Sharma, Professor Dr. Pramod Sharma

Abstract- The eventual goal of the forthcoming 5G wireless networking is to have relatively fast data speeds, incredibly low latency, substantial rises in base station’s efficiency and major changes in expected Quality of Service (QoS) for customers relative to the existing 4G LTE networks. In order to deal with state-of-the art technologies and connectivity in the form of smart cell phones, internet of things (IoT) devices, autonomous vehicles, virtual reality devices and smart homes connectivity, the broadband data use has risen at a fast rate. Further, to meet the latest applications, the bandwidth of the system needs to be increased widely. This development will be accomplished by using a modern spectrum with higher data levels. In particular, the fifth generation (5G) mobile network seeks to resolve the shortcomings of previous telecommunication technologies and to be a possible primary enabler for future IoT applications. This paper briefly discusses the architecture of 5G, following by the security associated with the 5G network, 5G as an energy efficient network, various types of efficient antennas developed for 5G and state of-the-art specifications for IoT applications along with their related communication technologies. We have also outlined the broader usage of 5G and its future impacts on our lives. Furthermore, at the end of each subtopic, the necessary recommendations are given for the future work.

A Review on Collapse Behaviour of Cable Stayed Bridge
Authors:- M. Tech. Scholar Masoud Ahmed Khan, Asst. Prof. Dhanesh Khalotia

Abstract- Cable stayed bridges have good stability, ultimate use of structural materials, aesthetic, tremendously low design and protection costs, and efficient structural traits. Therefore, this kind of bridges are becoming more and more famous and are generally preferred for lengthy span crossings as compared to suspension bridges. A cable-stayed bridge includes more than one tower with cables helping the bridge deck. In phrases of cable arrangements, the most not unusual forms of cable stayed bridges are fan, harp, and semi fan bridges. Because of their big length and nonlinear structural behaviour, the analysis of those kinds of bridges is greater complex than conventional bridges. However in these bridges, the cables are the principle supply of nonlinearity. An optimal design of a cable-stayed bridge with minimum cost with reaching power and serviceability necessities is a challenging project. Therefore a review on collapse behaviour of cable stayed bridge has been done.

Implementation and Utilization of Deep Learning Approach in the Medical Field
Authors:- Research Scholar Vishal Acharya, Associate Prof. & HOD. Dr. Bharti Chourasia

Abstract- The COVID-19 epidemic has brought about an unusually terrible circumstance for the entire planet, terrifyingly stopping life as we know it and taking thousands of lives. Due to the expansion of COVID-19 to 212 countries and territories, as well as the rise in infection cases and fatalities. The public health system continues to be seriously threatened. The deep learning strategy for predicting the severity of the decline in COVID-19-infected patients was proposed in this research and is based on CNN. The suggested model may learn complicated connections between a variety of heterogeneous parameters using this new methodology, including census data, intra-county movement, inter-county mobility, data on social distance, previous infection growth, and more. According to the simulated results, total accuracy is 23.85% higher than prior work, and classification error is 32.86% lower than prior methodology. The prior method yielded precision values of 6.29%, recall values of 78%, and f-measure values of 36.01%. The simulation results demonstrate that the overall enhancement of performance parameters is superior to the current method.

Digital Image Watermarking by Select ed Feature of Group Search Genetic Algorithm
Authors:- Dilesh Khairwar, Asst. Prof. Sumit Sharma

Abstract- Image is a proof of any instant happened in the universe. Transformation of image from hard to digital brings different flexibility and uses for the analysis and storage. Digital images need security from the intruder for that many communication protocols were developed. For the validity of authentic source watermarking plays an important role. This paper has proposed a model that embedded watermark into the original image by extracting DWT feature from the image. For embedding at Least significant coefficient proposed model has uses Group Search genetic algorithm. Food sources cloning and mutation steps has reduces the iteration count that decreases the embedding process time as well. Experiment was done on real and standard digital images. Result shows that proposed model has maintained the PSNR value of image even after embedding.

A Study on Various Continuous Functions
Authors:- Mrs. K.Kiruthika, Dr. N.Nagaveni

Abstract- In this paper, we present and study a new concepts namely strongly rb-continuous and Perfectly rb-continuous, Contra rb-continuous and Totally rb-continuous. Also examine some of their properties of such functions.

Review on Milli Meter-Wave (mmW) Imaging for Humans Bio-field
Authors:- Mangukiya Hitesh Kumar Bhupatbhai

Abstract- Increasing demands for screening personnel for concealed objects lead to additional research efforts related to suitableimaging systems and their industrial realization. In this context millimeter-wave systems are a promising approach, because the radiation does not present a health hazard to people under surveillance and readily passes through manyoptically opaque materials such as clothing fabrics allowing for the identification of concealed objects. Due to theextent of the human’s body and the resultant required amount of 3D resolution cells with a magnitude of 15mm orless, in principle all existing and proposed systems have to deal with a huge amount of scattering data which have tobe acquired and processed. For a highly resolved image principally as much information as available should be used. Interestingly electromagnetic field is associated with such activities. Psychological perception of one’s environment or a person’sthought process induces characteristic electrical impulses in the brain. These signals travel throughout the central, sympathetic and parasympathetic nervous system, creating the unique electromagnetic field of the organism that can radiate out of the body and is termed ‘Aura’ or ‘Bio-energyfield’. Thus, ‘aura’ gives the signature of the statusof health prior to its manifestation in the physical body.Therefore, human health can be effectively monitored bymeasuring this radiation field.

A Literature Review on Brain Tumor Detection and Segmentation
Authors:- Mithilesh Nandini Malviya , Asst. Prof. Ms. Priya Sen

Abstract- A Brain Tumor is essentially a malformed cell growth that can be cancerous and non-cancerous. The tumor in the Brain is the most dangerous disease and can be diagnosed easily and reliably with the help of detection of the tumor with automated techniques on MRI Images. Several methods of efficient diagnosis and segmentation of brain tumors have been suggested by many researchers for effective tumor detection. Magnetic Resonance Imaging (MRI) images are used by specialists and neurosurgeons for the diagnosis of brain tumors. The accuracy depends on the experience and domain knowledge of these experts, and is also a time consuming and expensive process. To overcome these restrictions, several deep learning algorithms have been proposed for the detection of presence of brain tumors. In this review paper, an extensive and exhaustive guide to the sub-field of Brain Tumor Detection, focusing primarily on its segmentation and classification, has been presented by comparing and summarizing the latest research work done in this domain. For that purpose, it is proposed to review the detection of brain tumor from MRI images by using hybrid computerized approaches. Therefore, brain tumor growth performance and analysis are described to generalize symptoms and guide diagnosis towards a treatment plan. Several approaches for the segmentation process of MRI are discussed from existing papers, the detection of brain tumors can be conclude.

Review on Milli Meter-Wave (mmW) Imaging for Humans Bio-field
Authors:- Mangukiya Hitesh Kumar Bhupatbhai

Abstract- Increasing demands for screening personnel for concealed objects lead to additional research efforts related to suitableimaging systems and their industrial realization. In this context millimeter-wave systems are a promising approach, because the radiation does not present a health hazard to people under surveillance and readily passes through manyoptically opaque materials such as clothing fabrics allowing for the identification of concealed objects. Due to theextent of the human’s body and the resultant required amount of 3D resolution cells with a magnitude of 15mm orless, in principle all existing and proposed systems have to deal with a huge amount of scattering data which have tobe acquired and processed. For a highly resolved image principally as much information as available should be used. Interestingly electromagnetic field is associated with such activities. Psychological perception of one’s environment or a person’sthought process induces characteristic electrical impulses in the brain. These signals travel throughout the central, sympathetic and parasympathetic nervous system, creating the unique electromagnetic field of the organism that can radiate out of the body and is termed ‘Aura’ or ‘Bio-energyfield’. Thus, ‘aura’ gives the signature of the statusof health prior to its manifestation in the physical body.Therefore, human health can be effectively monitored bymeasuring this radiation field.

Review on Robotic Arm Component and Functions
Authors:- M.Tech. Student Siddharth Jaiswal, Asst. Prof. Kriti Srivastava , Asst. Prof. Shweta Mishra

Abstract- Robots are used in a variety of production processes, including monitoring processes, doing pick-and-place tasks, and even carrying out remote surgical procedures. The robotic arm manipulator must be able to perform a variety of duties depending on the application. The robots are designed to carry out responsibilities that need all 6 degrees of freedom (DOF). The present study conducts a literature review on previous studies that have been done on the design, materials, and operation of robots. Studies that have already been conducted have focused on the use of VLSI systems, mechanical systems, and image processing to the operation of robots. Various researchers have also presented their work on the inclusion of new approaches based on artificial intelligence with the goal of boosting the functioning and decision-making capabilities of robots.

A Review on Solar Wind Hybrid Renewable Energy System
Authors:- Twinkle Kumara ,Prof. Neeti Dugaya, Dr. Geetam Richhariya, Dr. Manju Gupta

Abstract- Renewable Energy System comprising of solar and wind energy, is eco-friendly, and cost-effective option for powering the rural areas compared to conventional sources. The drawback of these systems is they are less reliable as the generated power depends on meteorological conditions. A properly designed hybrid renewable energy system (HRES) that combines two or more renewable energy sources like wind turbine and solar system with battery back-up increases the reliability of these systems in standalone modeThis Paper provides a succinct and well-organized overview of different maximum power point tracking (MPPT) algorithms used in photovoltaic (PV) generating systems that may operate in partial shade. To far, a broad range of algorithms, PV modelling methods, PV array designs, and controller topologies have been investigated. However, every method has both benefits and drawbacks; as a consequence, while building a PV generating system (PGS) under partial shade conditions, a thorough literature study is required. The thorough review of MPPT algorithms has been done in this article. The review of MPPT methods has been divided into four major categories. The first group consists of entirely new MPPT optimization algorithms, the second group consists of hybrid MPPT algorithms, the third group consists of novel modelling approaches, and the fourth group consists of different converter topologies. This article offers an accessible reference for doing large-scale research in PV systems under partial shadowing conditions in the near future..

The Covid-19 And Its Impact on Insurance Participation in Indonesia: A Case Study of BPJS Ketenagakerjaan
Authors:- Andri Afrianto, Tony Irawan, Alla Asmara

Abstract-The COVID-19 virus has become a worldwide pandemic, and studies of its impact on insurance are needed. The research is specifically about insurance participants, especially during the pandemic, to ensure the survival of insurance in the long term. However, research linking COVID-19 and insurance is lacking. This paper aims to look at the impact of the COVID-19 pandemic on insurance by using active membership data from BPJS Ketenagakerjaan in Indonesia, which covers 34 provinces. This study uses a time series spanning 2018 to 2021 and across 11 regional offices of BPJS Ketenagakerjaan. Empirical findings suggest that COVID-19 cases are associated with reduced insurance participation. Compared to before the pandemic, COVID-19 caused a decrease in active participation by an average of 0.0577709 per cent. Active participation tends to increase yearly, but in 2020, there was a decline. Based on the results of this study, BPJS Ketenagakerjaan must reduce the risk of future pandemics by maximizing digital transformation in its business services to provide excellent service to formal and informal workers, as well as strengthening collaboration with the government in designing fiscal policies such as relaxation of contributions and direct cash transfers. While for companies, they can transfer socio -economic risks that can occur to their employees by buying insurance such as BPJS Ketenagakerjaan insurance.

Generating Transmitting Codes for MIMO Radar Using Polyphase Codes to Reduce Side-lobe Levels
Authors:- Manzoor Ahmad Wani, Shaveta Bala

Abstract-High side-lobe levels reduction is an exhausting task in Multiple- Input Multiple-Output (MIMO) radar. Transmit sequence design plays a significant role in radar to overwhelm correlation side-lobe levels. In general, side-lobe levels performance of the incoming signals is observed by their cross-correlation function with other transmitted signals. New polyphase codes are projected that shows good auto-correlation and cross-correlation function responses to reduce peak side-lobe levels (PSL) and cross-correlation levels (CCL). Performances of the various poly phase codes are compared and the P4 code is chosen for the design of new poly phase code. The proposed composite poly phase codes (CPC) are produced by adding the left and right shifted versions of P4 code asP4 code is much Doppler accepting to another polyphase codes. Using ambiguity function, the influence of CPC on the delay-Doppler plane is observed. Finally, simulation results validate superiority of the proposed CPC equated to the counterpart techniques.

Pulse Compression Radar Waveform Design Using Classical Orthogonal Polynomials to Mitigate Range Side-Lobes
Authors:- Aamir Hussain Khan, Shaveta Bala

Abstract- Transmitting waveforms plays a significant role in radar system. The benefits of both long and short duration pulses are achieved using pulse compression technique. Radar waveforms performance is observed using matched filter response. Practically, the matched filter response consists of higher range side-lobes which creates accurate detection problem. On the other side, wider bandwidth is much desirable for a better range resolution. Therefore, waveforms are to be designed in such a way that offers mitigation in matched filter side-lobes having wider bandwidth. Using classical orthogonal polynomials, new radar waveforms are designed for transmission purposes. We observed the performance of different order polynomials and finally, choose that polynomial which offers wider bandwidth and significant side-lobes reduction in pulse compression radar. The designed waveform performances are compared with the existing linear frequency modulated (LFM) waveforms.

Machine Learning Algorithm Based Health Care Monitoring System
Authors:- M.Tech. Scholar Sonal Shrivastava , Prof. Rajesh Kumar Boghey

Abstract- The regular measurement of vital signs enables early diagnosis and warning of developing problems. Furthermore, it allows closer monitoring of the effects of medication and lifestyle, making more personalized treatment plans possible. The system contains a patient loop interacting directly with the patient to support the daily treatment. It shows the health development, including treatment adherence and effectiveness. An educated and motivated patient can improve his/her treatment compliance and health. The system also contains a professional loop involving medical professionals (e.g. alerting to revisit the care plan). The patient loop is securely connected with hospital information systems, to ensure optimal personalized care. Big data analytics provides services to various organizations, especially in the healthcare field. The medical field contains a large amount of data and is well suited for data analysis. Medical big data is mainly used for clinical data, and chronic disease monitoring and health monitoring are mainly used to detect changes in patients’ health. First, you must process the data to remove unnecessary data and provide effective prediction results. The second is the data analysis process – this is the process of cleaning, transforming and modeling data for the purpose of discovering useful information. In this process, we propose privacy protection to keep patient information secure. And support vector Machine learning algorithms are mainly used to predict diseases and provide more efficient prediction results. Finally, our system will predict the disease based on the patient’s symptoms and show the treatment to the patient.

Conditions Total Factor Productivity (TFP), Competitiveness, Democracy and Oligarchy in ASEAN
Authors:- Maulin Kusuma Wardani, Didin S. Damanhuri, Widyastutik

Abstract- The purpose of this study is to analyze the condition of Total Factor Productivity (TFP), competitiveness, democracy and oligarchy in ASEAN. This study uses secondary data sources in the period 2010-2019 and five (5) selected countries, namely Indonesia, Malaysia, the Philippines, Thailand and Singapore. The TFP variable is measured by TFP Growth, competitiveness is measured by The Global Competitiveness Index, the level of democracy is measured by the Democracy Index and oligarchy is measured by calculating the Material Power Index. The results of the descriptive qualitative analysis method show the differences in the conditions of each country in terms of TFP, competitiveness, democracy and oligarchy even though they are in the same region.

Review Of Pv Generation And Power Transmission Analysis Using Power Flow Controllers
Authors:- Dipak Borse, Assistant Professor Lovkesh Patidar

Abstract- Energy security is one of the most crucial factor in the development of any nation. Inter-Connections among different power system networks are made to lower the overall price of power generation as well as enhance the reliability and the security of electric power supply. Different types of interconnection technologies are employed, such as AC interconnections, DC interconnections, synchronous interconnections, and asynchronous interconnections. It is necessary to control the power flow between the interconnected electric power networks. The power flow controllers are used to (i) enhance the operational flexibility and controllability of the electric power system networks, (ii) improve the system stability and (iii) accomplish better utilization of existing power transmission systems. These controllers can be built using power electronic devices, electromechanical devices or the hybrid of these devices. In this paper, control techniques for power system networks are discussed. It includes both centralized and decentralized control techniques for power system networks.

Power System Transient Analysis For Wind And Solar Based Hybrid System
Authors:- Garima Jain, Prof. Rajeev Chouhan

Abstract- Energy is critical to the economic growth and social development of any country. Indigenous energy resources need to be developed to the optimum level to minimize dependence on imported fuels, subject to resolving economic, environmental and social constraints. This led to an increase in research and development as well as investments in the renewable energy industry in search of ways to meet the energy demand and to reduce the dependency on fossil fuels. Wind and solar energy are becoming popular owing to the abundance, availability and ease of harnessing the energy for electrical power generation. This paper focuses on an integrated hybrid renewable energy system consisting of wind and solar energies. Many parts of Libya have the potential for the development of economic power generation, so maps locations were used to identify where both wind and solar potentials are high. The focal point of this paper is to describe and evaluate a wind-solar hybrid power generation system for a selected location. Grid-tied power generation systems make use of solar PV or wind turbines to produce electricity and supply the load by connecting to the grid.

Internal Factor of Return-To-Work (RTW) Program for Work Injured Laborer in Indonesia
Authors:- Dwi Aprianto, Dedi Budiman Hakim, Sahara

Abstract- Workplace accidents can define the level of safety in the workplace, which helps to drive national economic development. Annual GDP losses from occupational injuries are projected to be 3.94%. There were 374 million non-fatal work accidents worldwide, and 2.78 million individuals died as a result of work injuries. With 1.1 million fatalities, the Asia Pacific area has the greatest rate of occupational injury compared to other regions globally. South-East Asia generates the most work injuries to this area. Indonesia had the highest number of fatal injuries, with 15.973 fatal accidents per 100,000 employees (20.9%). It is critical to revive work-injured individuals in order for them to be productive. The purpose of this study is to identified the internal factors that determine the the RTW Program for workers who have been injured on the job. Data were acquired from BPJS Ketenagakerjaan from 2020 to 2021, with 195 people participating in this program as a result of fatal workplace injuries. This is cross-sectional research. As a consequence, 75.90% of participants were able to work after completing this program. Younger age (18-29 years), lower working years (0-5 years), male (86%), and upper limb amputation (55%) dominated the participation in RTW program. Several groups require further attention by delivering information about the workplace and road dangers. This data may be used to develop the RTW program in order to increase help to high-risk patients who are unable to work following the RTW program.

Problems Formulation and Observation Of Repairing Damaged Floor Laid Expansive Soil
Authors:-Ritu Mewade

Abstract-Engineering structures constructed on expansive soils detrimental behavior of such soils, leading to their damage and cracking. The structure which can not resist the heave pressure of soil and undergo temporary or permanent deformation is known as light structure. Less lightly loaded structures like, house, canal banks and linings, cross drainage works, have been damaged and cracked due to these soil. The damage occurs, due to the swelling and shrinking behavior of such soils. Since the structures built on such soils get lifted up during rainy season due to the heave of the foundation soil and settle down during summer season due to the shrinkage of the foundation soil, there is a need to adopt remedial measures so as to prevent lifting and sinking of the structures.

The Tendency of Unemployment with Several Elements in Labour Market Institutions
Authors:- Gleys Kasih Deborah Simanjuntak, Yeti Lis Purnamadewi, Dedi Budiman Hakim

Abstract- Labour market institutions facilitate the arrangement of employment quality and working conditions that can influence the trend in employment and unemployment, thus, the elements regulated in labour market institutions are often contentious in public policy areas. Since unemployment can be jeopardised, the arrangement of effective and efficient policies in labour market institutions should prevent its growth. Hence, it is necessary to analyse the tendency of unemployment by the existence of several elements of labour market institutions such as the unemployment benefits system, collective bargaining, employment protection, and minimum wages. This takes into account whether there is a different tendency when comparing emerging and advanced economies. Moreover, the study also includes some factors outside labour market institutions to complement the analysis known as non-institutional factors consisting of macroeconomic variables such as GDP growth, exchange rates, and inflation and other relevant factors such as corporate tax and population growth. The study is analysed descriptively using cross-tabulation from thirty-two countries. The findings indicate that countries that have more generous unemployment benefits, higher collective bargaining coverage rates, minimum wage, inflation rates, corporate tax, and population growth tend to have higher unemployment rates. Meanwhile, countries tend to hold a lower unemployment rate with stricter employment protection legislation, a weak exchange rate of domestic currency, and higher GDP growth. Meanwhile, there are no different trends based on country economy comparison except for collective bargaining, employment protection legislation, and inflation.

Design of Electronic Device To Prevent On-Road Wheeling For Two-Wheelers
Authors:- Asst. Prof. Jaya Shubha J , Spoorthi P Shetty, Subhashini D, Vadde Sneha

Abstract- Driving has become difficult in the presence of bikers, who resort to dangerous stunts on busy roads despite the ban on the practice of the same. It is evident through enough cases where reckless youngsters risk their lives and perform dangerous stunts, one is wheeling. Recent years have seen an alarming rise in this dangerous trend amongst the youth. However, the police have miserably failed to curb this fatal practice amongst which has claimed several lives in the past. The project aims at developing an electromechanical device to prevent the wheeling of two wheelers on road. The need of such device is necessary for our society. These daredevils are often seen driving their motorcycles during the day and night on the back wheel, driving inversely and doing other dangerous tricks. So here is an electronic mechanical equipment which avoids the same. The bike consists of inbuilt sensor which sends a signal to the arduino board and stops the vehicle. It also sends a message to the police control room about the vehicle number and its location. The increasing trend of one-wheeling and bike-racing continues on roads, creating troubles for traffic. Therefore here comes a small effort of us for curbing the same. The usage of this device can save many lives and prevent such injuries that could not be repaired and cured by surgery as it would be a complicated task and minimize the chances of survival.

Image and Video Datasets for Yoga Pose Estimation: A Review
Authors:- Hukam Chand Saini, Dr.Renu Bagoria, and Dr. Praveen Arora

Abstract- Research and experimentation in various technical and scientific fields are based on benchmark datasets. Specifically in the field of deep learning, finding a high-quality dataset is a must for developing the model of any AI application. Dataset is an integral part of the field of deep learning as learning of the model depends on the quantity, quality, and relevancy of the dataset. In this paper, we present the literature review and summarized comparison of the different existing Yoga Pose datasets available publically for research and experiment. The purpose of this study is to help researchers to identify and select an appropriate yoga posture dataset for yoga pose recognition under human pose estimation using deep learning and machine learning technology.

Optimizing Task Scheduling in Cloud Computing Environments using hybrid approach MM-MM
Authors:- Assistant Professor Renu Tiwari

Abstract- In today’s era of rapid development in information and computing technologies, cloud computing has emerged as a highly scalable and widely used technology worldwide. It operates on the pay-per-use, remote access, Internet-based and on-demand concepts, providing customers with a shared pool of configurable resources. However, as the number of user requests continues to increase, efficient task scheduling and resource allocation have become major requirements for effective load balancing of workloads among cloud resources, thereby enhancing the overall cloud system performance. To address this issue, various types of task scheduling algorithms have been introduced. Heuristic task scheduling algorithms such as MET, MCT, Min-Min, and Max-Min play an essential role in solving the task scheduling problem. In this paper, a novel hybrid algorithm is proposed for the cloud computing environment based on two heuristic algorithms: Min-Min and Max-Min algorithms. To evaluate the effectiveness of this algorithm, the Cloudsim simulator is used with different optimization parameters such as average waiting time and total response time between small and large tasks. The results demonstrate that the proposed algorithm optimized the resource allocation and outperforms both the Min-Min and Max-Min algorithms for these parameters.

Automated Product Recognition for Retail Shopping from Video Imaging Using Machine Learning
Authors:- Sanghita Datta, Ankita Sah, Upamita Das, Debmitra Ghosh, Aman Malhotra

Abstract- The key factor to increase the profit in grocery stores now-a-days is the availability of items on the shelf. The growing market of computer vision has made it possible for the grocery stores to grow in various aspects. To tackle is growing market of on shelf detector, our model has been designed where the products kept on the shelf would be scanned and their recognition would be done in the computer screen using machine learning for the training of data onto the model. This study examines the creation of a real-time, video-based action recognition system for removing items from shelves and putting them back. In order to prevent the two classification components from operating continually, the system also includes a detector component. The action classification component of the system is evaluated to have an accuracy of 80 percent, and the object identification component of the system to have an accuracy of 70 percent.

Facial Image Data Preparation for Early Detection of Autism
Authors:- Debmitra Ghosh

Abstract- ADHD starts to appear in childhood and continues to keep going on into adolescence and adulthood. Propelled by the rise in the use of machine learning techniques in the research dimensions of medical diagnosis, this paper there is an attempt to explore the possibility to use VGG16, Mobilenet v2, Densenet-121, Resnet-51, Inceptionv3, and Convolution Neural Network for predicting A novel data-set is created with ADHD individuals of a toddler, adolescent, and adult agegroups to evaluate the model. The first data set related to ADHD screeningin children has 292 instances and 21 attributes. Second data-set related to ADHD screening. Adult subjects contain a total of 704 instances and 21 attributes. The third data-set related toADHD screening in Adolescentsubjects comprises 104 instances and 21 attributes. ACGAN is applied to increase the data set as there is an imbalance of data between healthy individuals and healthy individuals. After applying various deep learning architectures results strongly suggest that CNN-based prediction models work better on increased data sets with higher accuracy of 99.53, 98.30, and 96.88 % in Data for Adults, Children, and Adolescents respectively.

A Review on online learning and Emergency remote teaching in Music Education courses
Authors:- Urja Joshi

Abstract- This paper considers review of changes to music industry education in the digital era and evaluates the current level of technology use within the music industry curriculum as a result of a survey on student perception. Since analysis of the collected data revealed a need to enhance the curriculum with computing and information technology competences, thesepropose and discuss novel courses that would facilitate students’ acquisition of digital knowledge and skills. Theseadditionally provide comments on the possible enrichment of existing courses with material on digital technologies applications. The information in this study is aimed not only at music industry educators but also at instructors in other disciplines willing to make their students aware of the latest technological trends.

Review on Novel Approach to observation of Brain Image Anomaly
Authors:- Ronit Dey

Abstract- – An early diagnosis of brain anomaly plays a pivotal role in better prognosis, treatment outcomes and higher patient survival rate. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a difficult process. Thus, there is a crucial need for computer-aided methods with better accuracy for early anomalydiagnosis. Computer-aided brain anomaly diagnosis from MRI images consists of tumor detection, segmentation, and classification processes. Over the past few years, many studies have focused on traditional or classical machine learning techniques for brain tumor diagnosis. Recently, interest has developed in using deep learning techniques for diagnosing brain tumors with better accuracy and robustness. This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis. This review paper identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes. In addition, this study discusses the key findings and draws attention to the lessons learned as a roadmap for future research.