Whale Optimization Algorithm for Optimal Reactive Power Dispatch and Voltage Control
Authors:-Reza Azimi
Abstract- The Whale Optimization Algorithm is used in this study to perform multi objective probabilistic optimum reactive power dispatch and voltage control in distribution networks (WOA). To obtain the ideal value of voltage deviation, losses, reactive power flow through the OLTC, and voltage variations, control factors such as on-load tap changer (OLTC) settings at substations and substation and feeder switching capacitors must be determined. As a result, a precise optimization strategy is necessary to address this complex problem. The Whale Optimization Algorithm, one of the unique optimization algorithms inspired by humpback whales, is used in this study to tackle the challenge mentioned above. Because the proposed problem is a multi objective optimization problem with several answers rather than a single answer, we employed the Pareto optimum solution approach to finding all Pareto optimal solutions. In addition, the fuzzy decision technique is used to find the best compromise solution. The suggested approach is tested using the IEEE-33 bus system. The numerical results illustrate the efficacy of the suggested approach.
Independent Remote Therapy for Muscular Lumbago using IMU Sensor Network, Computer Vision and Machine learning
Authors:- Dhruva Iyer
Abstract- This project introduces a new potential tool that will help with independent detection and real-time check on the rehabilitation process of Low Back Pain(LBP) patients. This tool uses an integration of the sensors – IMU sensor to check the range of motion, FSR flex sensors to check flexibility, and an EMG sensor to check core muscle activation. The core of this tool is a PCB that houses the foundation of the multiple sensors and their connections to the microcontroller, the Arduino Nano. These sensors are coded on the Arduino software to give a collaborated result, which is then directly taken onto a python file. This is saved as a CSV file and runs on an algorithm to produce a graph through matplotlib. Then a program is performed on it to make a comparison of the collected data values to the initial threshold values which helps create the buzz and the skeleton mapping for the client. This way, the user knows if they are doing the correct postures of exercises as prescribed by the therapist and on the right track to enhancing their recovery, which might even help with psychosomatic instincts. Furthermore, remote physiotherapy becomes possible and the frequency of visiting the physiotherapist in distressing times of pain and anxiety caused due to the problem is reduced, a major plus point for sales. Dr Indu Tandon, a well-known physiotherapist, who has gained much appreciation for her work in the 20 years of her physiotherapy career helping local, national, and international people says that currently, there is no such device in the market that helps with LBP. So far we are testing and finding multiple ways to solve this very problem. In this research paper we have mentioned a detailed analysis and design approach of our two test prototypes which give us commendable analysis results.
A Review Article of Statcom Design and Inhancement Controlling of Power Quality
Authors:- M.Tech. Scholar Rashmi Singh, Prof. Mr. Arun Pachori, Asst. Prof. Mr. Pawan Kumar Pandey
Abstract- This paper deals with A New design of D-STATCOM (Distribution Static Compensator) is Used for Mitigation of Power Quality Problems under unbalance caused by various loads in distribution system. This paper addresses the modeling and analysis of custom power controllers, power electronic-based equipment aimed at enhancing the reliability and quality of power flows in low voltage distribution networks using DSTATCOM. A new PWM- based control scheme has been proposed that only requires voltage measurements the operation of the proposed control method is presented for D-STATCOM. Simulations and analysis are carried out in MATLAB/SIMULINK with this control method for two proposed systems.
Design and Development of Remotely Firmware Upgradation Based on Wired and Wireless Protocols
Authors:- Arbaaz khan, Mohd Anas Ali, Shanila Mahreen
Abstract- The focus of this work is on design considerations and the implementation of a highly portable bootloader for embedded devices with limited resources that allows for fail-proof firmware upgrades over the air. For STM32 series system on chips, a bootloader was created in this thesis that enables firmware upgrades over Wi-Fi networks. The implementation is for the STM32, but there are also general design considerations for bootloaders for fail-proof firmware updates presented here. As a result, I think that the concepts discussed here can be easily applied to projects involving similar embedded systems. First, a brief explanation of the over-the-air firmware upgrade process, its difficulties, and the related system components follows. The specifics of the implementation are then explained. A number of individual components are discussed in detail based on the System architecture. Discussion of the dependability and fault tolerance of the firmware upgrade process is one of the thesis’ main focuses. Several experiments are run to validate the implementation.
Identification of Long Run and Short Run Linkage between Energy and GDP towards Carbon Emissions- An Indian Perspective
Authors:- Dr. Neetu Narwal
Abstract- Energy is the driving force behind the economic growth of any country, and it is said to be coupled with environment degradation. In India, CO2 emission is continuously increasing, and the major contribution lies in coal burning to meet electricity demands. This study is an attempt to analyse the World Bank data pertaining to India in order to find the aggregate effect of different energy sources on economic growth. The results show that energy variables and GDP are cointegrated and hence there exist long term relationship between these variables. The outcomes are evident for existence of both short-term and long-term association of all energy variables and GDP with the carbon emission. This study further analyses the current energy scenario of India and suggests that there is urgent need to Transend towards alternative resources of energy like hydroelectric or nuclear energy.
Productivity Enhancement in Machining of Al6061 alloy Subjected to Dry and Nano-fluid Assisted Minimum Quantity Lubrication Approach
Authors:- Durgesh Singh, Sankalp Verma
Abstract- Since there is a lot of concern regarding energy wastage in the current world, so it is important to develop alternative methods which can be energy efficient for machining. Dry, minimum quantity lubrication (MQL) and nanofluid MQL-assisted are some of those processes. Investigation of experimental machining process parameters has been on notice for several decades among researchers. Aluminium (Al) 6061 alloys have been widely used in the field of automobile and aerospace industries owing to exceptional characteristics. In this paper, the Al6061 alloy specimen has been employed for the machining investigation subjected to dry, MQL and nanofluid (h-BN and Graphene) MQL-assisted CNC turning operation. The influence of the machining parameter on the wear response in the turning of Al6061 with dry and MQL employing nanoparticles-based nanofluid (h-BN and Graphene) was examined. These Al6061 alloy specimens were machined by varying the cutting speed (CS) (180-200 m/min), feed rate (FR) (0.1-0.3 mm/rev) and depth of cut (DOC) (0.5 mm) their influences on surface finish and tool wear were analyzed. The processes were performed in dry, MQL and nanofluid (h-BN and Graphene) MQL-assisted environments. It was found that machining under the nanofluid MQL-assisted conditions is preferable due to better surface finish and tool wear. Moreover, it was evident that machining characteristics were much more satisfactory in MQL-assisted conditions over the dry and MQL conditions.
An Overview of Fundamentals to Prospective of Immunometabolism in New Therapy
Authors:- Ahmad M Khalil
Abstract- Mitochondria represent a unique quality control cellular system, wherein they coordinate multiple functional activities. They are highly dynamic organelles and can easily modify their morphology by fusion or fission to adapt to cellular responses to various challenges. The intercommunication among mitochondria and other cellular organelles is responsible for the assembly and maintenance of the cell. The current overview followed PRISMA guidelines and used the PubMed/Medline databases to explore and summarize the entire literature generated and dedicated during the past few years to applications of mitochondrial biology in biomedicine. Analysis of the data demonstrated that mitochondria have a dual cellular function the “Immunometabolism”; in addition to their well-recognized bioenergetic function, they are key members of the innate immune system. Further, it is found that many disorders are associated with insufficient mitochondrial quality control. It is concluded that understanding the molecular mechanisms of mitochondrial function and dysfunction is a new exciting field. Outstanding candidate potential therapeutic roles of mitochondria are emerging. This research topic may take humanity to a new era of secure and efficient diagnosis, prevention, or therapy of human diseases.
Optimization of Machining Process Parameters of Al6061 alloy Subjected to Dry and Nano-fluid Assisted Minimum Quantity Lubrication Approach
Authors:- Durgesh Singh, Sankalp Verma
Abstract- Investigation of experimental machining process parameters has been on notice for several decades among researchers. Aluminium (Al) 6061 alloys have been widely used in the field of automobile and aerospace industries owing to exceptional characteristics. Surface finish and less tool wear for better productivity are prime requirements during machining processes. In most machining operations the main objective is the achievement of less surface roughness and tool wear. In this study, analysis has been performed for optimizing CNC machining process using Taguchi Method. We are analyzing the machine parameters and optimization of CNC machining employing Taguchi Method (L9 Orthogonal array); the parameters are cutting speed, feed rate and machining environment. We have considered three process parameters and their levels based on the analysis parameters which are affecting the machining process. For input machining process parameters experiments are designed using Taguchi L9 orthogonal standard array. For this purpose, MINITAB17 software is employed. While optimizing machining parameters lowest surface roughness (Ra) of 0.42310 μm was achieved corresponding to FR: 0.1 mm/rev, CS: 200 m/min. and Machining Environment: Groundnut oil/h-BN based nanofluid MQL-assisted machining.
Optimization for Speech to Text Conversion Using Convolutional Neural Network
Authors:- Rahul Singh Sengar, Vatsal Mehta
Abstract- The field of machine learning has taken a dramatic twist in recent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models are able to far exceed the performance of previous forms of artificial intelligence in common machine learning tasks. One of the most impressive forms of ANN architecture is that of the Convolutional Neural Network (CNN). CNNs are primarily used to solve dif icult image-driven pattern recognition tasks and with their precise yet simple architecture, of er a simplified method of getting started with ANNs. This document provides a brief introduction to CNNs, discussing recently published papers and newly formed techniques in developing these brilliantly fantastic image recognition models. This introduction assumes you are familiar with the fundamentals of ANNs and machine learning. The ability to accurately represent audio signals is central to language understanding. The network uses Conv1d, a global pooling operation over linear sequences. The network handles input audio signals of varying lengths and induces a feature graph over the audio signals that are capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the CNN in Modeling Audio signals to Texts. The network achieves the excellent performance of a greater than 25% error reduction in the last task with respect to the strongest baseline.
MPPT Integrate with Fuzzy Logic Control Compared with Conventional Techniques
Authors:-Krishan Kumar Meeena, Mr. Neeraj Sharma, Mr. Pushpendra
Abstract- Photovoltaic (PV), which functions on the concept of the photoelectric effect, is considered one of the environmentally beneficial Renewable Energy Sources (RES) that has a large deal of capacity and converts solar energy directly into electricity. Continually changing physical properties of PV systems are dependent on their surroundings. Therefore, it is crucial to regularly track the Maximum Power Point (MPP) in order to get the most power possible from PV. This paper discusses one artificial intelligence control tool, the fuzzy logic controller (FLC), as well as traditional hill climbing methods like perturb and observe (P&O) and incremental conductance (IC) for MPP tracking. Fuzzy controller application results in superior performance versus traditional methods. In comparison to other methods, the MPP stability produced by a fuzzy controller is higher. The amount of energy extracted from PV panels is also contrasted with other methods.
Asset Tracking Solution Based On Iot
Authors:- Shah Mohammed Aliuddin, Mohammed Anwaruddin, Shanila Mehreen
Abstract- The purpose of this project of this project is to integrate and implement IoT technology with Asset Tracking Solution. Asset Tracking Solution is a program that manages and monitors the IT assets found. It tracks an asset for its entire duration in the organization. You can manage LAN as well as work from home endpoints from a central location. Using FATS, you can manage both hardware and solution assets in your network anywhere, anytime, from your laptop or mobile phone. Fixed Assets Tracking Solution applies asset with Unique Asset Id (UAID) by means of barcode or RFID tags. These tags can be in compliance with requirements of various authorities like IEEE, SOX, etc.
A Review of System for Detecting Intruders Using Convolutional Neural Networks
Authors:- M.Tech. Scholar Sanjay Soni, Assistant Prof. Aditi Khemariya
Abstract- Internet use has made computer networks vulnerable to cyberspace-related attacks. As a consequence, researchers invented intrusion detection systems, or IDSs. Identifying network intrusions is an issue in network security research. As a preventative measure, it helps identify unauthorised network usage and assaults. Methods such as machine learning (ML), Bayesian algorithms, nature-inspired meta-heuristic methods, swarm smart algorithms, and Markov neural networks have been developed to find the most important characteristics and boost intrusion detection system efficacy. Hundreds of active studies were compared to various data sets over many years. This work analyses single, hybrid, and ensemble classification approaches. The analysis is broad. We compared the publications’ IDS results, limitations, and datasets. This helped us evaluate the research’s quality. Below is a possible future research path.
System for Detecting Intruders Using Convolutional Neural Networks
Authors:- M. Tech. Scholar Sanjay Soni, Assistant Prof. Aditi Khemariya
Abstract- On the internet, malicious activities may take place, which can infect a single computer or bring down a whole network. It’s possible that they are chosen at random. The number of people connecting to the internet at an ever-increasing pace makes it more difficult to keep up. The internet, just like real life, may provide a number of potential safety hazards. The IDS software watches a network for activity that might be hostile or suspicious. IDS stands for intrusion detection system, and it is a kind of technology that helps detect attacks on computer systems and determine who carried them out. Machine learning (ML) strategies were used in the past to increase IDS accuracy and the results of intrusion detection. In this article, we will go through the process of constructing IDS by using CNN approach. This is a method that may be used when developing effective IDS. On the KDD, the technique that was proposed will be implemented (Knowledge Discovery Dataset). The accuracy of the technique that has been provided is clearly superior to that of SVM, Naive Bayes, and Decision Tree. It is possible to have faith in this because: The following outcomes were a consequence of using our method: 3.24 minutes is the amount of time required for performance, with an accuracy of 96.78% and an error rate of 0.21 percent.
Distant-Hit Algorithm for Longest Common Subsequence
Authors:- P. S. Sathya Narayanan
Abstract- LCS stands for Longest Common Subsequence. Generally a subsequence is a set of characters that appear in same order relatively but they necessarily need not be contiguous. For example let there be a word ‘LOVE’ here ‘LO’ , ‘LVE’, ‘OV’, ‘LOVE’, etc… are known as the subsequences of the word LOVE. We have been using the LCS algorithm all these days which uses the Dynamic Programming Approach to find the length of the Longest Common Subsequence between two words which has the time complexity of O(m*n) and the SpaceComplexity of O(m*n) where m is the length of comparing string (say String1) and n is the length of compared string (say String2). In this approach the data structure used to declare the comparison space is a 2D Array and the datatype used for it is Integer which costs literally 2 – 4 bytes of space for every digit based on the compiler. Contradicting to this way the Approach mentioned in this paper (Distant-Hit Algorithm/Dist-hit Algorithm) can do the same LCS task by using an 1D Array data-structure of Bool datatype which actually costs 1 byte per digit. The space complexity is O(m+n) and time complexity is same as O(m*n). Since This LCS algorithm is used in various modern-day fields like linguistics, bioinformatics, Common sequence identification, biometrics, revision control systems (GIT)etc… This sort of optimization will be helpful in reducing the memory used for the comparison space of two strings.
Gender Bias and Economic Growth in West Java
Authors:- Magdalena Sinaga, Asst. Dr. Lukytawati Anggraeni
Abstract- LCS stands for Longest Common Subsequence. Generally a subsequence is a set of characters that appear in same order relatively but they necessarily need not be contiguous. For example let there be a word ‘LOVE’ here ‘LO’ , ‘LVE’, ‘OV’, ‘LOVE’, etc… are known as the subsequences of the word LOVE. We have been using the LCS algorithm all these days which uses the Dynamic Programming Approach to find the length of the Longest Common Subsequence between two words which has the time complexity of O(m*n) and the SpaceComplexity of O(m*n) where m is the length of comparing string (say String1) and n is the length of compared string (say String2). In this approach the data structure used to declare the comparison space is a 2D Array and the datatype used for it is Integer which costs literally 2 – 4 bytes of space for every digit based on the compiler. Contradicting to this way the Approach mentioned in this paper (Distant-Hit Algorithm/Dist-hit Algorithm) can do the same LCS task by using an 1D Array data-structure of Bool datatype which actually costs 1 byte per digit. The space complexity is O(m+n) and time complexity is same as O(m*n). Since This LCS algorithm is used in various modern-day fields like linguistics, bioinformatics, Common sequence identification, biometrics, revision control systems (GIT)etc… This sort of optimization will be helpful in reducing the memory used for the comparison space of two strings.
A Review of Crime Detection Using Machine Learning
Authors:- Sameeksha Bhati, Assistant Professor Priyanshu Dhameniya
Abstract- As a societal and economic issue, crime has a negative impact on people’s standard of living and the health of the economy [1]. The particulars of criminal behavior vary greatly from one group or community to the next. The crime rate can be predicted, at least in part, by looking at socioeconomic indicators such as levels of education, poverty, unemployment, and weather. Vancouver, one of Canada’s most populous and diverse urban centers, is home to people of many different cultural backgrounds and ancestries. There was a 1.5% decrease in 2017’s overall crime rate in Vancouver, although a persistent problem remains with car theft. The residential burglary rate in Vancouver dropped by 27 percent after the Vancouver Police Department (VPD) used a crimepredictive model to predict such crimes. Predicting criminal activity is a tool used by law enforcement that relies on data and statistical analysis to pinpoint potential hotspots. Research in this area has continued in several countries.
A Review on Design and Thermal Analysis of Double Pipe Heat Exchanger by Changing Mass Flow Rate
Authors:- M.Tech Scholar Naveen Kumar, Prof Abhishek Bhandari
Abstract- Heat exchangers are employed in a variety of applications, included power plants, nuclear reactors in energy production, RAC systems, self-propelled industries, food industries, heat retrieval systems, & chemical handling. The techniques of upgrading can be divided into two categories: active and passive ways. The active approach necessitates the use of peripheral forces. Discrete surface geometries are required for passive approaches. These strategies are commonly utilized to increase heat exchanger performance. Helical tubes have already been designated as among the passive heat transfer enhancement materials. Due the short construction and high heat transfer coefficient, and they will be widely employed in various industrial applications. The thermo-hydraulic performance of various configurations of gas- to-liquid double-pipe heat exchangers featuring helical fins was reviewed.
Survey on Existing Online Election Systems
Authors:- Milna Eldho, Vindhuja K, Pooja Nair, Simi M S
Abstract- Traditional voting machines used are quite time-consuming, energy consuming and requires the tasks to be done at an assigned place. The basic idea of such systems is to create an Online Voting System that will help to reduce the use of the manual voting system with added security using various technologies to facilitate voting from a remote place. The proposed systems includes multiple layers of verification to ensure the reliability of the device which includes face verification, OTP, verification, biometrics etc with validation data. Each voter can access to the system only when being recognized and checked with the given database of enlist voter to proceed for the further process.
Development and Analysis of Flow through Annular Curved Diffuser with Fins
Authors:- K. Manoj Kumar, S. Deepthi, Dr. P. Srikar
Abstract-Diffusers are largely used in centrifugal compressors, axial flow compressors, combustion chambers, ram jets, gas turbine engines, inlet portions of jet engines, and so on. A minimal change in pressure recovery will increases the efficiency of the machinery. Hence diffusers are absolutely essential for good turbo machinery performance. The internal part of gas turbine engines are curved annular diffusers at high speed air craft. By decreasing the entire pressure loss the diffuser facilitate effective working of the combustor. Performance of those diffusers depends on the geometrical dimensions of diffuser and inlet conditions. In the present investigation, distribution of static pressures inside the diffuser and velocity of the fluid at outlet are studied with help of CFD on a curved annular diffuser of 70 angle of turn, circular hub of diameter to 20mm and also by varying the fins on circular hub of height to 5mm and 10mm with a thickness of 3mm and allowing the air as fluid to pass through the diffuser. Annular curved diffuser is modeled by using CREO Parametric. Modifications are done by adding fins to the model and CFD analysis is done in ANSYS Fluent to determine the flow characteristics.
Nanotechnology in Diagnosis
Authors:- Fayza Khan
Abstract-Background: The current study outlines nanotechnology’s applications and uses in the diagnosis, screening purpose and treatment of variety of ailments and used to deliver drugs, chemotherapeutic agents or imagining substances, or antigen, antibody, DNA or RNA. Main Body: Nanotechnology offers multiple benefits in treating chronic human diseases by Site specific and target-oriented delivery. The discovery and application of nanomaterials and nanotechnology in improving the efficacy of both new and old pharmaceuticals, such as natural products, as well as selective detection through disease marker molecules, allows for individualised treatment. Conclusion: Nanotechnology can be used to diagnose and treat a variety of deadly diseases, like tuberculosis, cancer and several neurological disorders.
Review of Wormhole Attack on Mobile Ad-hoc Network
Authors:- M.Tech.Scholar Ms. Babita Kumari, Prof. Dr Rakesh Sharma
Abstract- WSNs are unstable because to the wireless nature of communication since any attacker with the desire to steal the data may do so by inserting rogue nodes into the network. Attackers may carry out this by launching attacks such as wormhole, floods, grey hole, and others. The goal of routing protocols is typically to determine the shortest route between a source and a destination node. The hop count is used as a statistic to calculate the journey length. The wormhole attack, one of the several above-described attacks, is risky since it builds a tunnel by bypassing a few nodes in between them. The hop length is automatically decreased by the tunnel, resulting in a short route between the source and destination nodes. This article provides a concise overview of the methods or strategies for the identification and defence against wormhole attacks.
Review on Design and Analysis of Bridge Structures
Authors:- Dilip Patidar, Asst.Prof. Rahul Sharma
Abstract- Bridge is the structure which is used for carrying the traffic over the valley or river by connecting highways or railways. The current research reviews the existing work conducted in the field of design and analysis of bridge structure. The study presents the study of bridge structures using both experimental and numerical techniques. The formulation of different output parameters associated with strength and deformation of bridge structure is presented.
Design and Control Grid-Connected Isolated PV Microinverters: A Review
Authors:- Sonali Mathur, Assistant Professor Mukesh Kumar
Abstract- Galvanic isolation is a very significant feature that should be present in grid-connected photovoltaic (PV) microinverters because it addresses both power quality and safety concerns. However, the efficiency of the isolated varieties of microinverters is reduced due to the presence of high-frequency transformers and significant switching losses. In recent times, a number of different isolated topologies have been suggested as a means of increasing the efficiency as well as the lifetime of PV converters. The purpose of this work is to provide a thorough analysis and assessment of the most recent isolated topologies for PV microinverters. In terms of the number of stages at which they process power, these topologies can be divided into two distinct classes: 1) single-stage microinverter, and 2) multi-stage microinverter. A number of possible topologies are discussed, contrasted, and analyzed in terms of the power losses that occur at various stages, control mechanisms, where the decoupling capacitor should be located, and the overall cost. In order to acquire a comprehensive image of the framework for the future generation of isolated PV microinverters, recommendations are made to improve the existing topologies and select the relevant control mechanisms.
Creating Context-Aware Chatbots In Salesforce Using LLMs And Einstein AI
Authors: Dmitry Ivanov
Abstract: The integration of Large Language Models (LLMs) and Einstein AI within the Salesforce ecosystem marks a transformative leap in customer service automation. Context-aware chatbots, powered by these advanced technologies, are redefining how organizations interact with their customers by delivering highly personalized, intelligent, and efficient support. Unlike traditional chatbots that rely on rigid, preprogrammed scripts, modern Salesforce chatbots leverage the vast capabilities of LLMs to understand and process natural language, interpret user intent, and access relevant data from the CRM in real time. This article explores the foundational principles and practical strategies for building context-aware chatbots in Salesforce, focusing on the interplay between LLMs, Einstein AI, and the robust data integration offered by the Salesforce platform. Contextual awareness is achieved through the seamless fusion of machine learning, deep learning, and transformer models, enabling chatbots to analyze the full context of customer queries, including past interactions, purchase history, and business documentation. This results in responses that are not only accurate but also tailored to the specific needs and preferences of each user. The article will also discuss the critical role of Retrieval-Augmented Generation (RAG) models in grounding chatbot responses in up-to-date, trusted data. By harnessing these technologies, businesses can automate routine inquiries, reduce resolution times, and free up human agents to focus on complex, high-value tasks. The adoption of context-aware chatbots is shown to significantly improve customer satisfaction, foster loyalty, and drive operational efficiency. Furthermore, the article highlights the importance of omnichannel deployment, analytics-driven optimization, and robust security measures in ensuring the success of Salesforce chatbots. It addresses the challenges and best practices associated with implementation, including customization, scalability, and ongoing maintenance. Through real-world examples and expert insights, the article demonstrates how organizations can leverage the combined power of LLMs and Einstein AI to create next-generation chatbots that deliver exceptional customer experiences and sustainable business value.
DOI:
Building Trustworthy AI Chatbots With Salesforce Einstein And Copilot AI
Authors: Nursyafiqah Ahmad
Abstract: In the rapidly advancing digital landscape, artificial intelligence (AI) chatbots have become pivotal in shaping customer interactions, automating routine tasks, and enhancing operational efficiency across industries. Salesforce’s Einstein and Copilot AI represent the forefront of this transformation, offering robust, intelligent conversational agents that leverage natural language processing (NLP), machine learning, and deep integration with enterprise data systems. This article explores the multifaceted process of building trustworthy AI chatbots using Salesforce’s advanced AI solutions, focusing on both technological innovation and ethical considerations.The discussion begins with an overview of Salesforce’s AI ecosystem, highlighting the capabilities of Einstein Chatbots and Copilot AI in delivering personalized, context-aware customer experiences. It then delves into the practical steps for developing, deploying, and maintaining these chatbots, emphasizing the importance of transparency, data privacy, and continuous learning. The article further examines how Einstein and Copilot AI can be customized for various business functions—such as sales, marketing, and customer service—while ensuring compliance with industry standards and regulatory requirements. A significant portion of the article is dedicated to the ethical guidelines that underpin trustworthy AI, including the necessity of clear communication about chatbot identity, limitations, and data usage. The piece also addresses the challenges of bias mitigation, security, and user trust, offering actionable strategies for organizations to foster confidence in their AI-driven solutions. By integrating Salesforce’s AI tools with best practices in ethical AI development, businesses can create chatbots that not only streamline operations but also build lasting relationships with customers. The article concludes with insights into the future of AI chatbots and the evolving expectations of users in a digital-first world.
DOI:
A Review Article on Auto-Categorization of Syslogs Using NLP and Deep Learning
Authors: Nisha Verma, Gaurav Nair, Swathi Reddy, Tarun Bhatia
Abstract: In modern IT ecosystems, syslogs serve as the primary diagnostic and auditing trail, capturing granular system-level, application, and security events. As infrastructures grow in scale and complexity spanning cloud-native applications, hybrid UNIX environments, and distributed edge deployments the volume of syslog data has become overwhelming. Traditional rule-based parsing methods and regex-driven filters struggle to scale across heterogeneous logs, leading to missed alerts, alert fatigue, and significant operational overhead. This review explores the transformative role of Natural Language Processing (NLP) and deep learning techniques in auto-categorizing syslogs with accuracy, adaptability, and semantic understanding. The paper begins with an overview of syslog formats, protocols, and the inherent variability in message content and structure. It then introduces modern NLP preprocessing techniques such as tokenization, entity masking, embedding strategies, and contextual vectorization. A detailed examination of deep learning architectures including CNNs, RNNs, LSTMs, and Transformer-based models like BERT is provided to demonstrate their effectiveness in capturing syntactic and contextual nuances. The review also presents methodologies for supervised, semi-supervised, and weakly supervised learning, with practical tools for building ground truth corpora. Operational pipeline considerations such as real-time streaming ingestion, model deployment, latency optimization, and SIEM integration are addressed. Use cases spanning data centers, telecom networks, and security monitoring highlight the practical impact of AI-based syslog categorization. Additionally, the article explores key challenges, including model interpretability, data privacy, false positives, and compliance risks. Future trends such as domain-specific Transformers, self-supervised log learning, federated training, and multi-modal observability are discussed as avenues for further innovation. Ultimately, this review positions NLP and AI as foundational to building scalable, intelligent, and proactive log management systems, paving the way for predictive operations and automated root cause analysis in complex enterprise environments.
DOI: https://doi.org/10.5281/zenodo.15846838
The AI-Powered Marketing Funnel: Predicting, Personalizing, And Converting Like Never Before
Authors: Karthik Vemana
Abstract: Artificial Intelligence (AI) is transforming the traditional marketing funnel into a dynamic, responsive system that continuously adapts to customer behavior. By enhancing every stage—from awareness to retention—AI enables marketers to predict user intent, personalize engagement, and optimize conversions with unprecedented accuracy and speed. This article explores how AI tools are reshaping modern marketing through intelligent audience targeting, real-time personalization, predictive lead scoring, automated content delivery, and advanced analytics. It also addresses ethical concerns, data governance, and the importance of human oversight. With AI acting as both a strategic advisor and tactical engine, the marketing funnel becomes more efficient, customer-centric, and performance-driven. Businesses that embrace AI-powered marketing gain a distinct competitive edge, unlocking higher ROI, deeper customer loyalty, and a more agile growth model. This is not just an upgrade to existing systems—it’s a fundamental reinvention of how brands attract, convert, and retain customers in the digital age.
DOI: http://doi.org/10.5281/zenodo.16742725
The Business Of Biohacking: How Entrepreneurs Are Monetizing AI-Driven Health And Longevity Solutions
Authors: Meenakshi Vudathu
Abstract: Biohacking has transitioned from a fringe concept into a thriving commercial movement—driven by rising consumer demand for personalized health, performance optimization, and longevity. At the core of this transformation is Artificial Intelligence (AI), which enables real-time, adaptive analysis of biological data, empowering individuals to take control of their wellness journeys. Entrepreneurs are leveraging AI to build scalable biohacking solutions—from wearable-integrated apps and personalized supplement platforms to predictive diagnostics and neurotechnology. This article explores how AI is accelerating innovation in the biohacking economy and highlights various monetization models, including subscription services, DTC smart devices, algorithm licensing, and content-based ecosystems. With compelling case studies and a candid look at challenges such as data privacy, algorithmic bias, and regulatory uncertainty, the article also forecasts the future potential of AI-driven wellness—including digital twins, autonomous health assistants, and population-scale insight generation. Ultimately, it reveals how ethical, consumer-focused entrepreneurship in this space can both redefine wellness and deliver real, lasting health impact.
DOI: http://doi.org/10.5281/zenodo.16742771
The Future Of Healthcare Lies At The Intersection Of Artificial Intelligence And Entrepreneurship
Authors: Naveen Kattamanchi
Abstract: The future of healthcare is being shaped at the powerful intersection of Artificial Intelligence (AI) and entrepreneurship. While traditional healthcare systems face limitations in scalability, personalization, and responsiveness, AI offers unprecedented capabilities in data analysis, diagnostics, and predictive modeling. Entrepreneurs are harnessing these capabilities to develop agile, impactful solutions that challenge legacy systems and address long-standing inefficiencies in care delivery. By combining AI's computational power with the speed, adaptability, and user-focus of startups, a new generation of health innovations is emerging—from virtual care platforms and AI-powered diagnostics to personalized mental health tools and chronic disease management systems. This article explores how AI-driven entrepreneurs are transforming global healthcare, highlights key opportunities and challenges, and emphasizes the importance of ethical, inclusive design. As we transition from reactive to proactive models of care, this convergence of AI and entrepreneurship holds the potential to create a more intelligent, equitable, and future-proof health system for all.
DOI: http://doi.org/10.5281/zenodo.16742806
From Zero To One In The Age Of AI: A New Blueprint For Aspiring Entrepreneurs
Authors: Keerthana Rajan
Abstract: Artificial Intelligence (AI) is reshaping the entrepreneurial landscape, allowing individuals to build scalable, intelligent businesses from scratch with unprecedented speed and efficiency. This article offers a modern blueprint for aspiring founders navigating the “zero to one” journey in the AI era. It explores how AI accelerates every phase of a startup’s lifecycle—from identifying high-potential problems and prototyping solutions to scaling operations and managing customer relationships. By leveraging no-code tools, pre-trained models, and intelligent automation platforms, solo entrepreneurs and lean teams can compete at a level once reserved for well-funded ventures. The article also covers ethical considerations, team dynamics in AI-assisted ventures, and evolving investor expectations. Packed with practical insights, tools, and case references, it provides a roadmap for launching responsible, data-driven ventures that are not only viable but also future-ready. Ultimately, it argues that in the age of AI, building a startup is no longer about brute force—it’s about clarity, creativity, and leveraging intelligence as a multiplier.
DOI: https://doi.org/10.5281/zenodo.16743271
Fueling Entrepreneurial Ecosystems With AI-Powered Incubation And Startup Support Platforms
Authors: Anirudh Chittibabu
Abstract: As entrepreneurship scales across global markets, traditional incubation and startup support models are under pressure to serve more founders, more efficiently, and more inclusively. This article explores how Artificial Intelligence (AI) is transforming entrepreneurial ecosystems by powering a new wave of intelligent, scalable, and adaptive incubation platforms. From personalized mentorship matching and automated market research to predictive analytics and no-code MVP development, AI is reshaping how early-stage ventures are launched and scaled. The integration of AI not only boosts the efficiency and precision of startup support but also democratizes access to resources—reaching underrepresented founders and decentralizing innovation beyond major tech hubs. Through real-world examples and case studies, the article illustrates measurable outcomes and highlights both the promise and ethical challenges of AI in ecosystem design. Ultimately, it offers a roadmap for incubators, accelerators, and ecosystem builders seeking to harness AI as a force multiplier for innovation, inclusion, and long-term entrepreneurial success.
DOI: https://doi.org/10.5281/zenodo.16743304
The Influence Of Big Data Analytics On Credit Scoring And Lending Practices In The U.S.
Authors: Oluwabanke Aminat Shodimu, Kofi Mensah
Abstract: The integration of big data analytics into credit scoring and lending practices has fundamentally transformed the financial services landscape in the United States. This transformation represents a paradigm shift from traditional credit assessment methods to sophisticated, data-driven approaches that leverage vast amounts of structured and unstructured data. This article examines how big data analytics is revolutionizing credit scoring processes, making them more personalized and dynamic while simultaneously raising important questions about fairness, privacy, and financial inclusion. Through comprehensive analysis of current practices, regulatory frameworks, and emerging trends, this study evaluates the multifaceted implications of big data adoption in the credit industry, highlighting both the unprecedented opportunities for improved risk assessment and the potential challenges that accompany this technological evolution.
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