Journal of Science and Technology Excellence
Welcome to the Journal of Science and Technology Excellence (JSTE) The Journal of Science and Technology Excellence (JSTE) is an international, peer-reviewed, open-access journal published by Novara Publishers. Our mission is to provide a premier scholarly platform for researchers, engineers, academicians, professionals, and industry experts to publish high-quality, innovative work across all major engineering domains. At JSTE, we believe in advancing the frontiers of technology. Our aim is to promote research that drives innovation and solves real-world challenges — from intelligent computing to sustainable energy. Join us in fostering scientific excellence. Aims & Scope The Journal of Science and Technology Excellence (JSTE) provides a platform for high-quality research in science, engineering, and technology. JSTE publishes original research, review articles, and case studies in a broad range of disciplines, including but not limited to: Science Fields: Physics and Applied Physics Chemistry and Materials Science Environmental Science Biotechnology and Life Sciences Technology & Engineering Fields: Electrical Engineering Mechanical Engineering Civil Engineering Computer Science and Engineering Electronics and Communication Engineering Artificial Intelligence and Machine Learning Internet of Things (IoT) Renewable and Sustainable Energy Other Emerging Engineering Fields JSTE aims to promote interdisciplinary research that bridges scientific discovery with technological innovation, supporting advancements that address real-world challenges. The journal follows a double-blind peer-review process to ensure the highest standards of quality and academic integrity. All published articles are made available under the Creative Commons Attribution (CC BY 4.0) License, enabling unrestricted access, sharing, and distribution with proper attribution. JSTE adheres to the ethical standards recommended by the Committee on Publication Ethics (COPE) and ensures long-term preservation of published content through LOCKSS and CLOCKSS archiving systems. Journal of Science and Technology Excellence (JSTE) Frequency of Publication: Quarterly (January, April, July, October) ISSN: 3108-3102 Publisher Name: NOVARA PUBLISHERS Publisher Address: #605, Jasmine Block, RDB Coconut Grove, Dynamics Colony, Mayuri Nagar, Hyderabad, Telangana – 500049, India Editor-in-Chief: Dr. G. Kishor Kumar Copyright: Journal Science and Technology Excellence Starting Year: 2025 Subject: Multi-disciplinary Language: English Publication Format: Online Phone Number: +91 9030161092 Email IDs: editor@jstejournal.com | editorjstejournal@gmail.com | info@novarapublishers.com / novarapublishers@gmail.com Website: jstejournal.com Publisher Website: novarapublishers.com
All articles published in the Journal of Engineering Excellence (JEE) are licensed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license, authors retain full copyright of their work while granting permission for anyone to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or author — provided that the original work is properly cited. This open-access license ensures maximum dissemination and impact of the published research by allowing free and immediate access to scholarly work. For more details, please refer to the official license page: ???? https://creativecommons.org/licenses/by/4.0/
IMPROVING SOCIAL MEDIA PLATFORM INTEGRITY USING MULTI FEATURE-BASED MALICIOUS PROFILE DETECTION
The purpose of this work is to enhance the integrity of social media platforms by utilizing a multi-feature-based strategy for detecting harmful profiles. This will allow for the identification and mitigation of coordinated inauthentic accounts, spam accounts, and fraudulent accounts. Because of the proliferation of online interactions, malicious actors can easily take use of social media platforms to spread false information, conduct phishing attacks, influence public opinion, and compromise users' privacy. Behavioral dynamics, account age, username patterns, bio characteristics, and content-based features like posting frequency, sentiment patterns, and hashtag usage are all part of the proposed method. Network-based metrics and interaction graphs are also part of it. Behavioral features like clustering behavior and automation indicators are also part of it. The detection accuracy is improved and the number of false positives is decreased by using advanced machine learning and ensemble classification algorithms. Using a thorough multi-dimensional feature space, this research aims to enhance social media ecosystems' trust, transparency, and security in order to foster safer online communities and more reliable digital communication environments.
Copyright (c) 2026 Journal of Science and Technology Excellence https://creativecommons.org/licenses/by-nc-nd/4.0
PROFITABILITY RATIO ANALYSIS IN INDIAN FIRMS AT ARVIND LTD
Profitability ratio analysis is essential for business financial and operational health. Aravind Ltd., a major Indian company, is being examined for long-term financial health and profitability. Operational profit margin, ROA, and ROE were assessed to determine the company's profitability based on sales, assets, and owners' equity. In the Indian market, the analysis shows the company's strengths and opportunities for improvement. The paper's findings help investors and management understand Aravind Ltd.'s financial performance. This aids strategic planning and decision-making.
COST SENSITIVE FINANCIAL FRAUD DETECTION USING VALUE AT RISK AND MACHINE LEARNING MODELS
In a cost-sensitive financial fraud detection system, our research uses advanced machine learning and Value at Risk (VaR) to help people perform more accurate financial uncertainty assessments. The vast majority of outdated fraud detection systems stress categorization over the financial consequences of false positives and negatives. The suggested approach uses a numerical risk indicator called Value at Risk to calculate the fraud costs. There are several parallels between financial risk management and predictive models. Cost-sensitive machine learning algorithms give high-risk agreements precedence over incorrect classifications in order to reduce expected financial losses. Supervised learning and statistical risk assessment enhance output and problem-solving skills. This makes it possible for organizations to minimize fraudulent activity and maximize their resources.
CAREER DEVELOPMENT PROGRAMS AND WORKFORCE GROWTH AT ITC LIMITED
Employee development and career growth are essential to any company's success in the fast-paced corporate world of today. By putting in place extensive job advancement programs, ITC Limited, an Indian corporation that sells a wide range of goods, has continuously placed a high priority on the skill and motivation growth of its workers. Formal training programs, mentoring programs, performance management systems, and leadership development frameworks that match individual goals with organizational goals are examples of this. By investing in staff development, ITC develops its own talent pool and promotes creativity, flexibility, and a long-term competitive edge. The creation, execution, and results of ITC's job development initiatives are all examined in this study. It emphasizes how important these initiatives are to the company's growth, staff retention, and workforce development.
WORK-LIFE BALANCE PROGRAMS FOR GIG WORKFORCE AT SWIGGY
Gig workers, and Swiggy delivery partners in particular, encounter challenges in maintaining a work-life balance as a result of their unpredictable income, irregular schedules, and demanding responsibilities. Swiggy, a food delivery application, has implemented numerous initiatives to assist its delivery partners in addressing these challenges and enhancing their overall quality of life. Mobile medical units, teleconsultation services, public restroom accessibility, flexible work schedules, and health and safety regulations are all being implemented in an ongoing endeavor to improve working conditions. Programs such as Swiggy Skills and Project Next contribute to long-term professional progress and economic stability by providing upskilling opportunities and alternative career paths. The extensive support system for gig laborers is supplemented by financial education, social security benefits, and disaster assistance. Swiggy has implemented these measures to establish a sustainable workplace, acknowledging the challenges associated with contract labor.
IMPROVING MARKETING STRATEGIES WITH LRFS BASED CUSTOMER SEGMENTATION FOR ONLINE PLATFORMS
In order to enhance online marketing strategies, this study uses client segmentation based on LRFS. The LRFS model examines lifetime value and consumer behavior using the Length, Recency, Frequency, and Spending variables. Transactional data is used in the study to classify clients for targeted promotions, personalized marketing, and successful customer retention. The findings demonstrate that LRFS-based segmentation improves profitability, resource allocation, marketing effectiveness, and customer engagement. According to the study, data-driven segmentation is essential for competitive advantage and the expansion of the digital market.
PREDICTIVE MODELING FRAMEWORK FOR ENERGY ECONOMY OF ELECTRIC TRANSIT BUSES
This research develops a predictive modeling system for assessing and improving the energy efficiency of electric cars under a variety of operating and environmental conditions. This research develops a data-driven model for energy consumption and efficiency forecasts by utilizing real-time operational data such as route parameters, passenger load, traffic patterns, driving behavior, battery performance, and weather conditions. In order to determine the main factors impacting battery discharge rates and variations in range, we use sophisticated statistical approaches and machine learning algorithms to examine energy consumption trends. Public transportation that is good for the environment and the bottom line may be more easily developed with the help of this platform, which allows scenario-based simulations to look at charging schemes, route design, and fleet deployment methods. By improving the accuracy of energy consumption projections, the proposed methodology enhances operating efficiency, decreases lifecycle costs, and facilitates strategic decision-making for urban electric transportation systems.
NETWORK SECURITY ENHANCEMENT THROUGH MACHINE LEARNING-BASED ATTACK DETECTION
This paper talks about a way to improve network security through intelligent threat identification that is based on machine learning. Cyberattacks are becoming more complex and changing at a rapid pace, making it difficult for traditional rule-based security solutions to keep up. In order to identify legitimate and malicious actions in real-time, the suggested system makes use of state-of-the-art machine learning techniques to examine network traffic patterns. Packet size, protocol behavior, and traffic flow characteristics are just a few of the many features that are gathered in order to train classification models. Attacks such as denial-of-service (DoS), infiltration attempts, and unauthorized access are all detectable by the system. Experimental results show improved detection accuracy, reduced false positives, and quicker reaction times when compared to earlier techniques. By allowing adaptive learning and continual development, the suggested methodology greatly enhances network defensive mechanisms and offers a scalable answer to contemporary cybersecurity challenges.
ATTENTION BASED DEEPFAKE DETECTION LEVERAGING LONG RANGE DEPENDENCIES
This investigation demonstrates an improved method of employing deep learning that employs attention processes to identify long-range connections between temporal and visual data in order to identify deepfakes. The proposed attention-based design, which integrates transformer models and self-attention, more effectively learns global contextual relationships across frames than traditional convolutional approaches, which frequently struggle to identify nuanced and widely dispersed changes. The system improves its ability to identify intricate and high-quality deepfakes by concentrating on critical face features and simulating the interaction between frames. The detection accuracy, robustness, and generalization have improved across a variety of datasets as a consequence of the experiments. This demonstrates the significance of long-range dependency modeling in addressing the increasing issues that advance synthetic media generate.
LIQUIDITY MANAGEMENT IN OIL COMPANIES AT INDIAN OIL CORPORATION LTD
Liquidity management is specifically critical to the financial and operational performance of energy firms, particularly in a capital-intensive and unpredictable industry such as petroleum. This investigation pertains to Indian Oil Corporation Limited (IOCL), the largest public sector oil company in India, which is responsible for the distribution, sale, and refining of petroleum products. The analysis demonstrates how IOCL manages its short-term assets and obligations by identifying the optimal balance between profitability and solvency. In light of factors such as regulatory regulations, high working capital requirements, and volatile crude oil prices, the current ratio, fast ratio, and cash flow analysis are critical metrics for evaluating a company's ability to meet its immediate obligations.
DERIVATIVES MARKET REGULATIONS AND INVESTOR PROTECTION AT ICICI SECURITIES
The rapid growth of India's derivatives business has enhanced the need for strong rules and smart policies to protect investors. This case paper uses ICICI Securities to evaluate derivatives market control's evolution, efficacy, and problems. To regulate derivatives trading, SEBI and stock exchanges use risk management, disclosure, and surveillance. This examination examines each method in detail. This paper examines ICICI Securities' brokerage, advisory, risk controls, and customer education programs to see if they meet these standards. The brokerage must evaluate compliance protocols, technology safeguards, and complaint resolution techniques to protect small investors, decrease counterparty risk, and promote market transparency in a highly leveraged market.
TRANSFORMATIONAL LEADERSHIP AND EMPLOYEE MOTIVATION AT INFOSYS
Transformative leadership affects employee motivation at Infosys, a large IT services business. The paper examines how Infosys leaders boost intrinsic motivation, engagement, and commitment by targeting employee performance and attitudes with intellectual stimulation, personalized care, role-modeling, and inspiring vision. This paper explores leadership theory, organizational structure, and employee views to evaluate how much transformational leadership boosts motivation in a competitive, innovation-driven setting.
TALENT ACQUISITION TRENDS AND BEST PRACTICES AT YASHODA HOSPITALS
This essay examines how Yashoda Hospitals adapts to new recruitment trends and best practices to attract and retain top personnel, demonstrating how modern healthcare organizations adapt to attract and keep top talent. This paper examines how companies are using AI, ATS, and online evaluations to improve job hiring. The paper suggests many ways the hospital may build a diverse and skilled workforce. Employer branding, social media recruitment, employee referral programs, and campus recruitment are key. Competition for qualified medical professionals is fierce, staff retention is difficult, and regulations need revising. The research found that structured onboarding, continuing skill development, and data-driven recruiting boost productivity and reduce hiring timelines.
RISK MANAGEMENT TECHNIQUES IN CAPITAL MARKETS AT ZEN MONEY
Risk management is essential for the long-term performance and stability of the capital market, which is characterized by market volatility, regulatory changes, and altering investor behavior. This paper examines the risk management strategies implemented by Zen Money to identify, quantify, and mitigate a variety of risks associated with their capital market operations. This paper analyzes Zen Money's approach to managing market, credit, liquidity, and operational risk, among other factors. The organization implements both conventional and innovative risk management strategies. In order to mitigate adverse risk and safeguard portfolio value, the investigation underscores the significance of asset allocation strategies, diversification, stop-loss procedures, and derivative instruments like options and futures. In addition, we assess the effectiveness of quantitative methods for risk assessment and decision-making, including scenario analysis, stress testing, and Value at Risk (VaR). Zen Money reinforces its risk management approach through regulatory compliance, internal controls, and technology-driven analytics.
PERSONALIZED PRODUCT RECOMMENDATION USING PERSONALITY PROFILING AND META-PATH DISCOVERY
This paper introduces a novel method for personalized product recommendations in heterogeneous information networks by combining meta-path discovery with personality assessment. Neuroticism, agreeableness, extraversion, conscientiousness, and openness are among the user personality characteristics that the proposed system employs to establish a more comprehensive comprehension of individual preferences and behavioral patterns. Unlike traditional recommendation algorithms, this approach employs meta-path-based links in conjunction with psychological insights to identify more profound semantic connections between people, objects, and their environment. By incorporating both the user's expressed preferences and their concealed personality traits, the framework enhances the precision of its recommendations. The testing results indicate that the methodology significantly improves recommendation relevance, diversity, and user satisfaction in comparison to conventional collaborative filtering and content-based methods. In conclusion, the research demonstrates that the successful development of next-generation intelligent recommendation systems can be achieved by combining sophisticated network analysis with psychological modeling.
PORTFOLIO MANAGEMENT STRATEGIES IN FINANCIAL SERVICES AT MOTILAL OSWAL FINANCIAL SERVICES
In conclusion, the financial success of public enterprises is a critical determinant of their efficacy, longevity, and contribution to the expansion of the nation's economy. Inefficient bureaucracy, low innovation rates, and budget overruns are common obstacles that companies in this sector encounter. However, with the appropriate strategic planning, these obstacles can be surmounted, and they can achieve significantly superior results. Public firms can achieve operational efficiency and profitability by concentrating on income diversification, stringent cost management, sound financial planning, and smart asset use. Investing in staff performance and training cultivates a productive and innovative work environment, while enhancing governance, transparency, and accountability secures the support of stakeholders.
MULTI-MODEL HYBRID MACHINE LEARNING APPROACH FOR IOT BOTNET ATTACK DETECTION
Internet of Things botnets are capable of launching DDoS attacks, breaching networks, and stealing data. Traditional detection methods and single-model machine learning are unable to detect intricate attack patterns due to the high-dimensionality, imbalance, and dynamic nature of network traffic data. This investigation enhances the precision and robustness of a multi-model hybrid machine learning IoT botnet detection system through the application of deep and supervised learning. Stacking and weighted voting are employed by SVM, LSM, and Random Forest to monitor temporal and geographical traffic. Performance and noise are enhanced through the selection of features and the subsequent preprocessing. Rival algorithms were surpassed by the model in the areas of precision, recall, accuracy, and false positives in large IoT datasets. A continuous detection of IoT botnets is achieved through a flexible hybrid architecture.
SMART CONTROL OF VOLTAGE SOURCE INVERTERS USING PWM TECHNIQUES
This project involves using advanced pulse width modulation (PWM) techniques to intelligently manage voltage source inverters (VSIs) to improve power electronic system efficiency and functionality. Voltage source inverters transform DC electricity into AC power for motor drives, renewable energy systems, and UPSs. The paper uses intelligent control strategies and pulse width modulation (PWM) techniques such adaptive modulation, space vector PWM, and sinusoidal PWM to reduce harmonic distortion, output voltage regulation, and switching losses. This paper examined how digital controllers and real-time control algorithms can keep inverters running smoothly under changing load and supply conditions. These methods improved energy conversion system efficiency and stability.
SMART HOUSE PRICE VALUATION USING AI-BASED PREDICTIVE TECHNIQUES
A variety of machine learning algorithms were implemented in our research to forecast the sale prices of properties. The ultimate selling price of a house is influenced by a variety of factors, including its size, neighborhood, age, construction style, number of bedrooms, garage space, and more. In this paper, we implement machine learning algorithms to construct a home prediction model. A prediction model is developed by employing machine learning techniques such as logistic regression, decision trees, support vector regression, and Lasso regression. The housing statistics of three thousand residences were examined. There are four models: Logistic Regression, SVM, Lasso Regression, and Decision Tree, each with an R-squared value of 0.98, 0.96, 0.81, and 0.99. These algorithms were evaluated using a variety of metrics, such as accuracy, RMSE, MSE, and MAE. This research serves as an additional illustration of the significance of our methodology and approach.
INCENTIVE PROGRAMS IN MANUFACTURING SECTORS AT TATA MOTORS
In industrial settings, incentive schemes boost staff retention and efficiency. Indian automaker Tata Motors Limited is well-known. This paper examines their reward systems' development, execution, and efficacy. Tata Motors uses monetary and non-monetary incentives to drive its workers to work harder and produce more. These include performance-based incentives, awards for outstanding work, professional growth, and organizational progression. These programs foster innovation, foster a culture of continuous improvement, and match employee goals with business goals. The research claims Tata Motors' incentive schemes have increased production productivity, staff attrition, and job satisfaction. The research shows that manufacturing companies need strategic reward programs to compete. It emphasizes the importance of combining intrinsic and financial motivations to sustain performance.
DESIGN OPTIMIZATION OF COMPOSITE PROPELLER BLADES FOR LIGHTWEIGHT AIRCRAFT
This investigation's primary goal is to improve the aerodynamic efficiency, structural integrity, and overall performance of composite propeller blades for light aircraft, all while minimizing their weight and fuel consumption. This study investigates the possible uses of modern composite materials, especially carbon fiber reinforced polymers, because of their exceptional strength, corrosion resistance, and longer lifespan after repeated usage. Computer programs and modeling tools are used to assess the blade's chord distribution, twist angle, airfoil shape, and fiber orientation in order to maximize performance in a range of working circumstances. The distribution of stress, vibration, power production, and airflow of propeller blades are examined using computational fluid dynamics (CFD) and finite element analysis (FEA). The improved composite blade design weighs less during manufacturing, has less structural deformation, lasts longer, and is more effective than conventional metal blades. This makes it perfect for the lightweight aircraft applications of today.
AN ENHANCED SECURE KEYWORD SEARCH FRAMEWORK FOR CONFIDENTIAL DATA SHARING OVER CLOUD ENVIRONMENTS
The price of computer hardware and programs has dropped significantly due to the increasing use of cloud infrastructure. Data encryption is a common practice prior to uploading files to the cloud in order to maintain data security. Encrypted data is more difficult to locate and transmit than unencrypted data. The onus is on the cloud provider to guarantee both the speed and security of search results and customer data. To circumvent these issues, we have developed CPAB-KSDS, an encryption approach for cloud data that utilizes ciphertext-policy attributes and incorporates keyword search and sharing capabilities. The proposed approach allows you to conduct both sending information based on characteristics and searching for terms based on characteristics simultaneously, which is an improvement over earlier systems that only allowed you to do one of the two. Additionally, if the PKG in the sharing area does not receive any interaction, the word associated with our theme may be modified. In this paper, we examine the CPAB-KSDS concept and the security model it stands for in greater detail. Also, we frequently propose a specific approach and demonstrate that it is secure against selected ciphertext and selected keyword attacks in the context of the random oracle model. When considering the features and functions of the proposed construction, it becomes clear that it is both practical and economical.
Copyright (c) 2025 Journal of Science and Technology Excellence https://creativecommons.org/licenses/by-nc-nd/4.0
THE ROLE OF EMPLOYER BRANDING IN ENHANCING HUMAN RESOURCE RECRUITMENT AND RETENTION
Employer branding's primary objective is to draw in and keep talented workers. Understanding the customs, preferences, and expectations of the company's current workforce helps achieve this. While remaining committed to its objectives, the company gave market expertise, genuine employee involvement, and human capital development top priority. Along with a dedication to sustaining profitability and effective human resource management, it placed a great emphasis on the creation of a corporate culture. We are now investigating more sources that might bolster Donald Decamp's claim that employer branding is more important than a company's image or Simon Barrow's view that employee recognition should be a part of the marketing brand. In order to determine whether the ideas are still applicable, we examine specialized literature as well as the approaches taken by teams operating in this field.
ENHANCING ORGANIZATIONAL LEARNING THROUGH EFFECTIVE KNOWLEDGE MANAGEMENT SYSTEMS
Organizational learning (OL) and knowledge management (KM) are two terms in today's business lexicon that strive to give companies an edge in the dynamic global market. By reviewing the literature and analyzing real-world examples, this paper delves deep into the topic of knowledge management's impact on organizational learning. Knowledge management and organizational learning's foundational principles are examined in the abstract's first section. When these principles are put into place, organizations will make it easier to generate, distribute, and evaluate data. To promote innovation, improve decision-making, and keep a competitive advantage, it stresses the importance of knowledge assets as basic resources. Knowledge management entails five distinct phases: production, acquisition, preservation, distribution, and application. This abstract delves deeply into each of these phases, analyzing the key approaches, tactics, and technological resources used at each. The article compares and contrasts various knowledge management systems and how they affect the efficiency and output of a business. Some examples of such structures are organizational memory systems, communities of practice, and knowledge repositories. Organizations must innovate, incorporate new information, and thrive in dynamic conditions; this is emphasized in the abstract, which also provides a detailed analysis of organizational learning. Different organizational learning approaches, such as single-loop and double-loop learning, are examined in the essay. In order to foster creativity and continuous improvement, the essay stresses the significance of establishing a learning culture. Topics pertaining to knowledge management and organizational learning are covered in the abstract. Data security, organizational change resistance, and the rapid deterioration of knowledge assets are urgent matters that require attention. The management of these challenges and the establishment of an environment that encourages learning and knowledge expansion are greatly affected by the company culture, leadership, and supporting infrastructure.
FORWARD Vs. FLYBACK CONVERTERS: A DETAILED INVESTIGATION FOR PERFORMANCE IMPROVEMENT
The efficiency, power factor, offset current, and core loss of both forward and flyback converters are investigated in detail in this paper. This research presents a comparison and contrast of the benefits and drawbacks of forward and fly-back layouts. Professionals are able to circumvent converter constraints by visually and aurally confirming performance metrics. The fast switching speeds of metal-oxide semiconductor field-effect transistors (MOSFETs) make them ideal for integrating the two designs. Included in this study is the merging of forward and flyback converter topologies. For optimal efficiency and power factor, use a single-stage balanced forward-flyback converter.
FIVE LEVEL INVERTER FOR RENEWABLE POWERGENERATIONSYSTEM
The usage of single-stage, single-phase inverters is on the rise among smaller photovoltaic (PV) systems due to regulations about green energy and shifts in the electricity market. Many people find that using transformers with three stages or more significantly increases their efficiency. The research concluded that photovoltaic (PV) systems should use inverters with one stage and five levels. The inverter's output current varies in response to changes in the photovoltaic (PV) grid voltage. The Sinusoidal Pulse Width Modulation (SPWM) architecture is the basis of this control method. Since SPWM simplifies the connection between the generator and the grid and eliminates the requirement for a phase-locked loop, it is finding increasing usage in a variety of technologies. The five-level inverter is demonstrated to function properly using Matlab models.
A ROBUST FRAMEWORK FOR MOTION BLUR DETECTION AND REMOVAL IN DIGITAL IMAGES
The majority of recent research in image processing has focused on correcting image distortions. The increasing relevance of image processing prompted the development of this novel concept. By employing these methods, haze can be located and eliminated. Among their many other capabilities is the ability to enhance and restore photos. Lens aberrations, out-of-focus blur, and moving subjects are a few of the many potential sources of blurry photographs. You can get all the information you need regarding recent advances in detecting and correcting motion distortion from this article. Using a Convolutional Neural Network and a Generative Adversarial Network (GAN), this study introduces a novel approach to detecting and repairing motion shift, hence reducing its impacts.
AN EFFICIENT GLITCH MITIGATION TECHNIQUE FOR LOW-POWER MULTIPLIER CIRCUITS
A couple of significant methods for processing digital signals that involve multiplication are filtering and the fast Fourier transforms (FFT). A common tool for achieving lightning-fast processing speeds is the parallel array multiplier. The majority of a digital signal processor's power goes into its multipliers, therefore finding ways to make them more efficient is a key component of developing low-power DSP systems. Constructing a full adder (FA) with minimal power consumption is a straightforward solution. Alterations done to the building can potentially compromise its strength. If required, omit sections that do not contain any missing goods. Several multipliers are constructed from a low-power, complete adder that uses ten transistors, as described in this article. In order to evaluate various instruments, these criteria are utilized. A fine-grained MTCMOS cell can prevent power leakage by utilizing power gating.
PRIVACY PROTECTION TECHNIQUES FOR WEB SEARCH PERSONALIZATION
The search engine returns more relevant results when you utilize personalized web search (PWS). Users' reluctance to divulge personal information while searching is a key factor in PWS's low adoption rate, according to the study. Our research primarily focuses on how Personalized Web Services (PWS) applications that leverage hierarchical user accounts reveal consumer preferences. A novel PWS platform, UPS, is the topic of this essay. By utilizing adaptive query-based approaches, this system can generalize user profiles while still honoring their privacy choices. One predictive metric considers the value of personalization, while the other considers the potential danger of revealing your profile; our runtime extension seeks a happy medium between the two. As an example, we demonstrate how to increase the amount of computations at runtime using greedy dynamic programming and integer linear programming techniques. Additionally, you can assess the potential value of question customization using our online tool. Extensive experiments have confirmed our method's efficacy time and time again. According to the research, GreedyIL outperforms Greedy DP.
A STUDY ON COST OF CAPITAL ESTIMATION IN MANUFACTURING FIRMS AT BHARAT FORGE
The cost of capital significantly influences a business's investment decisions, expansion strategies, and financial planning processes. Manufacturing businesses can enhance their financial strategy and boost the value of their shares by knowing the true cost of capital. This is because capital expenditures on equipment, software, and general company operations can add up quickly for manufacturers. A prominent player in India's automotive and industrial manufacturing industries, Bharat Forge is the focus of this article, which analyzes the company's use of debt and equity financing. It examines the process of calculating the WACC, the cost of debt, and the cost of equity by considering market circumstances, benchmarks, and business risks. This case study analyses Bharat Forge's market performance and financial statements to draw conclusions about the company's risk management, funding source selection, and return on investment (ROI) strategies for sustaining growth over the long term. Finding the correct cost of capital is more than simply a technical exercise, as the data demonstrate. It's a strategic tool that manufacturing organizations can use to stay competitive in an uncertain economic environment and come up with good ideas.