Decision Making: Applications in Management and Engineering
"Decision Making: Applications in Management and Engineering" is a scholarly journal that explores decision-making processes in management and engineering. It features research papers, case studies, and interdisciplinary discussions, providing insights into decision-making models, strategies, and their practical applications. The journal fosters dialogue between management and engineering disciplines and offers tools for improving decision-making efficacy. With its focus on both theory and practice, it serves as a valuable resource for academics, professionals, and students navigating decision-making in diverse contexts.
ROLE OF APP SECURITY AND PERSONALIZATION IN ENHANCING CONSUMER ENGAGEMENT IN RETAIL MOBILE APPLICATIONS
This paper discusses the impact of app security and personalization on consumer experience in retail mobile apps. The primary objective was to understand how the users influence the data protection and customized apps experience their interaction and their loyalty. The research design was quantitative cross-sectional research design sampled 420 respondents that were active users of the retail app and used a structured questionnaire that was administered online. The data were analysed in the SPSS (Version 28) and AMOS (Version 26). The descriptive statistics, the tests of reliability, validity, regression analysis, and Structural Equation Modeling (SEM) were used to evaluate the relationships between variables. The results revealed that the engagement of consumers was highly enhanced by the security of the app ( =0.42, p < 0.001) and the personalization ( = 0.46, p < 0.001). The model was also found to be reliable (Cronbachs Alpha was high (more than 0.85) and the good fit indices (CFI =0.958, RMSEA=0.046). The twofold significance of safe spaces and personal user experience can support the focus on the mobile retail app with the help of these outcomes. The study article applies to mobile marketing and design of applications to improve retention and satisfaction of the user.
App Security, Personalization, Consumer Engagement, Retail Applications, Structural Equation Modeling
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CONSUMER DECISION-MAKING DYNAMICS: THE IMPACT OF PACKAGING DESIGN ELEMENTS ON PURCHASE BEHAVIOR FOR ORGANIC CEREAL PRODUCTS IN DELHI NCR
The research problem addressed in this paper is the role of packaging design factors on consumer decision-making of organic cereals products in Delhi NCR. The main aims were to compare the impacts of visual features (color, picture, text), sustainability and informational indicators (nutritional assertions, certification logos, ingredient disclosure) on purchase intent, trust, and brand loyalty and compare demographic differences. The mixed-methods design was used, which involved administering structured questionnaires to 300 participants and conducting 15 interviews that were in-depth. Descriptive statistics, reliability analysis, correlation and multiple regression, ANOVA and factor analysis were used to analyze quantitative data and thematic analysis was used to extract qualitative information. It was found that the earthly colors and nature images greatly enhanced the purchase interest (mean score 4.2/5), and customers were ready to pay the average increase in price 17% because of sustainable packaging with transparent information. Regression and ANOVA showed that education and environmental consciousness are among the independent variables that influence purchase behavior; young consumers are more inclined to purchase using multi-sensory packaging. These observations indicate that visual attraction, eco-friendliness and transparency build consumer confidence, loyalty and purchase intentions.
Consumer behavior, Packaging design, Organic cereals, Sustainability, Purchase intent
IMPACT OF WORK-LIFE BALANCE ON FACULTY OUTCOMES AND INSTITUTIONAL PERFORMANCE: AN EMPIRICAL STUDY OF HIGHER EDUCATION INSTITUTIONS IN DELHI NCR
The current paper will evaluate the effect of work-life balance on the faculty outputs and institutional performance in higher education in Delhi NCR. The study explores the increasing issues regarding the stress of the faculty, disproportional job satisfaction, and the difference in the levels of organizational commitment within the academic context in the region. A cross-sectional research design was adopted with the help of structured questionnaire that was given to 412 faculty members in state and privated universities. The work-life balance, job satisfaction, and organizational commitment, and the perception of institutional effectiveness, were measured using standardized scales and analyzed with SPSS 29. The results showed a positive significant association, and the work-life balance strongly predicted job satisfaction (Beta = 0.63), organizational commitment (Beta = 0.57) and perceived institutional performance ( Beta = 0.48). Also, faculty with higher balance had 22 percent more job satisfaction scores and 17 percent higher commitment scores than those with lower balance. The results suggest that healthy work-life behaviors lead to the promotion of faculty well-being, institutional functioning, and sustainable academic performance. The paper makes the conclusion that supportive policies ought to be given priority in higher education institutions as a way of enhancing individual and organizational results.
work-life balance, job satisfaction, organizational commitment, faculty performance, higher education
Upasana Sharma, Dr. Bhagawan Chandra Sinha
CONNECTING THE SOUL TO STRATEGY: A STUDY ON SPIRITUALITY AND INNOVATION IN MODERN ORGANISATIONS
This research was organised to examine the association between various dimensions of workplace spirituality and innovative work behaviour, particularly in the corporate sector. the data were gathered with help of structured questionnaire from 400 corporate employees working on managerial level. The results made it clear that all the major workplace spirituality dimensions viz. meaningfulness, authenticity, embracing diversity, empathy, resilience, and gratitude, have a positive and statistically significant influence on innovative work behaviour. The findings identify that meaningfulness had the highest effect, indicating that employees’ perception of work as meaningful promotes their innovation. Additionally, embracing diversity, authenticity, and empathy also had significant effects. Although the effect of flexibility and gratitude was relatively small, it was still found to be helpful in enhancing innovation. The study also pointed out which can encourage the innovative work behaviour, the organization should create a spiritually rich work environment. The study also emphasized the importance of workplace spirituality in the corporate sector and presented measures to promote innovation through it.
Spirituality, Innovative Behaviour, HR Practices and Corporate Sector.
DYNAMIC REGULATORY CHANGE MANAGEMENT USING AI-POWERED REGULATORY KNOWLEDGE GRAPHS (RKGS)
In today’s dynamic business era, regulatory change is an ever-present risk; organisations are required to react to increasingly frequent legislative changes. This study investigates the transformative potential of AI-powered “Regulatory Knowledge Graphs” (RKGs) for the dynamic regulation management of high-risk sectors like Finance, Health, and Pharmaceuticals. This considers the limitations of conventional compliance systems and shows how AI and semantic technologies facilitate an automated, real-time compliance monitoring solution. The research includes examples of improved efficiency, risk reduction, and cost savings found in real-world case studies. The research adopts a mixed-methods secondary approach to inform compliance teams and make a contribution to the expanding RegTech research area. This closes by reflecting on future work by considering the scalability of the solution across sectors, integration within the compliance space, and AI-human hybrid compliance systems.
AI, Regulatory Knowledge Graphs, Compliance Automation, RegTech, Risk Management, Semantic Technologies, Knowledge Graphs, Digital Transformation
INTELLIGENT IOT ENABLED PIPELINE LEAKAGE DETECTION AND MONITORING SYSTEM USING DEEP METAHEURISTIC LEARNING ALGORITHM
Pipeline networks are the safest transportation of oil and gas-related products that may be susceptible to failures. These kinds of failures are due to the reason of manufacturing defects, degradation of the material, environmental defects, and interference of the third party. These issues are addressed through the Internet of Things (IoT) based solutions and give promising outcomes in predicting the failures and monitoring. The IoT can combine the sensor technologies to monitor the gas flow, pressure, temperature, condition of the compressor, concentration, and other related features inside the pipeline. The leaks in the pipe are measured as a pin-sized hole that is detected by the IoT sensor with advanced technologies such as deep learning. This paper proposed deep learning with a meta-heuristic approach-based model to detect leaks in pipelines through IoT sensors. The Deep Learning model called Deep Auto Encoder Neural network (DAENN) is the unsupervised model that can accurately classify the leaking and non-leaking pipeline conditions. The detection accuracy is further enhanced with the Bat Optimization algorithm (BOA) which obtained improved accuracy while leaks occur in the pipeline inside the sensor monitoring area. This observation deploys the monitoring sensors to cover the mentioned monitoring area. The proposed model can increase the system’s leak detection reliability and reduces the false alarm rate. The trained DAENN-BOA with its leak detection strategy is tested against a simulated dataset that secured promising results with an accuracy of 98.9% and a reduced error rate of 0.09.
Internet of Things (IoT), Oil pipeline, Leakage detection, Deep learning, Auto Encoder, Bat Optimization algorithm.
PREDICTION OF EV CHARGING PATTERNS USING HYBIRD MACHINE LEARNING ALGORITHMS
Electric cars are becoming increasingly popular in the transportation sector. Despite their growing acceptance, several factors still limit their widespread use. These factors include the relatively limited driving range of electric vehicles and the high costs related to battery production and maintenance. Understanding the energy usage of EVs becomes more important nowadays due to the rapid usage and emergence of EVs into the market. To address these challenges, researchers have turned to machine learning models to predict electric vehicle charging patterns accurately. Among these models, an ensemble learning approach has shown to outperform previous models significantly. This is evidenced by superior Symmetric Mean Absolute Percentage Error (SMAPE) scores, indicating more accurate and reliable forecasting of charging behaviours.
electric vehicle, charging behavior, transportation, SMAPE ensemble learning.
ESTIMATION OF ENERGY CONSUMPTION IN REAL-TIME EV SENSOR DATA THROUGH EXPLAINABLE AI AND MACHINE LEARNING ALGORITHM
Electric Vehicles (EVs) represent a transformative shift in transportation, promising reduced dependence on fossil fuels and lower emissions. EVs rely on sensors to gather vast amounts of real-time data on parameters such as speed, acceleration, battery charge, and environmental factors, which all play a crucial role in determining energy efficiency. This research focuses on real-time estimation of energy consumption using machine learning and explainable AI (XAI) to interpret sensor data effectively. While previous studies primarily focused on evaluating energy usage through traditional methods or basic machine learning algorithms, this work leverages advanced models like Random Forest and Neural Networks, trained on extensive real-time data from Battery Electric Vehicles (BEVs) across varied driving conditions. Additionally, the SHapley Additive exPlanations (SHAP) method is utilized to enhance model interpretability, offering insights into how different parameters—such as vehicle speed and battery current—affect energy consumption. This explainability not only enables more accurate predictions of energy use but also assists in identifying key factors influencing energy inefficiency in real-time scenarios. The proposed approach enhances prior work by improving prediction accuracy and adaptability through XAI, supporting more precise energy management strategies. Ultimately, this research contributes to optimizing EV performance, extending battery life, and reducing range anxiety, which are critical for accelerating EV adoption and guiding future policies on transportation electrification.
SHAP, Electric Vehicles, practical driving, random forest, energy efficiency
ROLE OF FINANCIAL INSTITUTIONS IN PROMOTING LOW-CARBON, CIRCULAR ECONOMY
The transition to a circular economy presents a transformative opportunity for sustainable development, yet it requires substantial financial support to realize its full potential. Financial institutions play a pivotal role in fostering this shift by providing the necessary capital and innovative financial solutions that drive the circular economy's growth. These institutions can support the circular economy in several key ways. This paper focusses on the role of financial institutions to promote resource efficiency, waste reduction, and sustainable practices. It discusses the innovative products offered by the financial institutions and circular economy initiatives, to help bridge the funding gap for companies transitioning to circular models. Furthermore, banks and investors can facilitate the scaling of circular economy solutions through strategic partnerships and joint ventures, providing both capital and expertise to innovative projects. Additionally, government regulatory bodies play crucial role, to help businesses navigate the transition. They can also support research and development in circular technologies and business models by funding relevant innovations and pilot programs. Thus the paper uses discourse analysis to study how the financial institutions are crucial enablers of the circular economy. Their ability to provide tailored financing, integrate ESG criteria, and support innovation and education is essential for advancing sustainable practices and achieving long-term economic and environmental benefits. Through these efforts, they not only contribute to the transition towards a circular economy but also enhance their own sustainability credentials and market opportunities.
Dr. Namrata Acharya, Dr. Vinay Gudi, Dr. Pankaj Natu, Dr. Suhas Dande
CULINARY TOURISM AND LOCAL FOOD HERITAGE IN MUMBAI: TOURIST PREFERENCES, RESTAURANT STRATEGIES, AND ECONOMIC IMPLICATIONS
This paper discusses how culinary tourism and regional food culture in Mumbai intersect through the prism of tourist preferences, the activities of restaurants, and the economic outcomes. The main aim was to determine what tourists like about food and what they dislike about food, their interest in Maharashtrian food, whether the restaurants in Maharashtra serve regional foods in the menus or not, and whether the restaurants are facing any challenges in promoting local food. The research was based on a mixed-methods approach, where data was collected on 200 tourists, addressing them through a survey, 15 restaurant managers in interviews, and content analysis of 50 restaurant menus. Quantitative results were achieved by applying SPSS v27 based on chi-squares, t-tests, ANOVA, correlation, and regression data interpretation whereas the qualitative results were explained using NVivo 14 through thematic analysis. There also were results that 70 percent of tourists liked Indian vegetarian food and local cuisine enthusiasm was strong (mean score = 7.6). Menus only offered 34 percent dishes of Maharashtrian cuisine and 60 percent of restaurants considered inadequate coaching of chefs as a key obstacle. The economic potential of local food promotion was also indicated by the fact that tourist enthusiasm was a large predictor of repeat visitation ( β = 0.58, R² = 0.37, p < 0.01).
Culinary tourism, Maharashtrian cuisine, tourist preferences, local food heritage, Mumbai restaurants
PERFORMANCE COMPARISON OF RELAY NODE RANDOM SELECTION METHOD USING DIFFERENT SET OF PARAMETER FOR MANET
Treads of today research uncertainty environment and natural situation if focussed in Mobile Ad hoc Networks, a big challenge to develop routing protocol that can meet different application needs and optimize routing paths according to the topology change in mobile ad hoc networks [1], [2]. The continuous transmission of small packet is called beacon packet, that advertises the presence of a base station and the mobile units sense the beacons and attempt to establish a wireless connection [3]. This research aims to propose CH-RNSR with hybrid cryptography (ECC) using RNSR algorithm. The main aim of the proposed research CH-RNSR with ECC algorithm is to increase the remaining energy with the number of malicious nodes detected during the communication via acknowledgement base than RNSR with help of one of leading simulation model called Network Simulator 2.34 work with different set of nodes, malicious nodes in same topology size using various parameters such as packet delivery ratio, throughput, routing overhead, packet loss, delay and remaining energy via Network Simulator 2 (NS2).
MANET, attack, Energy Models, cryptography, NS2
BALANCING SUSTAINABILITY AND COST : A REVIEW OF GREEN LOGISTICS IN ENGINEERING AND MANAGEMENT
This review explores the problematic relationship between sustainability and fee inside the context of green logistics within engineering and control. As industries international more and more understand the want for sustainable practices, the task lies in integrating green logistics techniques even as preserving price-effectiveness. Green logistics makes a speciality of decreasing the environmental effect of logistics activities through techniques which include strength-green transportation, waste discount, and optimized deliver chain management. However, imposing these techniques frequently entails preliminary funding expenses and operational modifications, elevating issues about their monetary feasibility. This paper discusses diverse processes to balancing sustainability and fee in logistics, together with innovative technology, policy frameworks, and high-quality practices in inexperienced supply chain control. The assessment similarly highlights case studies from distinct industries wherein green logistics practices have correctly been incorporated, reaching a harmonious balance between environmental duty and profitability. By synthesizing key findings from the prevailing literature, this paper gives valuable insights for corporations and policymakers to navigate the demanding situations of adopting sustainable logistics practices without compromising financial overall performance.
Green logistics, sustainability, fee efficiency, deliver chain management, environmental impact, power-green transportation, waste reduction, eco-friendly practices
HARNESSING ARTIFICIAL INTELLIGENCE AND AUTOMATION IN DIGITAL MARKETING: REVOLUTIONIZING CONSUMER ENGAGEMENT AND BUSINESS STRATEGIES
The integration of Artificial Intelligence (AI) and Automation in Digital Marketing is revolutionizing Consumer Engagement and Business Strategies, offering groups with the equipment to deliver surprisingly personalised and powerful marketing campaigns. AI technologies, together with Machine Learning and Predictive Analytics, enable organizations to investigate enormous quantities of client statistics, uncover styles in conduct, and create tailored reviews that resonate with person clients. This level of personalization now not most effective enhances client pride but also will increase engagement and conversion charges. Automation plays a essential position in streamlining repetitive tasks, optimizing workflows, and making sure well timed content shipping across more than one channels. By automating methods, groups can attention on strategic selection-making while keeping excessive levels of operational efficiency. The combination of AI and Automation allows organizations to make statistics-driven choices that refine advertising and marketing strategies, decorate purchaser interactions, and improve average performance. These improvements also lead to price-effective solutions, reducing human error and improving productiveness. As the virtual landscape continues to conform, organizations that harness the power of AI and Automation in Digital Marketing may be higher positioned to stay competitive, adapt to converting customer expectations, and force long-term boom and achievement.
Artificial Intelligence, Automation, Digital Marketing, Consumer Engagement, Business Strategies, Machine Learning, Predictive Analytics, Personalization.
Dr. Madhura Raghava Reddy, Dr. PC Reddy, Dr. G. Nanda Kishor Kumar, Sudheer Konduru
THE CREATION OF A DIESEL ENGINE USING GENETIC ALGORITHM PREDICTION MODELS AND MACHINE LEARNING TO VERIFY FUEL CONSUMPTION, EMISSIONS, AND HEAT TRANSFER
The advancement of diesel engine design has become increasingly reliant on computational techniques to optimize performance while minimizing fuel consumption, emissions, and heat transfer losses. This research explores the integration of genetic algorithm (GA) prediction models and machine learning (ML) techniques to enhance diesel engine efficiency. The study employs GA to generate optimal engine configurations by simulating various design parameters, selecting the most efficient solutions based on performance criteria. Machine learning models are then trained on experimental and simulated data to validate the accuracy of fuel consumption, emission levels, and thermal characteristics. The combined approach enables real-time optimization by iteratively refining engine parameters through adaptive learning. By leveraging data-driven methodologies, this study provides a novel framework for designing high-efficiency diesel engines with lower environmental impact. The results demonstrate that ML-assisted GA optimization significantly improves fuel economy and emission compliance while enhancing thermal performance. This research contributes to the on-going efforts in sustainable engine development, offering a predictive and adaptable design methodology for future diesel engines. Traditional methods rely on empirical correlations or computational fluid dynamics (CFD) simulations, which are computationally expensive and time-consuming. This thesis explores the application of machine learning (ML) techniques to predict and analyze heat transfer in IC engines. By leveraging historical engine performance data and modern ML algorithms, we aim to provide an efficient, accurate, and scalable solution for IC engine heat transfer analysis. The findings demonstrate the potential of ML approaches to enhance the efficiency of engine development cycles.
Abstract—Electric cars are becoming increasingly popular in the transportation sector. Despite their growing acceptance, several factors still limit their widespread use. These factors include the relatively limited driving range of electric vehicles and the high costs related to battery production and maintenance. Understanding the energy usage of EVs becomes more important nowadays due to the rapid usage and emergence of EVs into the market. To address these challenges, researchers have turned to machine learning models to predict electric vehicle charging patterns accurately. Among these models, an ensemble learning approach has shown to outperform previous models significantly. This is evidenced by superior Symmetric Mean Absolute Percentage Error (SMAPE) scores, indicating more accurate and reliable forecasting of charging behaviours.
ENHANCING IOT DEVICE COMMUNICATION PROTOCOLS WITH AI AND MACHINE LEARNING
The evolution of wi-fi networks has brought about substantial improvements in supplying new services to users via progressive community and tool technologies. The upcoming deployment of 5G community structures is ready to permit large connectivity, excessive throughput, low latency, superior electricity efficiency, and stepped forward security. With a focus on supporting a extensive range of IoT programs, including smart healthcare, agriculture, industrial manufacturing, shrewd visitors management, and strength structures, 5G is laying the inspiration for the next wave of wireless era. However, the growing wide variety of IoT gadgets has sparked studies into past-5G and 6G structures, aiming to offer greater bandwidth and better-nice service for larger networks. Artificial intelligence (AI) and system studying (ML) are emerging as vital technologies to optimize IoT network operations. This paper explores the role of AI/ML algorithms in developing energy-green, secure, and scalable IoT networks. It especially addresses key challenges in the layout of future IoT verbal exchange structures, highlighting applications in smart healthcare, agriculture, transportation, smart grids, and commercial automation. The paper concludes through discussing destiny research instructions for integrating AI/ML in overcoming the challenges of next-generation IoT networks.
Ravindra Changala, Dr.Yeligeti Raju, Vikneshkumar.D, D.Sailaja, Baby.V
Kavi Priya.G, C.Maheswari, G.Divya, M.Devendran, Saveetha R
Abstract— Electric Vehicles (EVs) represent a transformative shift in transportation, promising reduced dependence on fossil fuels and lower emissions. EVs rely on sensors to gather vast amounts of real-time data on parameters such as speed, acceleration, battery charge, and environmental factors, which all play a crucial role in determining energy efficiency. This research focuses on real-time estimation of energy consumption using machine learning and explainable AI (XAI) to interpret sensor data effectively. While previous studies primarily focused on evaluating energy usage through traditional methods or basic machine learning algorithms, this work leverages advanced models like Random Forest and Neural Networks, trained on extensive real-time data from Battery Electric Vehicles (BEVs) across varied driving conditions. Additionally, the SHapley Additive exPlanations (SHAP) method is utilized to enhance model interpretability, offering insights into how different parameters—such as vehicle speed and battery current—affect energy consumption. This explainability not only enables more accurate predictions of energy use but also assists in identifying key factors influencing energy inefficiency in real-time scenarios. The proposed approach enhances prior work by improving prediction accuracy and adaptability through XAI, supporting more precise energy management strategies. Ultimately, this research contributes to optimizing EV performance, extending battery life, and reducing range anxiety, which are critical for accelerating EV adoption and guiding future policies on transportation electrification.
A DATA-DRIVEN APPROACH TO CUSTOMER SEGMENTATION: APPLYING CLUSTERING ALGORITHMS FOR STRATEGIC MARKETING INSIGHTS
This research explores the application of clustering algorithms to beautify client segmentation for strategic advertising and marketing purposes. Traditional segmentation strategies regularly fail to capture the complexity of cutting-edge client behavior, necessitating greater robust, information-driven strategies. By leveraging clustering techniques including K-means and hierarchical clustering, this have a look at identifies awesome customer organizations based on behavioral and demographic information. The method involves comprehensive information preprocessing, set of rules implementation, and evaluation of segmentation outcomes the usage of applicable metrics. Key findings highlight the effectiveness of clustering in revealing actionable insights, permitting centered advertising techniques and stepped forward client engagement. This study additionally discusses the challenges in model scalability, the importance of function selection, and the position of visualization in deciphering segmentation results. It concludes with tips for integrating clustering-based totally insights into patron relationship control (CRM) structures, offering a sensible framework for entrepreneurs. Future studies directions consist of exploring hybrid clustering strategies and assessing the integration of actual-time facts for adaptive segmentation.
Dr. Saravanan . P, Dr. Shenbagaraman . V. M, Dr. Arunfred . N
OPTIMIZING THE PLACEMENT OF ELECTRIC VEHICLE CHARGING STATIONS WITH RENEWABLE ENERGY INTEGRATION USING PARTICLE SWARM OPTIMIZATION
The deployment of electric vehicle (EV) charging stations (EVCS) performs a crucial role in assisting the increase of electrical mobility. This observe addresses the project of strategically setting EVCS to maximise performance and sustainability by way of incorporating renewable strength sources. We suggest a Particle Swarm Optimization (PSO) method to decide ultimate locations for EVCS, considering factors along with strength demand, spatial distribution, and renewable energy potential (e.G., sun and wind). The PSO set of rules successfully navigates the solution area to stability accessibility, price, and environmental advantages, presenting a robust framework for integrating renewable power into EV charging infrastructure. Our consequences indicate that PSO extensively complements the placement method as compared to standard techniques. By optimizing using renewable electricity, the proposed technique now not best improves the environmental effect of EVCS but also reduces dependency on non-renewable energy sources. This optimization framework provides precious insights for urban planners and policymakers, facilitating the development of sustainable and green EV charging networks that align with environmental dreams and guide the transition to easy electricity.
AES ON MODBUS ASCII TO PREVENT AGAINST ACTIVE ATTACKS
The integration of industrial control systems with standard network infrastructure has amplified their vulnerability to cyberattacks, thereby underscoring the urgent need for robust security mechanisms. Modbus, a widely adopted communication protocol in industrial automation, is particularly susceptible to various cyber threats due to its inherent lack of security features. This research delves into the application of Advanced Encryption Standard on Modbus ASCII to fortify industrial control systems against evolving cyber threats, especially active attacks. Active attacks targeting Modbus networks can have dire consequences, ranging from financial losses for control system operators to safety concerns for the public who rely on these services. In light of the escalating number and complexity of cybersecurity incidents and threats, it has become crucial to analyze the cybersecurity of Modbus/TCP and to develop cryptography-based solutions. This study aims to investigate the effectiveness of AES encryption in mitigating these risks, focusing on its implementation within the Modbus ASCII framework. Man-in-the-middle attacks, intercepted information replay, and abnormal byte transferring are just a few of the many potential attacks that SCADA communication may face.
Dr Sameer S Nagtilak, Mr Tanaji B Patil, Mrs Archana S Shinde- Sawant, Dr G V Otari
Machine Learning Techniques for Fraud Detection in Financial Transactions
Monetary misrepresentation presents increasingly more danger that has serious outcomes in the monetary area. Therefore, monetary establishments are compelled to further develop their extortion recognition frameworks persistently. Lately, a few investigations have utilized AI and information mining procedures to give answers for this issue. In this paper, we propose a condition of craftsmanship on different misrepresentation methods, as well as identification and counteraction strategies proposed in the writing like characterization, bunching, and relapse. The point of this study is to recognize the strategies and techniques that give the best outcomes that have been consummated up to this point.
RISK-BASED DECISION-MAKING STRATEGIES FOR ENHANCING GRID RESILIENCE IN SMART POWER SYSTEMS
Pre-catastrophe safety techniques are essential for enhancing the resilience of electric strength structures in opposition to natural disasters, particularly given the dependency of other infrastructure systems on strength. This paper develops risk-impartial and threat management models to optimize pre-disaster safety techniques, thinking about budget constraints, disaster uncertainty, and device interdependencies. The hazard-impartial version maximizes the anticipated fee of resilience (EVR), while the threat control version additionally consists of threat minimization metrics. A case observe on energy infrastructure within the Greater Toronto Area validates the fashions, revealing that: (i) growing budgets improves EVR, however beyond a threshold, incremental profits lessen; (ii) reducing disadvantage chance can lead to better EVR, with a non-linear trade-off among the two goals; and (iii) price range choices have to balance catastrophe depth and protection costs. Furthermore, the paper examines historic transmission outage statistics from the North American Electric Reliability Corporation (NERC) and suggests that figuring out clusters of nearby outages may want to improve operator reaction instances, enhancing grid resilience beneath N-1 security. The paper also surveys existing methodologies for enhancing electricity grid resilience in opposition to extreme climate occasions, highlighting gaps in present day answers and offering destiny research guidelines. Finally, a novel technique integrating asset management with grid resilience, together with the Value of Lost Load (VoLL), is proposed, demonstrating its ability thru a North American application case take a look at for knowledgeable upkeep strategies.
Rajulapati Sudha, B. Madhuri, K Srikanth
COMPUTATIONAL INTELLIGENCE-BASED ASSESSMENT AND IMPROVEMENT OF VOLTAGE STABILITY
As the global demand for electricity rises and the integration of renewable resources will become extra accepted, making sure the stableness, manipulate, and protection of power systems has by no means been greater important. Traditional methods are an increasing number of insufficient for coping with the complex dynamics of contemporary power grids. This evaluation presents a complete examination of the function of synthetic intelligence (AI) and related clever techniques in addressing those demanding situations. It explores the software of AI, deep mastering (DL), device studying (ML), and metaheuristic optimization algorithms in improving energy machine balance and reliability. The evaluate additionally highlights the usage of fuzzy good judgment for decision-making under uncertainty, reinforcement studying for adaptive balance manage, and version predictive manage for proactive device management. Emphasis is positioned on the combination of Internet of Things (IoT) and massive information analytics for actual-time monitoring and optimization. Through case studies and giant literature assessment, the paper demonstrates the effectiveness of those advanced strategies in predictive upkeep, fault detection, and actual-time manipulate. The review identifies modern limitations and proposes future studies instructions, stressing the need for robust, scalable, and flexible clever systems. This paper is a valuable useful resource for researchers, engineers, and policymakers, presenting an in depth expertise of the ability and future instructions of wise strategies in strength system.
Amit Baban Kasar Dr. Rahul Budania Dr. Avinash Sarwade
IMPACT OF LEISURE TOURISM ON WEEKEND REFRESHMENT: A REVIEW ON BANGALORE CITY EMPLOYEES PERCEPTION
Bangalore, also known as Bengaluru, is a significant hub for leisure tourism in India. In recent years, the city has seen a consistent influx of tourists, both domestic and international. As per the Ministry of Tourism's latest reports, Bangalore attracts a substantial number of visitors due to its vibrant culture, historical landmarks, and modern attractions. In this, the study of review is based on the employees perception on how much impactful after experiencing the leisure time visiting places in Bangalore and nearby areas. This is an attempt to find the effectiveness of Leisure tourism on employee refreshment from their busy tasks in workplace. This study is going to give idea on how much impact the leisure tourism for employees for their refreshments and proving needful suggestions to the employees for their planning and executions.
Dr. S.B. Anil Kumar Dr. M. Krishna Naidu, Dr. D. Balaji
COMPARATIVE EVALUATION OF COPPER (II) OXIDE AND ZINC OXIDE NANOPARTICLES FOR DIABETIC WOUND HEALING APPLICATIONS
Diabetic wounds, present significant challenges due to their slow healing and high infection risk. This study investigates the therapeutic potential of Copper (II) Oxide (CuO) and Zinc Oxide (ZnO) nanoparticles in enhancing diabetic wound healing. We characterized the physicochemical properties of CuO and ZnO nanoparticles using Dynamic Light Scattering (DLS), scanning electron microscopy (SEM), X-ray diffraction (XRD), and Thermogravimetric Analysis (TGA). In addition to these characterizations, we conducted a series of in vitro studies, including cytotoxicity assays, cell proliferation, and migration assays, antioxidant studies through DPPH and Reactive Oxygen Species (ROS) scavenging assays, and angiogenesis evaluations using the chick chorioallantoic membrane (CAM) assay. These studies revealed a solid potential for mitigating oxidative stress and promoting cellular activities crucial for wound repair. The antibacterial efficacy was assessed by measuring the zone of inhibition, demonstrating effective control over E. coli and S. aureus. Anti-inflammatory effects were examined by analyzing levels of inflammatory markers such as TNF-Alpha and interleukins (IL-10), indicating reduced chronic inflammation. The wound healing potential was specifically assessed using NIH 3T3 cell lines in scratch wound healing assays to ensure consistent cell migration and proliferation evaluation. Our results showed that both CuO and ZnO nanoparticles significantly enhance the rate of wound closure. The results suggest that CuO and ZnO nanoparticles significantly benefit diabetic wound healing by promoting cellular activities and controlling infection and inflammation. Further optimization of formulation is needed to enhance efficacy and patient comfort. This study highlights the potential of these nanoparticles in advancing therapeutic approaches for diabetic wounds.
Subhasri Bogadi, Veera Venkata Satyanarayana Reddy Karri, Gowthamarajan Kuppusamy, Vetriselvan Subramaniyan
DIGITAL TRANSFORMATION AT SBI: ADDRESSING CHALLENGES IN TECHNOLOGY-DRIVEN SERVICES
The present study examines into the challenges encountered by the State Bank of India (SBI) in implementing technology-driven banking services, focusing particularly on customer perceptions. SBI, as one of India’s leading public sector banks, has made considerable strides in digital transformation, aiming to enhance customer experience, improve efficiency, and ensure seamless service delivery. However, the journey toward digitalization has not been without obstacles. Through a comprehensive customer survey, the study identifies a range of issues that customers face, including network slowdowns, connectivity problems, high transaction costs, payment gateway issues, limited service accessibility, and restrictions on transaction types and deposits. The study employs factor analysis to categorize these challenges into two major groups. The first group, labelled as bank-related issues, includes concerns such as payment gateway malfunctions, limited access time, and restrictions on certain transactions and deposits. The second group, referred to as network-related issues, encompasses network slowdown and connectivity challenges that impact service accessibility and reliability. A deeper analysis revealed significant gender-based differences in customer perceptions, particularly in response to network slowdown and high transaction costs. Interestingly, while gender appeared to influence views on these factors, it had no significant effect on perceptions related to connectivity problems, payment gateway issues, access limitations, or restrictions on deposits and transactions.
EXAMINING CROSS CHANNEL MARKETING STRATEGIES: A COMPARATIVE OF OMNI VS MULTI-CHANNEL APPROACHES
This paper examines the effectiveness of move-channel advertising and marketing strategies, focusing at the comparative advantages of omnichannel versus multichannel techniques. As businesses increasingly leverage various digital and traditional channels to interact purchasers, data those techniques' nuances is crucial for optimizing customer enjoy and maximizing go back on investment. The have a examine analyzes the important issue tendencies, blessings, and worrying situations of every strategies, using case research and quantitative statistics to demonstrate their results on purchaser engagement and conversion expenses. The findings imply that at the equal time as multichannel strategies provide broader attain, omnichannel processes foster deeper customer relationships thru seamless interactions. Ultimately, this research contributes to the continuing discourse on included marketing techniques, providing insights for entrepreneurs searching for to enhance their cross-channel efforts in an evolving panorama.
Ms. Radha T, Dr. Sasmita Panda, Dr. J. Merlin Sheela Magdaline
Copyright (c) 2024 Decision Making: Applications in Management and Engineering
HEAT STROKE PREDICTION USING FUSED MACHINE LEARNING WITH CASCADED LEVY FLIGHT OPTIMIZATION
Heat stroke is a severe condition resulting from prolonged exposure to high temperatures, posing significant health risks, particularly during extreme weather events. Accurate prediction of heat stroke is challenging due to the complex interplay of environmental and physiological factors. This study proposes a comprehensive machine learning framework to address this challenge effectively. We begin by ensuring data quality through normalization using a Reorder Iterative Imputer, which handles missing values and outliers with precision. Feature selection is then performed using a combination of Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor embedding (t-SNE) to identify the most relevant variables from a diverse set of indicators. The classification task is executed using a fused machine learning approach, integrating Random Forest, Support Vector Classifier, and Gradient Boosting methods to enhance prediction accuracy. Further, prediction optimization is achieved using a Cascaded Levy Flight Optimization algorithm, fine-tuning model parameters for superior performance. The proposed method demonstrates significant improvements in prediction accuracy and reliability over traditional approaches, offering a valuable tool for early intervention and preventive measures in health monitoring and safety management.