Effects of Macro Economic Indicators on Foreign Portfolio Investments
Foreign Portfolio Investment; Gross Domestic Product; Monetary Policy; National Stock Exchange Volume; the VECM (Vector Error Correction) Model
In this study, both institutional and retail investors were observed making exits and entries based on macroeconomic data, utilizing measurable indicators such as GDP, inflation, bank rates, foreign exchange rates, trade volume on the national stock exchange, and portfolio investments. Employing a Vector Error Correction Model (VECM) in an econometric analysis, the study found a significant association between macroeconomic indicators and portfolio investments in India. Investors followed a discernible pattern of entering and exiting markets, with economic growth fostering greater investments. Notably, GDP, NSE Volume, and bank rates were identified as variables impacting foreign portfolio investments. In the long run, GDP positively affected foreign portfolio investments, while inflation and foreign exchange rates exhibited a detrimental influence, leading to decreased portfolio investments. Foreign Institutional Investors, prioritizing profits over business operations, focused on market sentiments, directing investments towards economies with potential performance and resulting in a higher volume of capital inflow. Overall, the study concludes that a robust economic condition attracts superior foreign portfolio investments. 2024 IEEE.
Patil A.; Lakshmi R.B.; Niranjan L.R.; Vas J.
Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICONSTEM60960.2024.10568614 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198841367&doi=10.1109%2fICONSTEM60960.2024.10568614&partnerID=40&md5=a4db7b165063fbdba1ba75ad2609c45e
Patil A., School of Business & Management, Christ University, Bangalore, India; Lakshmi R.B., School of Business & Management, Christ University, Bangalore, India; Niranjan L.R., School of Business & Management, Christ University, Bangalore, India; Vas J., School of Business & Management, Christ University, Bangalore, India
An Investigation on the Mechanical and Durability Properties of Concrete Structures Incorporated with Steel Slag Industrial Waste
Concrete; Durability; Mechanical strength; Steel slag
The construction sector constantly looks for novel approaches to promote sustainability, minimize environmental impact and improve structural properties of construction materials. This work explores the incorporation of steel slag, a by-product from steel manufacturing industry, into concrete blocks. This research investigates the effects of steel slag on the mechanical strength and durability of the prepared concrete blocks, through a series of laboratory tests, including compressive, tension, flexure strength, water absorption and acid attack. This study evaluates the viability and feasibility of incorporating steel slag into concrete block production. In this study, samples of concrete mixture were set with 0% to 20% insteps of 5% steel slag as coarse aggregate. The findings show that concrete blocks consisting 20% of steel slag exhibited better compressive, tensile, flexural strength, reduction in water absorption and improved resistance to chemicals. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Mechanical Engineering, pp. 21-30.
Springer Science and Business Media Deutschland GmbH
Nirmala L., K S Institute of Technology, Karnataka, Bangalore, 560109, India; Tejaswini M.L., K S Institute of Technology, Karnataka, Bangalore, 560109, India; Shilpa M.L., Christ University, Karnataka, Bangalore, 560029, India
Digital Soil Texture Classification Using Machine Learning Approaches
Feature extraction; Image processing; Neural network; Soil texture classification; Support vector machines
The texture of the soil is an important factor to consider during cultivation. The water transmission property is being regulated by the texture of the soil. To determine sand, silt and clays percentage present in a soil sample, a conventional laboratory method is used, which consumes more time. Digitization in agriculture has given a new direction of innovative research in agriculture domain. In this paper, based on image processing an efficient model has been developed for soil texture classification. Eight different image preprocessing techniques were used for the image enhancement. Out of that, the linear contrast adjustment found to be best in image enhancement. A feature vector was calculated by extracting six different features from the enhanced image. The feature vector of an image is input to the machine learning classifier. The various classifiers used in this research work are SVM, KNN, ANN and PNN. The accuracy of the classifiers was SVM (0.98), KNN (0.89), ANN (0.89) and PNN (0.86). From the result, it is found SVM model has higher rate in classification of soil. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-1, pp. 133-144.
Sharmila G., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Rajamohan K., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India
Analysis of Social Media Marketing Impact on Customer Behaviour using AI & Machine Learning
Artificial Intelligence (AI); Brand sentiment; Customer behavior analysis; Machine Learning (ML); Marketing strategies; Multichannel marketing; Personalization; Social media marketing; temporal dynamics
The study of client behaviour has been revolutionized by the combination of social media marketing with cutting-edge technology like Artificial Intelligence (AI) and Machine Learning (ML) in today's age of digital transformation. This study delves into the complex interplay between AI/ML, consumer involvement, and social media marketing methods. Our research exposes crucial insights via careful data collecting, sentiment analysis, and the construction of prediction models. By stressing the importance of catering content to individual interests, AI-driven customization emerges as a potent tool, increasing user engagement by 18%. Analysis of online sentiment shows how important it is to keep people feeling good about a business; postings with positive feelings get 30% more likes and comments on average. Accurate and time-saving insights from machine learning models provide up new avenues for optimizing marketing's use of available resources. As a result of the study's conclusions, companies will be able to better connect with their customers, use their resources more efficiently, and behave ethically moving forward. Promising new developments in the subject include the next steps, which include sophisticated AI models, temporal dynamics analysis, and investigation of long-term consequences, ethical issues, and multichannel techniques. This study helps companies, marketers, and policymakers better understand the convergence of technology and marketing in today's ever-changing digital world so that they may better serve their customers and build a successful brand over time. 2024 IEEE.
Thamaraiselvi P.; Masih J.; Giri P.; Sridevi J.; Karim Shaikh I.A.; Prasad M.V.R.
https://doi.org/10.1109/ICONSTEM60960.2024.10568805 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198835918&doi=10.1109%2fICONSTEM60960.2024.10568805&partnerID=40&md5=4bbf55d0e24bfaa5974eb99e180b62c8
Thamaraiselvi P., Business School (MBA), Kumaraguru College of Technology, Tamilnadu, Coimbatore, India; Masih J., School of Management, Bml Munjal University, Haryana, Gurugram, India; Giri P., School of Business And Management, Christ (Deemed To Be University), Delhi NCR, India; Sridevi J., Dr. MG. R. Educational & Research Institute, Management Studies & Dean-Centre of Excellence, India; Karim Shaikh I.A., Faculty of Business, Bahrain Polytechnic, Bahrain; Prasad M.V.R., Gitam School of Business, Gitam (Deemed To Be University), Bangalore, Doddaballapur taluk, Bengaluru, Nagadenahalli, India
The Role of IoT in Revolutionizing Payment Systems and Digital Transactions in Finance
Digital transactions; Finance; Financial inclusion; IoT; Payment systems; Security; Technological innovation
The revolutionary impact of the Internet of Things (IoT) on payment systems and digital transactions within the financial industry is investigated so as to better understand its implications. During this period of unparalleled digitalization in the financial environment, the Internet of Things has emerged as a crucial participant in the process of altering traditional payment paradigms. For the purpose of improving efficiency, security, and the overall user experience, this article analyzes the incorporation of Internet of Things (IoT) devices into financial transactions. These devices include smart cards, wearables, and linked appliances. The paper elucidates how Internet of Things-driven innovations are expediting payment processes, reducing transaction costs, and mitigating fraud risks. This is accomplished through a comprehensive investigation of case Researches, technology breakthroughs, and regulatory frameworks. In addition to this, the article investigates the implications of the Internet of Things (IoT) in terms of promoting financial inclusion by providing digital payment services to groups that were previously underserved. This research gives useful insights for policymakers, financial institutions, and technologists who are looking to navigate and harness the potential of the Internet of Things in transforming payment systems. These insights are gained through an examination of the obstacles and opportunities related with the adoption of IoT in the financial sector. 2024 IEEE.
Thirumagal P.G.; Das T.; Das S.; Khatri E.; Kiran S.; Rani S.
5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 171-176.
Thirumagal P.G., VISTAS, Department of MBA, Tamil Nadu, Chennai, India; Das T., Christ University, Department of School of Business Management, Delhi NCR Campus, UP, Ghaziabad, India; Das S., Christ University, Department of School of Commerce, Finance & Accountancy, Delhi NCR Campus, UP, Ghaziabad, India; Khatri E., Nitte Meenakshi Institute of Technology, Department of Management Studies, Karnataka, Bengaluru, India; Kiran S., CMR Institute of Technology, Department of MBA, Karnataka, Bengaluru, India; Rani S., Kalasalingam Academy of Research and Education, Department of Commerce, Tamil Nadu, Sriviliputtur, India
Beyond Automation: Understanding the Transformational Capabilities of AI in Management
AI; augmentation; automation; management
The investigation explores at the various ways that artificial intelligence (AI) is affecting management techniques. The study highlights the dichotomy between automation and augmentation, highlighting how artificial intelligence (AI) can replace human work through automation, but its ultimate use in augmenting human capabilities (augmentation) leads to better organisational performance. This analysis reveals how AI-driven tactics enhance operational efficiency, decision-making, and productivity by synthesising research findings from a variety of domains, including manufacturing, banking, municipal sectors, and remote work environments. It also looks at how AI may change management through big data and data analytics, recommending a shift to an integrated strategy that combines automation and human understanding to promote creativity and long-term growth. 2024 IEEE.
Chib S.; Gandhi M.P.; Sastry N.S.; Zabiullah B.I.; Ragavendra B.S.B.; Lakshmi S.R.
Proceedings of International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024, pp. 664-667.
Chib S., Datta Meghe Institute Of Management Studies, Madhya Pradesh, Nagpur, India; Gandhi M.P., Yeshwantrao Chavan College Of Engineering, Madhya Pradesh, Nagpur, India; Sastry N.S., School Of Management, Cmr University, Karnataka, Bangalore, India; Zabiullah B.I., School Of Management, Cmr University, Karnataka, Bangalore, India; Ragavendra B.S.B., Donbosco Institute Of Management Studies And Computer Applications, Mba Department, Kumbalgodu, Bangalore, India; Lakshmi S.R., Christ University, Department: Business & Management, Nagasandra, Karnataka, Bangalore, India
Powering Ahead: Navigating Opportunities and Challenges in the Electric Vehicle Revolution
The technology is clearing ways for buzz in the market brimming with innovative items and new prospects. The government has planned to shift to electric vehicles by 2030, whether it is for personal or commercial use. As innovative improvements are developing quickly, it is blasting the market with the EVs industry which expected to transform the future (Rajkumar S, in Indian electric vehicle conundrum: a tale of opportunities amid uncertainties, 2020). Volvo company has also announced that it will be fully electric by 2030 (https://gadgets.ndtv.com, in Volvo to go all electric by 2030, sell exclusively online, 2021). It is expected that EVs will generate more demand for electricity and help in settling the focus on resources problem. It will also help in improving the financial feasibility of power sector projects. In India, there is more dependency on renewable energy so this is a chance to be independent and provide cheap power to the people. The EVs are more economical than petrol or diesel vehicles. The government is also giving incentives to the makers of electric vehicles. GST on electric vehicles is 12% as compared to petrol and diesel vehicles with 28% GST. As per the Electricity Act, 2003, a distribution license is needed to supply power from respective state electricity regulatory commissions. Another challenge is that charging the EVs will lead to a rise in the demand of electricity which is risky for the electricity distribution companies (www.livemint.com, in Indias electric vehicle drive: challenges and opportunities, 2017). Indians are very price conscious. A recent study revealed that Indians are ready to compromise on more charging time, but they are not ready to pay higher price for EVs (Gupta NS, in Electric vehicle adoption in India: study reveals three tipping points, 2020). From Fig.1, it can be seen that in 2014 investment in EVs was $2.2 billion which has increased to $406 billion in 2019 (Shanti S, in The road to green: what makes electric vehicle adoption a challenge for India. 2020). This shows that people are shifting toward EVs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Electrical Engineering, Vol-1162, pp. 643-649.
Sharma B., School of Commerce, Finance and Accountancy, Christ University, Karnataka, Bangalore, India
Artificial Intelligence Application in Human Resources Information Systems for Enhancing Output in Agricultural Companies
Agricultural; Artificial Intelligence; Human Resources; Information Systems
Artificial intelligence (simulated intelligence) apparatuses like master systems, normal language handling, discourse acknowledgment, and machine vision have changed how much work in agribusiness, yet in addition its nature. This is on the grounds that the total populace and interest for food are developing, and the climate and water supply are evolving. Specialists and researchers are presently moving towards involving new IoT advances in shrewd cultivating to assist ranchers with utilizing manmade intelligence innovation to improve seeds, crop security, and composts. This will get ranchers more cash-flow and help the pay of the country in general. In agribusiness, computer-based intelligence is making its mark in three primary regions: checking soil and harvests, prescient examination, and cultivating robots. Along these lines, ranchers are utilizing sensors and soil tests increasingly more to accumulate information that can be utilized by ranch the board apparatuses for additional exploration and examination. This book adds to the field by giving an outline of how computer-based intelligence is utilized in agribusiness. It begins with a prologue to simulated intelligence, including a survey of all the computer-based intelligence techniques utilized in horticulture, similar to AI, the Web of Things (IoT), master systems, picture handling, and PC vision. 2024 IEEE.
Ramasamy K.; Rajendran S.; Saurabh S.; Dwivedi S.; Soundarraj P.L.; Jeyalakshmi S.
https://doi.org/10.1109/ICONSTEM60960.2024.10568833 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198829922&doi=10.1109%2fICONSTEM60960.2024.10568833&partnerID=40&md5=4bed0abebdd430f520206e22bbae7251
Ramasamy K., Sri Krishna Arts And Science College, Department of Management Science, Coimbatore, Kuniyamuthur, India; Rajendran S., Psg College of Arts And Science, Department of Corporate Secretaryship, Coimbatore, India; Saurabh S., I Business Institute, Knowledge Park-II, UP, Greater Noida, 201310, India; Dwivedi S., I Business Institute, Knowledge Park-II, UP, Greater Noida, 201310, India; Soundarraj P.L., School of Business And Management, Christ University, Lavasa, Maharashtra, Pune, 412112, India; Jeyalakshmi S., Psg College of Arts And Science, Department of Corporate Secretaryship, Coimbatore, India
Human-Computer Interaction: Innovations and Challenges in Virtual Reality
Empirical Analysis; Haptic Feedback; Inclusivity; Innovations; Interdisciplinary Collaboration; Motion Sickness; Transformative Technology; Virtual Reality
In an effort to shed light on the advances and difficulties that are shaping the area of Virtual Reality (VR), this research paper digs into the ever-evolving world of Human-Computer Interaction (HCI) within the context of VR. We have found important insights with theoretical and practical applications via a careful research technique comprising mathematical modelling, data collecting, and empirical analysis. Through our investigation of new technologies, we have shown the revolutionary potential of haptic feedback systems in VR settings. Our results, backed by a solid mathematical model, provide light on the measurable effect of haptic feedback and suggest it has the potential to radically alter user experiences in fields as diverse as gaming, instruction, and treatment. At the same time, we have overcome a number of obstacles inherent to virtual reality human-computer interaction, including motion sickness. Our mathematical model of motion sickness and its treatment lays the groundwork for creating VR experiences that are both enjoyable and safe for a wider range of users. This study highlights the ethical implications of VR HCI, highlighting the importance of responsible development and deployment in addition to advances and problems. To make sure that the advantages of this gamechanging technology are used in a responsible manner, we discuss issues like privacy, informed consent, and the possibility for addiction in VR. Our results, as we reach the end of our trip, are both a celebration of potential and a guide for where VR HCI is headed. They motivate more research into inclusive and human-centered design, personalized motion sickness prevention, and cutting-edge haptic feedback technologies. To further lead the development of VR HCI, we advocate for continuous multidisciplinary cooperation and the adoption of thorough ethical rules and regulations. 2024 IEEE.
Anitha C.; Soundarraj P.L.; Geethanjali N.; Boopathy C.; Venkatesh A.N.; Krishna I.M.
Anitha C., School of Computing, Mohan Babu University, Erstwhile SreeVidyanikethan Engineering College, Department of Computer Science And Engineering, Tirupati, 517102, India; Soundarraj P.L., School of Business And Management, Christ University, Lavasa, Maharashtra, Pune, 412112, India; Geethanjali N., Psna College of Engineering And Technology, Department of Management Studies, Tamil Nadu, Dindigul, India; Boopathy C., School of Management Studies, Sri Krishna College of Engineering And Technology, Coimbatore, Kuniyamuthur, India; Venkatesh A.N., Hrm And General Management, Isbr Business School, Karnataka, Bangalore, India; Krishna I.M., K L Business School, Klef, Andhra Pradesh, Vaddeswaram, India
A Novel Approach to Predicting the Risk of Illegal Activity and Evaluating Law Enforcement Using WideDeep SGRU Model
Gated Recurrent Unit(GRU); Illegal Activity; Law Enforcement Officers (LEO)
The main reaction to the illicit extraction of natural resources in protected areas around the world is law enforcement patrols headed by rangers. On the other hand, research on patrols' efficacy in reducing criminal behavior is lacking. Similarly, tactics to enhance the effectiveness of patrol organization and monitoring have received very little attention. Sequencing is crucial for model training, feature selection, and preprocessing. Preprocessing steps include cleaning, discretizing, duplicating, and normalizing data. Feature selection makes use of genetic algorithms, which are basically search algorithms with an evolutionary bent that factor in natural selection and genetics. Training stacked GRU models necessitates meticulous feature management. Even the most cutting-edge algorithms, GRU and BiGRU, are no match for the suggested technique. An astounding 97.24% accuracy grade was disclosed by the results, showcasing exceptional growth. 2024 IEEE.
Mishra P.; Anusha P.; Kumar A.; Pal S.; Patra J.P.; Chauhan A.
1st International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024
https://doi.org/10.1109/ICECCC61767.2024.10593977 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200657446&doi=10.1109%2fICECCC61767.2024.10593977&partnerID=40&md5=fdbfb038bb3effe675cfd1430d23b203
Mishra P., Computer Science and Application Reva University, Karnataka, Bangalore, India; Anusha P., R.M.K. Engineering College, Department of ECE, Tamilnadu, Kavaraipettai, India; Kumar A., SCA Manav Rachna International Institute of Research and Studies, Haryana, Faridabad, India; Pal S., Computer Science and Engineering Indian Institute of Technology Jammu, Jammu & Kashmir (UT), Jagti, India; Patra J.P., EE And EEE Krupajal Engineering College-KEC, Odisha, Puri, India; Chauhan A., CHRIST (Deemed to be University), Department of Life sciences School of Sciences, Karnataka, Bengaluru, India
Advancing Road Safety through Driver Drowsiness Detection Using Deep Learning Model
CNN; computer vision; deep learning; Driver drowsiness; facial expressions; head pose; image processing; mouth movement; ResNet101
Driver drowsiness poses a significant threat to public safety, contributing to numerous road accidents and fatalities annually. Drowsy drivers exhibit characteristic changes in facial expressions and behaviors, including eye closure, head nodding, and yawning. These indicators can be detected through various techniques, including image processing, computer vision, and machine learning. This research investigates a promising approach: utilizing a ResNet-101 deep convolutional neural network (CNN) for driver drowsiness detection based on eye, head, and mouth states. The model was trained on a vast dataset of 2.2 million images, covering diverse driving conditions. Despite achieving a 69% accuracy, suggesting real-world potential, computational limitations restricted training to only a quarter of the data. This necessitates further research with larger datasets and increased resources to enhance accuracy and robustness. 2024 IEEE.
2024 3rd International Conference on Artificial Intelligence for Internet of Things, AIIoT 2024
Pandey S., CHRIST(Deemed To Be) University Lavasa, School of Science, Maharashtra, Pune, India; Lekha J., CHRIST(Deemed To Be) University Lavasa, School of Science, Maharashtra, Pune, India; Kadyan S., CHRIST(Deemed To Be) University Lavasa, School of Science, Maharashtra, Pune, India
A Machine Learning Approach for Revving Up Revenue of Indian Tech Companies
arima; fb prophet; indian tech companies; machine learning; revenue; v.a.r model
This study addresses a critical gap in research by examining the effectiveness of various machine learning models in predicting revenue for Indian tech companies. The V.A.R, ARIMA, simple moving average, weighted moving average, and FB Prophet models were employed and their performances was compared. The findings demonstrate that FB Prophet consistently outperforms other models, exhibiting superior accuracy in revenue forecasting. This underscores FB Prophet's potential to offer precise revenue predictions, enabling companies to gain insights into their financial health, anticipate market trends, and optimize decision-making. Future research could further enhance accuracy by incorporating economic indicators, providing a more holistic view of revenue dynamics and empowering companies to make more informed strategic decisions. 2024 IEEE.
2nd IEEE International Conference on Data Science and Information System, ICDSIS 2024
https://doi.org/10.1109/ICDSIS61070.2024.10594491 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200126947&doi=10.1109%2fICDSIS61070.2024.10594491&partnerID=40&md5=839ff3e2b69f9adccb24eb4e56707556
Tungar S.K., Christ (Deemed To Be University), Department Of Statistics And Data Science, Karnataka, Bengaluru, India; Umme Salma M., Christ (Deemed To Be University), Department Of Statistics And Data Science, Karnataka, Bengaluru, India
Utilizing Artificial Intelligence-Powered Chatbots for Enhanced Customer Support in Online Retail
Anthropomorphism; Artificial intelligence; Chatbot; Compliance; Customer service; Social presence
In many e-commerce contexts, live chat interfaces have become popular as a way to communicate with consumers and provide real-time customer support. Conversational software agents, commonly known as Chatbots, are systems created to converse with users in natural language and are often based on artificial intelligence (AI). These systems have replaced human chat service agents in many cases. Although AI -based Chatbots have been widely used due to their time and cost savings, they have not yet met consumer expectations, which may make users less likely to comply with chatbot requests. We empirically study, through a randomized online experiment, the impact of verbal humanoid design cues and a direct approach on compliance with user requirements, based on Social Reactions and Attachment Commitment Theory. Our results show that consumers are more likely to cooperate with chatbot service response requests when there is humanity and consistency. Furthermore, the results demonstrate that social presence plays a mediating role between humanoid design cues and user compliance. 2024 IEEE.
Jayakumar M.; Soundarraj P.L.; Vishnu Priya P.M.; Gupta R.; Abbas J.K.; Naser S.J.
Jayakumar M., Vel Tech RangaraJan Dr. Sagunthala R&d Institute of Science And Technology, Department of Management Studies, No.42, Avadi-Vel Tech Road, Chennai, Tamil Nadu, Avadi, 62, India; Soundarraj P.L., School of Business & Management - Mba, Christ University, Lavasa Campus, Maharashtra, Pune, 412112, India; Vishnu Priya P.M., Kristu Jayanti College (Autonomous), Department of Computer Science, Kothanur, K. Narayanapura, Karnataka, Bangalore, 560077, India; Gupta R., Management, Bharati Vidyapeeth Inst. of Mgmt. And Res., Affil. To Bharati Vidyapeeth (Deemed To Be Univ.), Pune, A-4 Paschim Vihar, Rohtak Road, new Delhi, 110063, India; Abbas J.K., AL-Nisour University College, Baghdad, Iraq; Naser S.J., College of Pharmacy, National University of Science And Technology, DhiQar, Iraq
IoT and AI for Real-Time Customer Behavior Analysis in Digital Banking
AI (Artificial Intelligence); Context-aware computing; Customer behavior analysis; Customer experience; Data analytics; Digital banking; IoT (Internet of Things); Machine learning; Personalization; Predictive modeling; Real-time; Smart devices
Digital transformation has revolutionized the banking industry, ushering in an era of enhanced customer experiences and operational efficiency. The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has further propelled this evolution by providing real-time insights into customer behavior. This research explores the integration of IoT and AI for real-time customer behavior analysis in the context of digital banking. The proliferation of connected devices, ranging from smart phones to wearables, has generated an unprecedented volume of data. IoT facilitates the collection of diverse data points, such as transaction history, location information, and device interactions, creating a comprehensive digital footprint for each customer. Simultaneously, AI algorithms leverage this wealth of data to analyze, predict, and respond to customer behavior dynamically. In the realm of digital banking, understanding and adapting to customer behavior in real-time is crucial for providing personalized services, preventing fraud, and optimizing operational processes. This research delves into the mechanisms by which IoT sensors and devices, coupled with AI algorithms, enable banks to gain deeper insights into customer behavior patterns. Key components of the proposed system include data acquisition through IoT devices, secure data transmission protocols, and AI-driven analytics engines. In conclusion, this research advocates for the symbiotic relationship between IoT and AI in digital banking to enable real-time customer behavior analysis. 2024 IEEE.
Madhumita G.; Das T.; Das S.; Khatri E.; Ravisankar P.; Hemachandu P.
5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 198-203.
Madhumita G., Vels Institute of Science, Technology and Advanced Studies, Department of MBA, Tamil Nadu, Chennai, India; Das T., Christ University, Department of School of Business Management, UP, Ghaziabad, India; Das S., Christ University, Department of School of Commerce, Finance & Accountancy, UP, Ghaziabad, India; Khatri E., Nitte Meenakshi Institute of Technology, Department of Management Studies, Karnataka, Bengaluru, India; Ravisankar P., Saveetha College of Liberal Arts and Sciences, Department of Commerce, Thandalam, Chennai, India; Hemachandu P., Sasi Institute of Technology and Engineering, EEE Department, Andhrapradesh, Tadepallidusem, 534101, India
Prioritizing Factors Affecting Customers Satisfaction in the Internet Banking Using Artificial Intelligence
Artificial Intelligence; Convolutional Neural Networks; Customer Satisfaction; etc; Internet Banking; Random Forest
Internet banking has revolutionised the way customers interact with their banks, providing them with convenient access to a wide range of financial services from the comfort of their homes or mobile devices. Customer satisfaction the success of an endeavour is contingent upon a vital component internet banking Service provision, as it pertains directly impacts customer retention and loyalty. This research explores the application of artificial intelligence (AI) techniques, specifically random forest and convolutional neural networks (CNN), to prioritise the factors that affect customer satisfaction in internet banking. The study begins with data collection from a diverse sample of internet banking customers, including demographic information, transaction history, and customer feedback. These may include the ease of navigation, the response time of the platform, and the level of trust in the bank's security measures. Furthermore, convolutional neural networks (CNN) are utilised to analyse unstructured data such as customer feedback and reviews. By applying natural language processing techniques, CNN s extract sentiment and topic information from customer comments. This approach can ultimately lead to improved customer retention and loyalty, ensuring the long-term success and competitiveness of internet banking platforms. In conclusion, this study showcases the power of AI, specifically Random Forest and CNN, in prioritising factors affecting customer satisfaction in internet banking. It highlights the significance of using both quantitative and qualitative investigations in order to attain a comprehensive comprehension of customer sentiments and preferences in the digital banking landscape. 2024 IEEE.
Soundarraj P.L.; Javaraj P.; Meetna S.; Elaiyaraja A.; Naqvi S.R.; Gamadia R.
https://doi.org/10.1109/ICONSTEM60960.2024.10568595 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198853225&doi=10.1109%2fICONSTEM60960.2024.10568595&partnerID=40&md5=cc0330807822ac68b00fb053dcb616e0
Soundarraj P.L., School of Business And Management, Christ University, Maharashtra, Pune, 412112, India; Javaraj P., Sri Ramakrishna College of Arts And Science, Department of B. Com It, Coimbatore, India; Meetna S., Amrita School of Business, Coimbatore, India; Elaiyaraja A., PanimalarEngineering College, Department of Management Studies, Chennai, India; Naqvi S.R., Amity Business School, Amity University, Department of Human Resource And Organizational Behaviour, Up, Noida, India; Gamadia R., C K Shah Vijapurwala Institute of Management, Department of Management, Gujarat, Vadodara, India
7Li Photodisintegration withCircularly Polarized Photons
The study of photodisintegration of 7Li is of importance to Nuclear Physics, Particle Physics and Astrophysics. Primordial abundances of light elements such as D, 3He, 4He and 7Li are predicted by Big Bang theory of early universe and is of great interest to cosmologists. Lithium, being fragile gets destroyed easily at relatively low temperatures. WMAP measurements have inferred that 7Li abundance is two to three times more than that inferred by the low metallicity halo stars [1]. In recent years based on lithium isotopes series of experimental measurements are being carried out using High-Intensity Gamma-Ray Source (HIGS) at Duke Free Electron Laser Laboratory. Experiments [2, 3] were carried out, to measure the differential cross-section of the photoneutron reaction channel in photodisintegration of 7Li, where the progeny nuclei is in the ground state as well as in excited states. Theoretical study on photodisintegration of deuteron was carried out using a model-independent formalism [47] and in these studies, it was shown clearly that there could be 3 different E1? amplitudes leading to final relative n-p state. Subsequently, evidence for the existence of these three amplitudes was found in experimental studies [6] at slightly higher energies in different contexts. Using the same approach, model-independent formalism was developed for photodisintegration of 7Li [8] and an analysis was carried out to study the differential cross section with linearly polarized photons. Extending this study we propose to discuss the reaction channel 7Li+??6Li+n with initially circularly polarized photons. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Aswathi V.; Shastri V.; Shilpashree S.P.
Springer Proceedings in Physics, Vol-304 SPPHY, pp. 651-653.
Aswathi V., School of Engineering and Technology, Christ University, Bangalore, 560074, India; Shastri V., School of Engineering and Technology, Christ University, Bangalore, 560074, India; Shilpashree S.P., School of Engineering and Technology, Christ University, Bangalore, 560074, India
Unveiling the pattern of PhishingAttacks using the Machine Learning approach
Adaptive Learning; Models; Phishing Detection; Real-time Threat Detection; User-Centric Design
This study introduces a unique approach to strengthening cybersecurity by combining advanced models for real-time detection of phishing websites. A classifier is trained to discern patterns associated with legitimate and phishing URLs, leveraging a carefully organized labeled dataset. The model in this paper forms the foundation for a real-time detection system, providing users with real-time information on potential phishing threats. Integrating an adaptive decision-making algorithm improves decision-making adaptability, particularly in scenarios challenging the model's confidence. A user feedback loop ensures the continuous learning and refinement of the system, aligning it more closely with user expectations. The future scope of this research involves exploring advanced models, improving explainability, and incorporating dynamic features for enhanced detection. Adaptive policies, large-scale deployment, and ethical implications are pivotal for real-world applicability. In conclusion, this study contributes to advancing phishing detection methodologies and lays the groundwork for future innovations in cybersecurity. The collaborative efforts of academia, industry, and cybersecurity stakeholders arenecessaryfor realizing the full potential of this paper and ensuring a safer online platform for users. 2024 IEEE.
Proceedings - 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2024
Sinha V., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India; Shanbhog M., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India; Upreti K., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India
Analysis of Cardiovascular Diseases Prediction Using Machine Learning Classification Algorithms
Cardiovascular Diseases; COVID-19; Cross-validation; Grid Search; Healthcare Decision Making; Logistic Regression; Machine Learning; Random Forest; XGBoost Fatality Prediction
Worldwide healthcare systems have faced enormous hurdles because of the COVID-19 pandemic, especially when it comes to treating individuals who already have pre-existing disorders such as cardiovascular diseases (CVDs). Prioritizing medical therapies and resources for COVID-19 patients who are at increased risk of mortality from underlying CVDs requires early identification. In this work, we investigate how well three machine learning algorithms-, Random Forest, XGBoost, and Logistic Regression-predict death in COVID-19 patients who already have cardiovascular disease. We performed grid search and cross-validation using a dataset of clinical and demographic features of COVID-19 patients with and without CVDs to reduce overfitting and maximize model performance. Our findings show that among patients with CVDs, Logistic Regression had the best accuracy in predicting COVID-19 fatality, followed by Random Forest and Decision Tree coming in a close second. These results highlight how machine learning algorithms can help clinical professionals detect high-risk COVID-19 patients who have underlying cardiovascular diseases (CVDs), enable prompt interventions, and enhance patient outcomes. 2024 IEEE.
Srivastava S., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India; Upreti K., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India; Shanbhog M., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India
Artificial Intelligence Influence on Leadership Styles in Human Resource Management for Employee Engagement
Adaptive Leadership; Artificial Intelligence (AI); Human Resource Management (HRM); Leadership Styles; Longitudinal Studies; McGregor's Theory Y; Organizational Transformation
In this work, we investigate how the revolutionary effects of AI on leadership styles in the field of human resource management (HRM) have impacted employee motivation. To investigate the intricate relationship between AI adoption, HR management, and employee morale, we use a mixed-method approach, combining quantitative survey data with qualitative interview results. Both Leadership Style Change (LS-Change) and Employee Engagement (EE) show a statistically significant positive correlation with AI adoption. In the new AI-enabled HRM environment, HR executives are shifting their methods of leadership, adopting more flexible styles, giving workers more autonomy, and improving lines of communication. This research links theory and practice by providing actionable advice to HR managers and business owners. In order to further develop the topic of AI-enhanced HRM, future studies should investigate longitudinal dynamics, cross-industry variances, cultural and ethical issues, cutting-edge AI applications, and employee perspectives. 2024 IEEE.
Baruah A.; Shaikh M.; Kumar R.M.; Shaikh I.A.K.; Thomas S.N.; Natrayan L.
https://doi.org/10.1109/ICONSTEM60960.2024.10568819 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198835784&doi=10.1109%2fICONSTEM60960.2024.10568819&partnerID=40&md5=f00e282771a35033e72cff735a3fdb01
Baruah A., Christ University, Department of Psychology, Delhi-NCR, India; Shaikh M., Dr Vishwanath Karad Mit World Peace University, Department School of Business, Pune, India; Kumar R.M., School of Commerce And Management, Kristu Jayanti College, (Autonomous), Kothanur, K.Narayanapura, Bengaluru, India; Shaikh I.A.K., Bahrain Polytechnic, Faculty of Business, Bahrain; Thomas S.N., Marian College Kuttikkanam (Autonomous), Commerce, Kerala, Peerumedu, India; Natrayan L., Saveetha School of Engineering, Simats, Department of Mechanical Engineering, Tamil Nadu, Chennai, India
Smart Agriculture: Machine Learning Approach for Tea Leaf Disease Detection
Agriculture; Convolutional neural network; Machine learning; Pattern identification; Tea leafs
Across the globe, plant infections from pathogens such as fungi, bacteria and viruses are the major issues in the agricultural sector. Agricultural productivity is one of the most important things on which the nations economy highly depends. The detection of diseases in plants plays a major role in the agricultural field. This study proposes a multi-stage network involving Convolutional neural network, Pattern identification and Classification using Siamese network. The main objective behind this study is to enhance the disease detection technique performance. The image data of Tea leaves chosen for this study will be gathered. The algorithms based on techniques of image processing would be designed. The proposed algorithm was tested on the following diseases namely Red rust, Blister blight, Twig dieback, Stem canker, Grey Blight, Brown Blight, Brown root rot disease and Red root rot disease in Tea leaves. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Rajagopal M.; Sivasakthivel R.; Pandey M.
Lecture Notes in Networks and Systems, Vol-967 LNNS, pp. 199-209.
Rajagopal M., School of Business and Management, Christ University, Bangalore, India; Sivasakthivel R., Department of Computer Science, School of Sciences, Christ University, Bangalore, India; Pandey M., School of Business and Management, Christ University, Bangalore, India