Machine Learning Enabled Financial Statements in Assessing a Business's Performance
Business Performance; Ethical Considerations; Financial Analysis; Financial Ratios; Financial Statements; Interpretability; Investor Confidence; Machine Learning; Predictive Analytics; Return on Assets (ROA)
Machine Learning Enabled Financial Statements (MLEFS) revolutionize corporate performance analysis. This study examines MLEFS's dramatic effects using data gathering, model creation, interpretability, deployment, and ethics. We found that MLEFS accurately predicts crucial financial measures, helping investors, lenders, and financial analysts make better judgments. The study emphasizes the importance of financial measures like Return on Assets (ROA) in supporting financial theories and models. The research also stresses interpretability and ethics, promoting responsible machine learning in finance. Future trends include enhanced interpretability, strong ethical frameworks, real-time analysis, big data integration, regulatory adaption, and industrial acceptance. This study opens the door to data-driven financial analysis and decision-making, improving strategic planning, risk reduction, and investor trust. 2024 IEEE.
Priya P.S.; Shah J.A.; Aarawal A.; Kalra R.; Kadam S.; Sontakke K.A.
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/ICONSTEM60960.2024.10568659 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198842164&doi=10.1109%2fICONSTEM60960.2024.10568659&partnerID=40&md5=4dff6c8297c695b78c840478be3d047e
Priya P.S., Sri Krishna College of Technology, Tamil Nadu, Coimbatore, India; Shah J.A., Amity Business School, Amity University, Maharashtra, Mumbai, India; Aarawal A., Management, Ndiit, Delhi, New Delhi, India; Kalra R., School of Business And Management, CHRIST(Deemed To Be University), Karnataka, Bangalore, India; Kadam S., Symbiosis Institute of Business Management, Pune, Symbiosis International, Deemed University, Maharashtra, Pune, India; Sontakke K.A., Sies College of Management Studies, Department of Finance, Maharashtra, Navi Mumbai, India
Synergizing Senses: Advancing Multimodal Emotion Recognition in Human-Computer Interaction with MFF-CNN
Deep Learning and Emotion Classification; Facial Expressions; Human-Computer Interaction; MFF-CNN (Multimodal Fusion and Convolutional Neural Network); Multimodal Emotion Recognition; Speech Signals
Optimizing the authenticity and efficacy of interactions between humans and computers is largely dependent on emotion detection. The MFF-CNN framework is used in this work to present a unique method for multidimensional emotion identification. The MFF-CNN model is a combination of approaches that combines convolutional neural networks and multimodal fusion. It is intended to efficiently collect and integrate data from several modalities, including spoken words and human facial expressions. The first step in the suggested system's implementation is gathering a multimodal dataset with emotional labels added to it. The MFF-CNN receives input features in the form of retrieved facial landmarks and voice signal spectroscopy reconstructions. Convolutional layers are used by the model to understand hierarchies spatial and temporal structures, which improves its capacity to recognize complex emotional signals. Our experimental assessment shows that the MFF-CNN outperforms conventional unimodal emotion recognition algorithms. Improved preciseness, reliability, and adaptability across a range of emotional states are the outcomes of fusing the linguistic and face senses. Additionally, visualization methods improve the interpretability of the model and offer insights into the learnt representations. By providing a practical and understandable method for multimodal emotion identification, this study advances the field of human-computer interaction. The MFF-CNN architecture opens the door to more organic and psychologically understanding human-computer interactions by showcasing its possibilities for practical applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Upreti K.; Vats P.; Malik K.; Verma R.; Divakaran P.; Gangwar D.
Lecture Notes in Networks and Systems, Vol-1047 LNNS, pp. 279-288.
Springer Science and Business Media Deutschland GmbH
Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Ghaziabad, India; Vats P., Department of Computer Science and Engineering, SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India; Malik K., School of Law, CHRIST (Deemed to Be University), Delhi NCR, Ghaziabad, India; Verma R., School of Business and Management, CHRIST (Deemed to Be University), Delhi NCR, Ghaziabad, India; Divakaran P., School of Business and Management, Himalayan University, Arunachal Pradesh, Itanagar, India; Gangwar D., Department of Management, Babu Banarasi Das Institute of Technology and Management, Lucknow, India
Machine Learning Approach for Evaluating Industry-Based Employer Ranking and Financial Stability
Data-driven Assessment; Employer ranking; Employer Ranking; Financial Stability; Job Market Metrics; Longitudinal Studies; Machine Learning; Sentiment Analysis
Using the computational prowess of machine learning, this study presents a fresh method for assessing the relative standing and fiscal health of employers across different sectors. The research makes use of a wide variety of data, including financial reports, statistics on the labor market, employee evaluations, and indicators unique to the business, to arrive at in-depth judgements. The financial stability assessment applies a linear regression model, whereas employer ranking is predicted using a logistic regression model. Financial data, employment market dynamics, and sentiment research are used as foundational characteristics for these models. Company A is more financially stable than Company B, yet it is anticipated to be ranked lower as an employer. This highlights the difficulty of judging businesses. The implications of these results for job-seekers, investors, and businesses are varied. The study also highlights the significance of ethics, openness, and addressing biases in assessment. This study paves the way for future advancements in this crucial subject and provides a basis for data-driven, well-informed decision-making in the ever-changing landscapes of contemporary industrial evaluations. 2024 IEEE.
Swain P.C.; Balan S.; Lakshmi S.R.; Choudhary A.; Patjoshi P.K.; Raja J.
5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 111-115.
https://doi.org/10.1109/ICRTCST61793.2024.10578454 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199334547&doi=10.1109%2fICRTCST61793.2024.10578454&partnerID=40&md5=76ce345a6cf8cdce78c5aa79bb5b8a9d
Swain P.C., Kalinga Institute of Industrial Technology (KIIT), School of Social, Financial & Human Sciences, Odisha, Bhubaneswar, India; Balan S., Rajalakshmi Engineering College, Department of Management Studies, Chennai, India; Lakshmi S.R., Christ University, Department of Business & Management, Karnataka, Bangalore, 560073, India; Choudhary A., Graphic Era Deemed to be University, Department of Computer Science & Engineering, Uttarakhand, Dehradun, 248002, India; Patjoshi P.K., Centurion University of Technology and Management, BBSR Campus, School of Management, India; Raja J., St. Martins Engineering College, Department of Computer Science and Engineering, Telangana, Secunderabad, India
Advanced Cervical Lesion Detection using Deep Learning Techniques
cervical cancer; cervigram; cervix; colposcope; Segmentation
Cervical cancer has been one of the common causes for mortality by cancer in women across the world. But there are currently not enough skilled colposcopists, and the training process is drawn out. This implicates that there is a significant scope for artificial intelligence based computational models for segmentation of colposcope images. This paper proposes a segmentation network to accurately segment the cervix region and acetowhite lesions in a cervigram. This research can lay a foundation for research aiming to classify the cervix malignancy using AI. The method performed with a precision of 0.73870.1541, accuracy of 0.9291, recall of 0.79120.1439, a dice score of 0.74310.1506 and specificity of 0.95890.0131. The results prove that the model is reliable and robust. 2024 IEEE.
2024 1st International Conference on Communications and Computer Science, InCCCS 2024
Mukku L., Computer Science and Engineering, CHRIST(Deemed to be University), Bangalore, India; Thomas J., Computer Science and Engineering, CHRIST(Deemed to be University), Bangalore, India
Biomedical Mammography Image Classification Using Patches-Based Feature Engineering with Deep Learning and Ensemble Classifier
Biomedical images; Breast mass classification technique; CNN; Deep learning; Mammography
In order to reduce the expense of radiologists, deep learning algorithms have recently been used in the mammograms screening field. Deep learning-based methods, like a Convolutional Neural Network (CNN), are now being used to categorize breast lumps. When it involves classifying mammogram imagery, CNN-based systems clearly outperform machine learning-based systems, but they do have certain disadvantages as well. Additional challenges include a dearth of knowledge on feature engineering and the impossibility of feature analysis for the existing patches of pictures, which are challenging to distinguish in low-contrast mammograms. Inaccurate patch assessments, higher calculation costs, inaccurate patch examinations, and non-recovered patched intensity variation are all results of mammogram image patches. This led to evidence that a CNN-based technique for identifying breast masses had poor classification accuracy. Deep Learning-Based Featured Reconstruction is a novel breast mass classification technique that boosts precision on low-contrast pictures (DFN). This system uses random forest boosting techniques together with CNN architectures like VGG 16 and Resnet 50 to characterize breast masses. Using two publicly accessible datasets of mammographic images, the suggested DFN approach is also contrasted with modern classification methods. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Poonia R.C.; Upreti K.; Jafri S.; Parashar J.; Vats P.; Singh J.
Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 275-285.
Poonia R.C., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Ghaziabad, India; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Ghaziabad, India; Jafri S., Administrative Science Department, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; Parashar J., Department of Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Technology and Management, Delhi, India; Vats P., Department of Computer Science and Engineering, SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India; Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India
Development of LIDAR-SLAM Integrated Low Cost Health Care Monitoring Robot with Sustainable Material
3D LIDAR; health care monitoring robot; localization and mapping; patient assistance; sustainable innovation
Beyond the global pandemic, healthcare has faced a myriad of challenges, from rising costs and accessibility issues to the need for precision in patient care and efficient medication delivery. This project embodies a visionary response to the multifaceted challenges faced by healthcare systems in health centers located in rural areas. The proposed research work focused on design and development of a health care monitoring robot with integration of 3D LIDAR Simultaneous Localization and Mapping (SLAM) based navigation approach, introduction of sustainable materials like bamboo and wood composites for development of robotic arm and robotic body frames. Also, from the initial tests it has been observed that with the developed mobile robot functions like precision medicine delivery, Open AI-Enabled continuous monitoring, hospital environment sanitization and emergency oxygen supply can be performed efficiently. 2024 IEEE.
Rout A.; Reddy H.; Raj S.V.; Reddy B.; Naidu L.; Kiran K.; Ravikumar R.
2024 9th International Conference on Control and Robotics Engineering, ICCRE 2024, pp. 12-16.
https://doi.org/10.1109/ICCRE61448.2024.10589609 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199903688&doi=10.1109%2fICCRE61448.2024.10589609&partnerID=40&md5=db7ddd74fc5a9420b3cbcbd5923eba41
Rout A., CHRIST (Deemed to be University), Dept. of Mechanical and Automobile Engineering, Bangalore, India; Reddy H., CHRIST (Deemed to be University), Dept. of Mechanical and Automobile Engineering, Bangalore, India; Raj S.V., CHRIST (Deemed to be University), Dept. of Mechanical and Automobile Engineering, Bangalore, India; Reddy B., CHRIST (Deemed to be University), Dept. of Mechanical and Automobile Engineering, Bangalore, India; Naidu L., CHRIST (Deemed to be University), Dept. of Mechanical and Automobile Engineering, Bangalore, India; Kiran K., CHRIST (Deemed to be University), Dept. of Mechanical and Automobile Engineering, Bangalore, India; Ravikumar R., CHRIST (Deemed to be University), Dept. of Mechanical and Automobile Engineering, Bangalore, India
Human-Machine Interactions andAgility inSoftware Development
Agile project management; Burndown; Collaboration; Inspection; Lead time; Project management; Scrum; Transparency; Velocity
A modern organization cannot function without project management. Organizations, governments, and non-profits recognize how important modern project techniques are to the success of their IT projects. Many people understand that excellent project skills are crucial for remaining competitive in the workplace. Many project management concepts will help them with their everyday interactions with people and technology. Project management aims to plan, organize, motivate, and control resources to accomplish specific objectives and meet specific success criteria. The major challenge is to achieve all the project goals and objectives while respecting the preconceived constraints of the project. Project management for data science is easy with Agile. Understanding the different approaches to project management and how they can fit into information science is essential. Several project management tools are available to maintain and report on a projects progress. As proposed in this paper, a comprehensive study on project management and Agile methodologies helps enhance the teams interactions when working for data science project management. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Sahu S.K.; Muloor K.; Samanta D.; Rajaram P.; Sengar S.S.
Lecture Notes in Networks and Systems, Vol-1, pp. 423-435.
Sahu S.K., Larsen & Toubro Infotech Limited, Karnataka, Bengaluru, India; Muloor K., Larsen & Toubro Infotech Limited, Karnataka, Bengaluru, India, CHRIST Deemed to be University, Karnataka, Bengaluru, India; Samanta D., CHRIST Deemed to be University, Karnataka, Bengaluru, India, Department of Computing and Information Technologies, RIT Kosovo (A.U.K), Rochester Institute of Technology, RIT Global Campus, Prishtina, Kosovo, 10000; Rajaram P., Department of Computing and Information Technologies, RIT Kosovo (A.U.K), Rochester Institute of Technology, RIT Global Campus, Prishtina, Kosovo, 10000; Sengar S.S., Department of Computer Science, Cardiff Metropolitan University, Cardiff, CF5 2YB, United Kingdom
AI-Enhanced IoT Data Analytics for Risk Management in Banking Operations
AI; Banking Operations; Data Analytics; Financial Risk; IoT; Real-time Analysis; Risk Management
Using IoT data analytics in conjunction with artificial intelligence (AI) has the potential to improve banking operations' risk management. Sophisticated analytical methods are necessary for the detection and management of possible risks due to the increasing complexity and amount of data generated by the banking industry. This research proposes a novel method for analysing real-time data from IoT devices by employing artificial intelligence algorithms. The risks associated with financial transactions and operations can be better and more accurately assessed using this method. Through the integration of AI's pattern recognition, anomaly detection, and predictive modelling capabilities with the massive amounts of data generated by Internet of Things devices, this project aims to substantially enhance the efficacy and efficiency of risk management approaches in the banking sector. Research like this could lead to innovative solutions that make financial institutions more resistant to rising risks by enhancing decision-making, reducing operational weaknesses, and so on. 2024 IEEE.
Thirumagal P.G.; Vaddepalli S.; Das T.; Das S.; Madem S.; Immaculate P.S.
5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 177-181.
https://doi.org/10.1109/ICRTCST61793.2024.10578533 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199358472&doi=10.1109%2fICRTCST61793.2024.10578533&partnerID=40&md5=cb3c7936415ed5b0a248ef13d338b17b
Thirumagal P.G., VISTAS, Department of MBA, Tamil Nadu, Chennai, India; Vaddepalli S., University of Technology and Applied Sciences, Department of Economics and Business Administration, 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, Delhi NCR Campus, UP, Ghaziabad, India; Madem S., Vignan's Institute of Information Technology, Department of Management Studies, India; Immaculate P.S., Sri SaiRam Institute of Technology, Department of MBA, West Tambaram, TamilNadu, Chennai, India
IoT-Based Smart Indoor Navigation System with Voice Assistance for Museums
Bluetooth low energy; Internet of Things; Navigation system; Smart indoor systems
In the current era of smart heterogenous devices, the surrounding environment too needs to be smarter to match the gravity of such devices. Such advanced environment can be built with the technology called Internet of Things (IoT). Due to the presence of such vivid thing devices in the Internet of Things (IoT) environment, the task of automatically predicting the end users desires can play an important role when it comes to match the pace of modern society with too much diverse aspects. Since last decade, people have deviated their attention towards Indian ancient culture and Museums are eye catching attraction where our ancient cultural heritage exist. To improvise the slow pace growth of the tourism sector, there is the crucial requirement of technological improvement especially due to the restrictions on installations of external hardware within the close proximity. One prominent way of improving tourists experience at museums is to renovate existing museums with IoT-based smart devices which is programmed such a way to automatically navigate the user indoor and briefs the associated information about artwork without any user intervention. In this paper, we propose an IoT-based smart indoor navigation system along with voice assistance which can enhance the tourists experience in a museum. In addition, the proposed design also delivers the very personalized cultural contents related to the visited artworks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-967 LNNS, pp. 449-457.
Manju, Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, India; Singh S., Department of Computer Science and Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, 560074, India
IoT-Driven Credit Scoring Models: Improving Loan Decision Making in Banking
Banking analytics; financial inclusion; IoT data in lending; IoT-driven credit scoring; Loan approval optimization; Loan decision-making; Risk assessment
By the game-changing possibilities of credit scoring models driven by the Internet of Things, this hopes to shed light on how the banking sector may enhance its loan decision-making procedures. Financial organisations are putting more and more faith in Internet of Things technologies to improve their risk assessment and lending processes. These IoT-driven models provide a more accurate and thorough assessment of creditworthiness by including real-time and detailed data on borrowers' activities, spending habits, and asset utilisation. This research examines the practicality and accuracy of Internet of Things (IoT) credit scoring by comparing it to conventional methods, looking closely at case researches, and analysing empirical data. The findings shed light on potential ways to enhance the loan approval and risk prediction procedures while also addressing concerns and considerations related to data privacy, security, and regulatory compliance. It is possible that decision-making frameworks could be altered by IoT-driven credit scoring algorithms, which could lead to a more inclusive and informed lending atmosphere. The contributes to the growing area of banking credit evaluation by showing that these models have promise. 2024 IEEE.
Thirumagal P.G.; Das T.; Das S.; Vinit Sikka C.S.; Amit Kumar C.S.; Kusuma T.
5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 193-197.
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, UP, Ghaziabad, India; Vinit Sikka C.S., School of Leadership and Management, Manav Rachna International Institute of Research and Studies, Department of UG Management Studies, Haryana, Faridabad, India; Amit Kumar C.S., School of Leadership and Management, Manav Rachna International Institute of Research and Studies, Department of UG Management Studies, Haryana, Faridabad, India; Kusuma T., Sree Vidyanikethan Engineering College, Department of Commerce, Andhra Pradesh, Tirupati, India
Factors Effecting on Work Values Towards Career Choices Among University Students
Anxiety; COVID-19 pandemic; Work values and career choices
The pandemic effect of COVID-19 triggered a global recession in the year 2020. The unpredictability that surrounds the coronavirus is the most challenging problem that many people must confront, particularly in terms of making decisions regarding their careers, considering the significant shift in employment opportunities. The purpose of this research is to investigate the influence anxiety and the Covid-19 pandemic have on work values and the reality of career choices among university students. A quantitative research methodology was applied to 110 respondents from a nearby institution to achieve the study's objective. This was done through online surveys and the snowball sampling technique. In order to acquire the findings, a data analysis using SPSS and PLS-SEM was carried out. It is evident from the study's findings that students work values are impacted by anxiety and the COVID-19 pandemic. Moreover, the findings support the hypothesis that anxiety and the COVID-19 pandemic influence students employment decisions. The findings of the study provide insight into the body of knowledge. The influence of anxiety and the COVID-19 pandemic on current work values among university students about career choices are examined, and recommendations are made to various stakeholders, such as policymakers, university management, and career counselors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Marimuthu N.V.; Chethiyar S.D.M.; Arshad M.Z.; Devarajah K.; Damodaran Y.K.; Arshad M.A.
Springer Proceedings in Business and Economics, pp. 765-788.
Marimuthu N.V., School of Applied Psychology, Social Work and Policy, College of Arts and Sciences, Universiti Utara Malaysia, Sintok, Malaysia; Chethiyar S.D.M., School of Applied Psychology, Social Work and Policy, College of Arts and Sciences, Universiti Utara Malaysia, Sintok, Malaysia; Arshad M.Z., School of Business Management, College of Business, Universiti Utara Malaysia, Sintok, Malaysia; Devarajah K., Medical Officer UD48, Klinik Kesihatan Linggi, Negeri Sembilan, Port Dickson, Malaysia; Damodaran Y.K., School of Sociology and Social Work, Christ Deemed to Be University, Karnataka, Bengaluru, India; Arshad M.A., School of Business Management, College of Business, Universiti Utara Malaysia, Sintok, Malaysia
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
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
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.
Proceedings - 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2024
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
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.
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
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
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
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
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
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