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.
Springer Science and Business Media Deutschland GmbH
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
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
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.
Institute of Electrical and Electronics Engineers Inc.
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
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
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
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
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
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
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
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
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.
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 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
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
Stress Management among Employees in Information Technology Sector Using Deep Learning
Deep Learning; Employees; Information Technology; Management; Stress
Information technology is one of the areas in India that is developing the quickest India's information technology (IT) administrations industry has become more merciless. The information technology area has been managing additional difficult issues like specialized development, administration enhancement, and worldwide overhauling starting from the beginning of this long period. Along these lines, it is unimaginable for everybody to adjust to the moving difficulties they experience in the field of information technology, which causes stress. Stress is something that individuals battle with for most of their lives. Albeit the information technology (IT) industry is notable for its hazardous turn of events and development, it is likewise portrayed by high worker burnout and stress levels. This theoretical proposes an original strategy for overseeing stress in the IT business that utilizes deep learning methods. This study utilizes deep learning calculations to expect, distinguish, and decrease stress makes all together location the earnest issue of stress among IT experts. The principal objective is to make a shrewd framework that can help organizations proactively recognize stress-related issues in their labor force and proposition specific cures. 2024 IEEE.
Soundarraj P.L.; Pelleti S.; John E.P.; Gadgil A.A.; Sehrawat A.; Yadav L.K.
Soundarraj P.L., School of Business And Management, Christ University, Lavasa, Maharashtra, Pune, 412112, India; Pelleti S., Klh Global Business School, KoneruLakshmaiah Education Foundation (Deemed To Be University, Estd. U/S. 3 of Ugc Act 1956), Catefgory - I, NIRF University Ranking: 28, Hyderabad, India; John E.P., Srm Institute of Science & Technology, Faculty of Management, Chennai, 600026, India; Gadgil A.A., E&c, KLSGogte Institute of Technology, Karnataka, Belagavi, 590008, India; Sehrawat A., Amity Institute of Corporate Communication, Amity University, Uttar Pradesh, Noida, India; Yadav L.K., Amity Institute of Corporate Communication, Amity University, UP, Noida, India
Improving Consumer Engagement with AI Chatbots: Exploring Perceived Humanness, Social Presence, and Interactivity Factors
AI chatbots; Artificial intelligence; Consumer engagement; Customer interaction; Human-computer interaction; Interactivity; Perceived humanness; Social presence; User experience
In many consumer industries, AI robots are becoming more and more popular because they let businesses communicate with their customers in a cheap and quick way. However, how well these measures work rests on how real and present people think they are in social situations. The main things that affect how customers deal with AI chatbots are looked into in this research. These are interaction, social presence, and perceived humanity.A wide range of users will be asked to fill out quantitative polls that will be used to judge how humanlike AI chatbots are, how well they can interact with others, and how much they interact with people. Additionally, performing qualitative interviews will give you a fuller picture of what customers want and how they interact with AI chatbots. Companies can make their chatbot exchanges with customers better by figuring out what makes the bots act like humans: friendly, interested, and sociable. This will allow them to make chatbots that are very specific to their customers' needs and tastes. The goal of this researchprogramme is to make customers happier, more loyal to brands, and have better experiences by creating AI chatbots that can have conversations with people like real people. 2024 IEEE.
Tripathi A.P.; Dhaundiyal P.; Sharma K.; Sukheswala J.; Dhiman A.; Sharma A.K.
Proceedings - 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2024
https://doi.org/10.1109/ACCAI61061.2024.10601926 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200559335&doi=10.1109%2fACCAI61061.2024.10601926&partnerID=40&md5=ecec965d4af409edc0320da4b08432c7
Tripathi A.P., Christ Deemed to Be University, Uttar Pradesh, India; Dhaundiyal P., Christ Deemed to Be University, Uttar Pradesh, India; Sharma K., K P B Hinduja College of Commerce, Maharashtra, India; Sukheswala J., K P B Hinduja College of Commerce, Maharashtra, India; Dhiman A., Manipal University Jaipur, Rajasthan, India; Sharma A.K., University of Technology and Applied Sciences, College of Economics and Business Administration, Salalah, Oman
Optimization of Friction Stir Welding Parameters for the Optimum Hardness of AlCu Butt Joints Using the Taguchi Method
Friction stir welding; Hardness; OHNS steel tool
In the present study, the base plates made of alloys AA6101 and C11000 (each 5 mm thick) were welded bythe FSW technique using a hardened OHNS steel weld tool. The percentage contribution of the input process parameters, such as tool rotational speed in rpm, feed rate in mm/min, and tool pin offset in mm, on the output parameter joint hardness, were examined using the experimental design Taguchi L9 and ANOVA numerical tool analysis. From the optimization method, at 1000rpm tool rotational speed, 40mm/min feed rate and weld tool pin toward AA6101 alloy side will have the highest hardness. The tool rotational speed experiences a maximum significant impact on the joint hardness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Kumar J.P.; Raj A.; Rout I.S.; Ravichandran G.; Darshan S.M.; Niranjana S.J.; Vinod S.K.
Lecture Notes in Mechanical Engineering, pp. 63-70.
Kumar J.P., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Raj A., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Rout I.S., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Ravichandran G., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Darshan S.M., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Niranjana S.J., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Vinod S.K., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India
Research on Unmanned Artificial intelligence Based Financial Volatility Prediction in International Stock Market
Feature Engineering; Financial Volatility Prediction; International Stock Market; Long Short-Term Memory (LSTM); Root Mean Squared Error (RMSE); Unmanned AI
This study digs into the area of unmanned artificial intelligence (AI) for financial volatility prediction in the worldwide stock market, delivering unique insights into the deployment of cutting-edge technology to handle the multifarious issues of market dynamics. Our research uses Long Short-Term Memory (LSTM) networks as the AI model of choice, showing its usefulness in capturing temporal relationships in financial data by analyzing past stock price data, trading volumes, and a variety of technical indicators. Our findings suggest a potential capacity to reliably predict financial market volatility after extensive data pretreatment, feature engineering, and model training. A powerful instrument for investors, fund managers, and financial institutions to make better informed and accurate investment choices, the model's low Root Mean Squared Error (RMSE) and high (R2) values highlight its practical usefulness. Beyond the purely technical, our study considers the ethical, regulatory, risk reduction, and optimization implications for the financial sector. Financial decision-making and risk management are being transformed by the increasingly globalized market environment, and the results given here provide a concrete roadmap towards the appropriate integration of unmanned AI systems. 2024 IEEE.
Boggavarapu S.; Ramkumar G.; Gedamkar P.R.; Kaneria A.; Pundir S.; Selvameena R.
5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 16-20.
Boggavarapu S., University of Cumberlands, Prospect Heights, IL, United States; Ramkumar G., Christ University, School of Commerce, Finance and Accountancy, Department of Commerce, Bengaluru, India; Gedamkar P.R., Dr. VishwanathKarad MIT World Peace University, Department of MBA, Maharashtra, Pune, India; Kaneria A., Sri Balaji University, Balaji Institute of Technology & Management, Maharashtra, Pune, India; Pundir S., Graphic Era Deemed to be University, Department of Computer Science Engineering, Uttrakhand, Dehradun, India; Selvameena R., Dr.M.G.R Educational and Research Institute, Department of Computer Science and Engineering, Maduravoyal, Chennai, 600095, India
Deep Learning Approaches for Environmental Monitoring in Smart Cities
Air pollution; Deep learning; Environmental monitoring; Smart cities
It introduces a novel integrated environmental monitoring system capable of doing on-the-go measurements. In metropolitan settings, air pollution is one of the most serious environmental threats to human health. The widespread use of automobiles, emissions from manufacturing processes, and the use of fossil fuels for propulsion and power generation have all contributed to this issue. Air quality predictions in smart cities may now be made using deep learning methods, thanks to the widespread adoption of these tools and their continued rapid growth. Particulate Matter (PM) with a width of less than 2.5 m (PM2.5) is one of the most perilous kinds of air pollution. To anticipate the hourly gauge of PM2.5 focus in Delhi, India, we utilized verifiable information of poisons, meteorological information, and PM2.5 fixation in the adjoining stations to make a spatial-worldly element for our CNN-LSTM-based deep learning arrangement. According to our experiments, our 'hybrid CNN-LSTM multivariate' method outperforms all of the above conventional models and allows for more precise predictions. 2024 IEEE.
Latha Soundarraj P.; Jamadar V.M.; Rappai S.; Alkhafaji M.A.; Shareef A.M.; Zearah S.A.
Latha Soundarraj P., School of Business & Management - Mba, Christ University, Maharashtra, Pune, 412112, India; Jamadar V.M., Dr. DaulatraoAher College of Engineering, Department of Mechanical Engineering, Satara, Maharashtra, Karad, 415124, India; Rappai S., Kristu Jayanti College (Autonomous), Department of Computer Science, Kothanur, K. Narayanapura, Karnataka, Bangalore, 560077, India; Alkhafaji M.A., College of Engineering Technology, National University of Science And Technology, DhiQar, Iraq; Shareef A.M., National University of Science And Technology, DhiQar, Iraq; Zearah S.A., Al-ayen University, Scientific Research Center, Thi-Qar, Iraq
Revolutionizing Arrhythmia Classification: Unleashing the Power of Machine Learning and Data Amplification for Precision Healthcare
Arrhythmia Classification; Diagnostic Accuracy; Electrocardiogram (ECG) Signals; Healthcare Analytics; Machine Learning Models; Principal Component Analysis (PCA); Random Oversampling
This paper presents a comprehensive exploration of arrhythmia classification using machine learning techniques applied to electrocardiogram (ECG) signals. The study delves into the development and evaluation of diverse models, including K-Nearest Neighbors, Logistic Regression, Decision Tree Classifier, Linear and Kernelized Support Vector Machines, and Random Forest. The models undergo rigorous analysis, emphasizing precision and recall due to the categorical nature of the dependent variable. To enhance model robustness and address class imbalances, Principal Component Analysis (PCA) and Random Oversampling are employed. The results highlight the effectiveness of the Kernelized SVM with PCA, achieving a remarkable accuracy of 99.52%. Additionally, the paper discusses the positive impact of feature reduction and oversampling on model performance. The study concludes with insights into the significance of PCA and Random Oversampling in refining arrhythmia classification models, offering potential avenues for future research in healthcare analytics. 2024 IEEE.
Arpan; Singh M.; Garg P.; Srivastava S.; Saggu A.K.
Proceedings - 2024 6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024, pp. 516-522.
https://doi.org/10.1109/CCICT62777.2024.00086 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199920068&doi=10.1109%2fCCICT62777.2024.00086&partnerID=40&md5=6b099fe0082abb54acab81aeb0634aa7
Arpan, School of Sciences, Christ Deemed to be university, Delhi NCR, India; Singh M., School of Sciences, Christ Deemed to be university, Delhi NCR, India; Garg P., SAITM, Delhi NCR, Gurgoan, India; Srivastava S., School of Sciences, Christ Deemed to be university, Delhi NCR, India; Saggu A.K., School of Sciences, Christ Deemed to be university, Delhi NCR, India
Depiction ofNifty Midcap Index Efficiency Using ARIMA
ARIMA; Market efficiency; Nifty Midcap Index; Volatility
In recent years, the desirability of midcaps in Indian stock markets has received considerable attention from researchers, academicians, and financial analysts due to expectation of multi-bagger returns. The present study is undertaken to determine the market efficiency of Indian stock market using Nifty Midcap Index at High Frequency. The market efficiency of Nifty Midcap Index is determined using ARIMA technique. The fitted ARIMA model had a MASE value close to one. Hence, the findings suggest that the Nifty Midcap Index is inefficient. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-1, pp. 123-131.
Ashok Kumar B., Government First Grade College Vemagal, Kolar, 536102, India; Balaji L., Christ University, Bengaluru, 560029, India
Security and Privacy in Internet of Things (IoT) Environments
Data Flow Analysis; Empirical Experiments; Homomorphic Encryption; Internet of Things (IoT); IoT Ecosystem; Privacy Preservation Techniques; Security Modeling; Threat Intelligence Sharing; User-Centric Design
Although the proliferation of IoT devices has led to unparalleled ease of use and accessibility, it has also raised serious privacy and safety issues. Using a systematic approach that incorporates security and privacy modelling, data analysis, and empirical trials, this study provides a deep dive into the topic of IoT security and privacy. Our results show how crucial it is to take precautions against 'Information Disclosure' by using strong encryption and authorization protocols. The need to protect against 'Unencrypted Data' vulnerabilities is further emphasized by vulnerability analysis. Encryption (AES-256) and other access control rules fare very well in the assessment of security systems. Furthermore, 'Homomorphic Encryption' is identified as a potential strategy to protecting user privacy while retaining data usefulness based on our review of privacy preservation strategies. A more secure and privacyaware IoT environment may be fostered thanks to the findings of this study, which have ramifications for the industry, government, consumers, and academics. Addressing the ever-evolving security and privacy issues in the IoT will need a future focus on cutting-edge security mechanisms, privacy-preserving technology, regulatory compliance, user-centric design, multidisciplinary cooperation, and threat intelligence sharing. 2024 IEEE.
Bhoomadevi A.; Soundarraj P.L.; Gupta V.; Kumaravel S.K.; Deivasigamani S.; Kumar A.
Bhoomadevi A., Amity Institute of Public Health & Hospital Administration, Amity University, Noida, 201303, India; Soundarraj P.L., School of Business And Management, Christ University, Lavasa, Maharashtra, Pune, 412112, India; Gupta V., Mahatma Gandhi Kashi Vidyapith, Department of Commerce, Varanasi, India; Kumaravel S.K., Mathematics, Vel Tech Rangarajan Dr. Sagunthala R And D Institute of Science And Technology, TamilNadu, Chennai, India; Deivasigamani S., Digipulse Technologies Inc, 811 Church Road, Suite 169, 08002, NJ, United States; Kumar A., Swami Vivekanand Subharti University, Department of Computer Application Faculty of Science, Meerut, India