CeLaTis: A Large Scale Multimodal Dataset with Deep Region Network to Diagnose Cervical Cancer
subject: cervical cancer; colposcope image; dataset; Deep learning; segmentation
description: Cervical cancer is a leading cause of mortality in third world countries. Although there are multiple ways of screening cervical cancer, colposcope image analysis is considered to be standard routine method of diagnosis. Due to factors like lack of skilled personnel and interobserver variability, there is a need for automated diagnostic support for cervical cancer. However, artificial intelligence solutions for medical image analysis done through deep and machine learning models require high quality, non-erroneous and sufficient amount of data. Owing to the lack of such established benchmark datasets for the colposcope images, this work aims at establishing a standard benchmark multi state colposcope image dataset that also contains clinical findings pertaining to each case. In order to establish the quality of the images, mask R-CNN method is used for segmenting the images. Subsequently, a series of IMAGENet pretrained deep learning models are deployed on the dataset to evaluate the performance. The dataset will be made available upon request for strictly research purposes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
creator: Mukku L.; Thomas J.
source: Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 154-163.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303164812-0
format: Online
language: English
type: Conference paper
coverage: Mukku L., CHRIST (Deemed to be University), Bangalore, India; Thomas J., CHRIST (Deemed to be University), Bangalore, India

A Way Towards Next-Gen Networking System for the Development of 6G Communication System
subject: 6G; AI; challenges; communication services; core services; design plan; networking; performance indices; solutions; supporting technologies
description: In this talk, the advancements announced by sixth-generation mobile communication (6G) as compared to the earlier fifth-generation (5G) system are carefully examined. The analysis, based in existing academic works, underscores the goal of improving diverse communication aims across various services. This study finds five crucial 6G core services designed to meet distinct goal requirements. To explain these services thoroughly, the framework presents two central features and delineates eight significant performance indices (KPIs). Furthermore, a thorough study of supporting technologies is performed to meet the stated KPIs. A unified 6G design is suggested, imagined as a combination of these supporting technologies. This design plan is then explained by the lens of five prototype application situations. Subsequently, possible challenges contained in the developing track of the 6G network technology are carefully discussed, followed by suggested solutions. The debate ends in an exhaustive examination of possibilities within the 6G world, seeking to provide a strategy plan for future research efforts. 2024 IEEE.
creator: Mohan C.; Singh D.P.; Singh K.R.; Divya N.; Ramkumar R.; Abirami S.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 276-281.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Mohan C., Christ University, School of Business and Management (SB and M), Bangalore, India; Singh D.P., Ies Institute of Technology and Management, Ies University, Electronics & Communication Engineering Department, M.P, Bhopal, India; Singh K.R., Karpagam Academy of Higher Education, Department of Computer Science, TN, India; Divya N., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Civil Engineering (CE), TN, India; Ramkumar R., Bannari Amman Institute of Technology, Department of Ece, Tamil Nadu, Sathyamangalam, India; Abirami S., Hindustan Institute of Technology and Science, Department of Information Technology, Tamil Nadu, Kelambakkam, India

Application of Regression Analysis of Student Failure Rate
subject: Machine learning; Regression; Student failure
description: The education sector has been rapidly growing and is currently facing several challenges. One such challenge is identifying students who are at risk of failing, as this can help educators provide targeted interventions to improve student performance. Machine learning models have been developed to predict the probability of student failure based on various student performance metrics to address this issue. In this paper, we present a regression-based model that predicts the probability of student failure using student performance metrics such as attendance, previous academic performance, and demographic information. The model was trained on a dataset of students and achieved high accuracy in predicting the probability of student failure. While the model performs well in predicting the probability of student failure, there is always room for improvement. Possible enhancements to the model include feature engineering, ensemble learning, hyperparameter tuning, deep learning, and interpretability. These enhancements can improve the models accuracy, stability, and transparency, leading to better predictions and targeted interventions for at-risk students. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Sybol S.S.; Srivasatava S.; Sharma S.
source: Lecture Notes in Networks and Systems, Vol-994 LNNS, pp. 897-910.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981973179-4
format: Online
language: English
type: Conference paper
coverage: Sybol S.S., CHRIST (Deemed to be University), Delhi NCR, India; Srivasatava S., CHRIST (Deemed to be University), Delhi NCR, India; Sharma S., Uttaranchal University, Dehradun, India

The Integration of HMS using IOMT and CE Through ANFIS
subject: ANFIS; cloud; healthcare; heart disease; IoMT; monitoring system
description: Advances in the IoMT-enabled cloud computing and interactive applications provide a basis for reconsidering the landscape for delivery of healthcare services. Even though the IoMT-cloud-based systems monitor patients remotely, it fails to take into account the sustainability of the healthcare systems. The paper presents the integrated framework of green healthcare under the umbrella of unique technology to enhance user interactivity. Our system is user-friendly, considering scalability and performance for both patients and doctors. Patients can send their health data to the doctor in real time with the help of the wearable sensor. We propose that in the presence of Hierarchical Clustering Algorithms and adaptive neuo-fuzzy inference system (ANFIS) for identification and analysis of the data, the applied solutions could enhance the healthcare experience interaction among all the stakeholders. 2024 IEEE.
creator: Sreelakshmy R.; Donald C.; Rajaram G.; Putta P.; Thajeel S.K.; Hameed H.; Zaidan D.T.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 445-449.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Sreelakshmy R., Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology, Department of Electronics and Communication Engineering, Chennai, India; Donald C., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, India; Rajaram G., Saveetha Institute of Medical and Technical Sciences, Simats, Saveetha School of Engineering, Department of Computer Science and Engineering, Chennai, India; Putta P., Mohan Babu University, Department of Mechanical Engineering, Andhra Pradesh, Tirupati, India; Thajeel S.K., National University of Science and Technology, College of Technical Engineering, Dhi Qar, 64001, Iraq; Hameed H., Al-Farahidi University, Baghdad, Iraq; Zaidan D.T., Kut University College, Department of Laser and Optical, Electronics Engineering, Al Kut, Iraq

Revolutionising Tumour Diagnosis: How Clinical Application of Artificial Intelligence and Machine Learning Enhances Accuracy and Efficiency
subject: Artificial intelligence; Machine learning; Tumour diagnosis
description: This research paper examines the transformative influence of Artificial Intelligence (AI) and Machine Learning (ML) on tumour diagnosis within clinical settings. The advent of AI and ML technologies has revolutionised the field of oncology, offering the unprecedented potential for more accurate, timely, and personalised cancer detection. By leveraging vast datasets of medical images, genomic information, and patient records, these intelligent systems enable the early identification of tumours, classification of cancer types, and prediction of patient outcomes with remarkable precision. This paper delves into the mechanisms through which AI and ML algorithms analyse complex data, highlighting their ability to detect subtle patterns and anomalies that may escape human perception. Moreover, we examine the successful integration of these technologies into clinical workflows, their potential to reduce diagnostic errors, and the implications for patient care and outcomes. As AI and ML continue to emerge, the synergy between technology and clinical expertise promises to enhance tumour diagnosis, ultimately contributing to more effective and personalised cancer treatments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Malathy V.; Pasupulla A.P.; Srivastava D.; Hymavathi J.; Pandian P.M.; Acharjee P.B.
source: Smart Innovation, Systems and Technologies, Vol-390, pp. 141-151.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 21903018; ISBN: 978-981972715-5
format: Online
language: English
type: Conference paper
coverage: Malathy V., School of Engineering, SR University, Telangana, Warangal, India; Pasupulla A.P., Oral and Maxillofacial Pathology, School of Medicine, College of Health Sciences, Wachemo University, Hosanna, Ethiopia; Srivastava D., Department of Biotechnology and Chemical Engineering, Manipal University, Jaipur, India; Hymavathi J., Department of Computer Science and Engineering, KLEF, Andhra Pradesh, Vaddeswaram, India; Pandian P.M., Department of Chemistry, Saveetha Engineering College, Chennai, Thandalam, India; Acharjee P.B., Computer Science, CHRIST University Pune Lavasa Campus, Lavasa, India

Analysing Crypto Trends: Unveiling Ethereum and Bitcoin Price Forecasts Through Analytics-Driven Weighted Moving Averages
description: This research meticulously analyses the performance dynamics of two paramount cryptocurrencies, Bitcoin and Ethereum, over 2,682 observations. Preliminary findings indicate a near alignment in the mean returns of both assets, with Ethereum marginally outperforming Bitcoin. Interestingly, Ethereums superior returns are accompanied by heightened volatility, underlined by its more significant standard deviation. Both cryptocurrencies manifest negative skewness, hinting at a proclivity for negative returns, with Bitcoin showing a sharper skew. Their pronounced kurtosis values attest to the potential for extreme price swings. Regarding forecasting efficacy, the Weighted Moving Average (WMA) method emerges as superior for both assets, yielding the most accurate predictions. At the same time, the Exponential Moving Average (EMA) demonstrates the highest forecast errors. Further, the Relative Strength Index (RSI) evaluation suggests Ethereum may be oversold, alluding to potential investment opportunities. In contrast, Bitcoin, with its mid-range RSI, resides in a neutral zone devoid of clear market signals. The findings shed light on the nuanced performance and forecasting landscape of these leading cryptocurrencies, offering pivotal insights for potential investors. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
creator: Dhaku C.R.; Shivarudrasetty D.; Arumugam S.K.; Uma V.R.
source: Lecture Notes in Networks and Systems, Vol-1048 LNNS, pp. 450-457.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303164649-2
format: Online
language: English
type: Conference paper
coverage: Dhaku C.R., School of Commerce, Finance and Accountancy, Christ University, Bengaluru, India; Shivarudrasetty D., School of Commerce, Finance and Accountancy, Christ University, Bengaluru, India, Department of Commerce and Management, MLA Academy of Higher Learning, Bengaluru, India; Arumugam S.K., Department of Professional Studies, Christ University, Bengaluru, India; Uma V.R., School of Commerce, Finance and Accountancy, Christ University, Bengaluru, India

A Structured Design of 5G Based Assisted MTC System using Mission-Critical System
subject: 5G; connectivity network; D2D linkages; Internet of Things; IoT applications; LTE networks; machine-type telecommunications; mc MTC; urban communications; vehicular connection
description: Critical machine-type relationships (mc MTC) has become known as a crucial element within the Business Internet of Things (IoT) ecosystem, showcasing lucrative opportunities in disciplines like autonomous vehicles, intelligent energy/smart grid control, security services, while advanced wearable applications. As the fifth generation of cell phones unfolds, the changing environment of mc MTC puts diverse demands on the underlying technology. These demands embrace standards for low power usage, heightened dependability, and minimal delay connection. In answer to these challenges, recent versions and current advances in Long-Term Evolution (LTE networks) systems have added features that promote cost-effective solutions, increase coverage, reduce delay, and improve reliability for devices with different movement levels. This study focuses on assessing the impacts on mc MTC effectiveness in a connectivity network for 5G with varying user and equipment accessibility, influenced by a variety of movements. According to the study, integrating other modes of contact, such as quadcopter assistance and device-to-device linkages a voice, contributes a crucial role in achieving the strict demands of mc MTC programs across diverse situations that tell which includes industrial automation, vehicular connection, and urban messages. Significantly, our results confirm gains of as much as forty per cent in link availability and dependability when applying nearby connections as opposed. 2024 IEEE.
creator: Mohan C.; Agrawal S.; Francis G.A.; Sheril S.; Al-Farouni M.; Rosaline S.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 354-358.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Mohan C., Christ University, School of Business and Management, Bangalore, India; Agrawal S., Ies College of Technology, Department of Computer Science & Engineering (Cs and E), M.P, Bhopal, India; Francis G.A., Karpagam College of Engineering, Department of Electronics and Communication Engineering, TN, Coimbatore, India; Sheril S., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Mechanical Engineering, India; Al-Farouni M., The Islamic University of Al Diwaniyah, College of Technical Engineering, Department of Computers Techniques Engineering, Al Diwaniyah, Iraq; Rosaline S., R.M.K. Engineering College, Department of Electronics and Communication Engineering, Tamil Nadu, Kavaraipettai, India

Popularity Prediction of Online Social Media Content: A Bibliometric Analysis
subject: Bibliometric; Online social networks; Popularity; Prediction; VOS viewer
description: An online social network is a platform that enables individuals to interact with others who have similar backgrounds, preferences, activities, and associations. The number of features available and the format of each online social network range widely. Users of online social networks, such as Twitter, Instagram, Flicker, and Pinterest, have increased dramatically in recent years. Content sharing is the most popular feature of online social networks, used by both specific users and big enterprises. This study has used bibliometric methods to analyze the growth of the social media popularity prediction on online social network content from 2013 to 2022. The publications have been extracted from the dimensions database, and the VOS viewer software was used to visualize research patterns. The finding provides valuable information on the publication year, authors, authors country, authors organizational affiliations, publishing journals, etc. Based on the findings of this analysis, researchers will be able to design their studies better and add more insights into their empirical studies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Anand G.; Srivastava S.; Shandilya A.; Tipathi A.
source: Lecture Notes in Networks and Systems, Vol-994 LNNS, pp. 311-325.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981973179-4
format: Online
language: English
type: Conference paper
coverage: Anand G., CHRIST (Deemed to be University), Bangalore, India; Srivastava S., CHRIST (Deemed to be University), Bangalore, India; Shandilya A., CHRIST (Deemed to be University), Bangalore, India; Tipathi A., Manipal University, Jaipur, India

An Intelligent Model forPost Covid Hearing Loss
subject: covid-19; mathematical model; ordinary differential equations; stability; Sudden sensorineural hearing loss (SSHL)
description: Several viral infections tend to cause Sudden Sensorineural Hearing Loss (SSNHL) in humans. Covid-19 being a viral disease could also cause hearing deficiencies in people as a side effect. There have been pieces of evidence from various case studies wherein covid infected patients have reported to be suffering from sudden sensorineural hearing loss. The main objective of this study is to inspect the phenomenon and treatment of SSNHL in post-COVID-19 patients. This study proposes a mathematical model of hearing loss as a consequence of covid-19 infection using ordinary differential equations. The solutions obtained for the model are established to be non-negative and bounded. The disease-free equilibrium, endemic equilibrium and basic reproductive number have been obtained for the model which helps analyse the models trend through stability analysis. Moreover, numerical simulations have been performedfor validating the obtained theoretical results. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
creator: Poonia R.C.; Aarthi S.; Samanta D.
source: Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 39-46.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303164812-0
format: Online
language: English
type: Conference paper
coverage: Poonia R.C., CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Aarthi S., CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Samanta D., Department of Computing and Information Technologies, RIT Kosovo (A.U.K), Rochester Institute of Technology - RIT Global Campus, Prishtina, Kosovo, 10000

AI Healthcare Industry in Life Science Industry: A Perspective View
subject: AI; communication; healthcare; innovation system; life science; policymakers; resources; socio-technical; technology; TIS approach
description: The main goal of this study is to look at how well the innovation system for AI healthcare technology is working in the life science business and find things that are getting in the way of progress. A lot of different types of research were used for this study. It combines both quantitative and qualitative data from tertiary studies, business-related written sources, and conversations with 21 experts and 25 life science management leaders to get new ideas. The results make it clear that innovation system performance is being held back by a lack of resources and poor communication from top healthcare experts about what they need to improve healthcare with AI technology innovations. The study says that to deal with these problems, policymakers need to make changes that increase the resources that are available and come up with clear goals and visions for how AI technology can improve healthcare. Using the socio-technical technological advancement System (TIS) approach in the healthcare setting, the study adds to our knowledge of how the innovation system works and how different parts of it affect each other. Overall, this study throws light on the complicated ways that innovation works in the life science field. It gives lawmakers, industry workers, and other interested parties useful information for pushing AI healthcare technology forward in a sociotechnical framework. 2024 IEEE.
creator: Sakthivel S.; Reddy M.U.; Arunkumar B.; Srisainath R.; Ashok J.; Desai P.M.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 5-11.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Sakthivel S., Nehru Institute of Engineering and Technology, Department of Electrical and Electronic Engineering, Tamil Nadu, T.M.Palayam, India; Reddy M.U., Ies College of Technology, Department of Computer Science & Engineering, M.P, Bhopal, 462044, India; Arunkumar B., Karpagam Academy of Higher Education, Coimbatore, 641021, India; Srisainath R., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Mechanical Engineering, Chennai, India; Ashok J., Christ University, Bangalore, India; Desai P.M., Cmr Technical Campus, Hyderabad, Medchal, 501401, India

Exploring Ethical Considerations: Privacy and Accountability in Conversational Agents like ChatGPT
subject: Artificial intelligence; ChatGPT
description: In recent years, advances in artificial intelligence (AI) and machine learning have transformed the landscape of scientific study. Out of all of these, chatbot technology has come a long way in the last few years, especially since ChatGPT became a well-known artificial intelligence language model. This comprehensive review investigates ChatGPT's background, applications, primary challenges, and possible future advancements. We first look at its history, progress, and fundamental technology before delving into its many applications in customer service, health care, and education. We also discuss potential countermeasures and highlight the major challenges that ChatGPT faces, including data biases, moral dilemmas, and security threats. Finally, we go over our plans for ChatGPT's future, outlining areas that need further research and development, improved human-AI communication, closing the digital gap, and ChatGPT integration with other technologies. This study offers useful information for scholars, developers, and stakeholders interested in the rapidly evolving subject of artificial intelligence-powered conversational bots. This study looks at the ways that ChatGPT has changed scientific research in several domains, such as data processing, developing hypotheses, collaboration, and public outreach. In addition, the paper examines potential limitations and ethical quandaries associated with the use of ChatGPT in research, highlighting the importance of striking a balance between human expertise and AI-assisted innovation. The paper addresses multiple ethical issues with the state of computers today and how ChatGPT can cause people to oppose this notion. This study also has a number of ChatGPT biases and restrictions. It is noteworthy that in a very short period, ChatGPT has garnered significant interest from academics, research, and enterprises, notwithstanding several challenges and ethical issues. The Author(s), under exclusive license to Springer Nature Singapore …
creator: Deshmukh R.; Gaikwad S.B.; Reddy K.S.; Joseph C.; Arnone G.; Kalaivaani P.C.D.
source: Smart Innovation, Systems and Technologies, Vol-390, pp. 193-201.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 21903018; ISBN: 978-981972715-5
format: Online
language: English
type: Conference paper
coverage: Deshmukh R., School of Engineering, SR University, Telangana, Warangal, India; Gaikwad S.B., Management Studies, MET Institute of Management, Maharashtra, Mumbai, India; Reddy K.S., Department of Commerce and Management, International Institute of Business Studies, Bengaluru, India; Joseph C., Department: School of Law, University With City and State: Christ (Deemed to Be University), Bengaluru, India; Arnone G., Department of Private and Economic Law, Vrije Universiteit Brussel, Bruxelles, Belgium; Kalaivaani P.C.D., Department of CSE, Kongu Engineering College, Perundurai, 638060, India

Harnessing Medical Databases and Data Mining in the Big Data Era: Advancements and Applications in Healthcare
subject: Big data; Data mining; Medical database
description: In the contemporary period of Big Data, the healthcare industry is witnessing a transformative paradigm shift, propelled by the convergence of medical databases and data mining technology. This research paper delves into the multifaceted application of this synergy, offering a comprehensive overview of its implications and opportunities. With the exponential growth of healthcare data, the utilisation of medical databases serves as the bedrock for data mining techniques, fostering critical advancements in diagnosis, treatment, and patient care. Through this research, we explore the integration of electronic health records, genomic data, and clinical databases, unveiling new dimensions of predictive analytics, patient profiling, and disease monitoring. Moreover, we assess the ethical and privacy concerns entailed in this data-rich landscape, emphasising the need for robust governance and security measures. Our paper encapsulates the evolving landscape of health care, demonstrating the immense potential and the ethical responsibilities accompanying this groundbreaking merger of technology and medicine in the period of Big Data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Ranjith Kumar G.; Nassa V.K.; Manikandan K.; Suribabu P.; Pandian P.M.; Acharjee P.B.
source: Smart Innovation, Systems and Technologies, Vol-390, pp. 153-165.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 21903018; ISBN: 978-981972715-5
format: Online
language: English
type: Conference paper
coverage: Ranjith Kumar G., School of Computer Science and Artificial Intelligence, SR University, Telangana, Warangal, India; Nassa V.K., Department of Information Communication Technology (ICT), Tecnia Institute of Advanced Studies(Delhi), Affiliated With Guru Gobind Singh Indraprastha University, Delhi, India; Manikandan K., Department of Pharmaceutical Analysis, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, SRM Nagar, 603203, India; Suribabu P., Computer Science and Engineering, JNTU Hyderabad, Hyderabad, India; Pandian P.M., Department of Chemistry, Saveetha Engineering College, Chennai, Thandalam, India; Acharjee P.B., Computer Science, CHRIST University Pune Lavasa Campus, Lavasa, India

The Various Challenges Involved in Sensor Based Cloud System to Protect the Data and to Avoid Attacks: A Technical Review
subject: Cloud Computing; Data Confidentiality; Data Integrity, Access Control; Security Model; Sensor Network
description: In these studies, we introduce a unique protection framework for the integration of Wireless Sensor Networks (WSN) with cloud computing, aimed closer to enhancing statistics-centric programs consisting of far-flung healthcare structures. The framework's cornerstone is a robust, bendy safety version that ensures immoderate-degree information confidentiality, integrity, and terrific-grained get proper of access to control, addressing the important protection demanding situations in WSN-Cloud integration. By the use of a hybrid encryption mechanism that mixes the strengths of symmetric and uneven encryption techniques, our method gives a entire safety answer that protects information during transmission and garage. Furthermore, the version includes an efficient key manipulate gadget, facilitating the dynamic era and relaxed distribution of encryption keys. This contemporary framework is designed to mitigate common safety threats, such as Man-in-the-Middle (MITM) and Denial of Service (DoS) attacks, even as preserving the overall performance and standard performance of the blanketed gadget. Our research offers a massive contribution to securing statistics-centric packages in WSN-Cloud ecosystems, making sure dependable and comfortable facts verbal exchange and get right of entry to for a way off healthcare programs and past. 2024 IEEE.
creator: Sharon Sophia J.; Thakur G.; Kapoor M.; Bhanu S.; Shnain A.H.; Bhavya K.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 924-928.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Sharon Sophia J., Christ (Deemed to Be) University, School of Business and Management, Bangalore Yeshwanthpur Campus, India; Thakur G., Uttaranchal University, Uttaranchal Institute of Technology, Department of Civil Engineering, Dehradun, 248007, India; Kapoor M., Chitkara University, Centre of Research Impact and Outreach, Punjab, Rajpura, 140401, India; Bhanu S., Cmr College of Engineering & Technology, Department of CSE(AI&ML), India; Shnain A.H., The Islamic University, Najaf, Iraq; Bhavya K., N.M.A.M Institute of Technology (Nitte Deemed to Be University), Department of Mathematics, Nitte, India

Enhanced Learning in IoT-Based Intelligent Plant Irrigation System for Optimal Growth and Water Management
subject: ANN; CNN; Machine Learning; Precision Agriculture; Smart Irrigation; SVM
description: This research looked at the transformative potential of cutting-edge machine learning algorithms in various areas of precision agriculture, with an emphasis on enhancing smart irrigation systems for onion farming. Using a vast sensor network and real-time monitoring, we investigated the performance of CNN, ANN, and SVM, three well-known machine learning algorithms. After extensive testing and investigation, our results reveal that CNN beats ANN and SVM in terms of outstanding accuracy in predicting plant water requirements. Because of CNN's superior predictive powers, our intelligent irrigation system maintains perfect soil conditions, resulting in increased agricultural yields and resource savings. The study's findings have important implications for modern agriculture, paving the way for data-driven, sustainable agricultural methods that address global concerns such as food security and environmental sustainability. As we approach the era of smart agriculture, our research demonstrates how technology has the potential to alter crop farming and aid in the development of a more resilient and successful agricultural industry. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
creator: Singh J.; Shelke N.A.; Hasan D.S.; Sajid M.; Alsahlanee A.T.R.; Upreti K.
source: Lecture Notes in Networks and Systems, Vol-1050 LNNS, pp. 231-240.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303164846-5
format: Online
language: English
type: Conference paper
coverage: Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Shelke N.A., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Hasan D.S., Department of Computer Science and Information Technology, University of Salahaddin-Erbil, Erbil, Iraq; Sajid M., Department of Computer Science, Aligarh Muslim University, Aligarh, India; Alsahlanee A.T.R., Development and Continuous Education Center, University of Thi-Qar, Thi-Qar, Iraq; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Ghaziabad, India

Exploring the Opportunities of AI Integral with DL and ML Models in Financial and Accounting Systems
subject: accounting; case studies; empirical analysis; ethical considerations; Explainable AI; financial decisionmaking; interpretability; regulatory compliance; transparency; XAI techniques
description: With the integration of artificial intelligence (AI), today's fast financial landscape increasingly promises the most efficient and accurate processes for decision-making in accounting practices. On the other hand, the opacity of models represents a truly difficult challenge, given that transparency and accountability are key for using AI in making financial decisions. This is a research paper that focuses on the explanation of an XAI model application as a way of improving transparency in financial decision-making within the accounting field. The paper begins by outlining how transparency is important and opens the room for trust and understanding in the process of financial decision-making. Traditional black-box AI models, although able to provide remarkable predictions, usually exhibit low interpretability; this entails that stakeholders may have a small degree of understanding regarding the rationale behind the decisions. This provides a cloudy appearance not to hamper trust and supports compliance with regulatory standards like GAAP (Generally Accepted Accounting Principles) and IFRS (International Financial Reporting Standards). The proposed work applies to the accounting domain and brings about some of the different XAI techniques that are designed under this domain. The following techniques aim at demystifying the AI algorithms for effective AI stakeholders' understanding of the model predictions and underlying decision-making processes. 2024 IEEE.
creator: Mishra R.K.; Srivastava S.; Singh S.; Upadhyay M.K.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 999-1003.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Mishra R.K., Christ University, Bengaluru, India; Srivastava S., Christ University, Bengaluru, India; Singh S., Christ University, Bengaluru, India; Upadhyay M.K., St. Xavier's College of Management & Technology, Patna, India

Comparative Analysis of Predictive Models for Customer Churn Prediction in the Telecommunication Industry
subject: Attrition; customer segmentation; retention
description: To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. The results indicate that the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition. 2024 IEEE.
creator: Christopher D.; Anand G.
source: Proceedings of International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024, pp. 534-539.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036684-6
format: Online
language: English
type: Conference paper
coverage: Christopher D., Christ (Deemed To Be University), Delhi NCR, India; Anand G., Christ (Deemed To Be University), Delhi NCR, India

Exploring the Adoption Readiness of the Indian Generation for Social Media Payments: An In-Depth Analysis of WhatsApp Payments
subject: Extended UTAUT2; FinTech; Social media payment; WhatsApp payment
description: Advancements in technologies always get higher acceptance among people. Regarding payment technologies, integrating payment facility in the Social Media platform are considered a second-generation payment technology. With the introduction of Hike wallets and WhatsApp payment, unprecedented opportunities are available to the users. In India, with the introduction of WhatsApp on November 2020, the users of FinTech got opened a gateway to social media payment. Social Media payments are considered easy and convenient, but is the Indian generation, especially people born in the internet phase (Gen Y and Gen Z), ready to adopt WhatsApp payment. The current study was done to investigate the elements that contribute to the acceptance and use of the WhatsApp payment service in India. To attain this objective, we used an extended UTAUT2 model with the moderating effect of generation. The data was gathered from 265 respondents and analyzed using the PLS-SEM method. The results of the study outlined that Gen Z is strengthening the moderating effect only between the facilitating conditions of the users and the actual usage of WhatsApp payment. The practical implications and directions for the further research are mentioned in the study. ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.
creator: Joseph D.; Girish S.
source: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Vol-517 LNICST, pp. 1-15.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 18678211; ISBN: 978-303166043-6
format: Online
language: English
type: Conference paper
coverage: Joseph D., Department of Commerce, CHRIST (Deemed to be University), Bangalore, India; Girish S., Department of Commerce, CHRIST (Deemed to be University), Bangalore, India

Optimization of Friction Stir Welding of AlCu Butt Joint Using Taguchi Method
subject: Friction stir welding; Hardened OHNS steel tool; Impact load
description: In this work, the 5mm thickness of base metals AA6101 and C11000 was welded using a hardened OHNS steel tool by FSW mechanism. The Taguchi method involves the optimization of welding mechanism variables tool rotation speed (rpm), feed rate (mm/min), and tool offset (mm) to gain extremely rigid joints. The ANOVA reveals the percentage contribution of the three welding mechanism variables can be examined. From the Taguchi design of optimization technique, at 1000rpm, 40mm/min, andtool offset towards softer metal will possess maximum impact load. The tools rotating speed produced the greatest contribution to the impact load. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Kumar J.P.; Raj A.
source: Lecture Notes in Mechanical Engineering, pp. 53-61.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 21954356; ISBN: 978-981973653-9
format: Online
language: English
type: Conference paper
coverage: 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

The Impactful Role of ML Algo in the Field of Enactment Nostrum: An Intensive/Deep Review
subject: Machine Learning; Machine Translation; Natural Language Processing; Support Vector Machines; Translation medicine
description: Machine translation (MT) research has explored a variety of models, including statistical machine translation (SMT), rule-based machine translation (RBMT), and hybrid approaches. Hybrid MT systems aim to improve translation quality by using the strengths of different models. However, the complex set of functions associated with MT systems is still difficult to understand and optimize. This instant study propose an approach based on ML with respect to hybrid MT that addresses these issues by automatically interpreting and weighting features using ML tools. This research framework includes a classification approach to classify and compare translations from multiple black-box A system that uses ML classifiers trained on feature vectors derived from natural language processing tools. This study presents a method to train and use an SVM-based classifier to generate hybrid interpretations. The test results for English-Chinese pairs show the potential of this research approach to improve translation quality. The proposed framework is a simple and efficient way to combine different MT systems, improving translation results without manual intervention. 2024 IEEE.
creator: Narasimharao J.; Hariharan R.; Sudha E.; Bisht D.; Mohan E.; Almusawi M.; Kumar N.S.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1407-1411.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Narasimharao J., Cmr Technical Campus, Department of Cse, Telangana, Hyderabad, India; Hariharan R., Christ (Deemed to Be University), School of Business and Management, Yeshwantpur, Bangalore, India; Sudha E., Christ (Deemed to Be University), Department of Commerce, Yeshwantpur Campus, Bangalore, India; Bisht D., Uttaranchal Institute of Management, Uttaranchal University, Department of Management, Dehradun, 248007, India; Mohan E., Simats, Saveetha School of Engineering, Department of Ece, Tamilnadu, Chennai, India; Almusawi M., The Islamic University, Najaf, Iraq; Kumar N.S., Srm Institute of Science & Technology, Department of Computer Science & Engineering, Chennai, Vadapalani, India

The Development of ID System for Detecting Attacks in WSN Through Ontology Method and its Strategy
subject: Cyberattacks; Internet of Things (IoT); Intrusion Detection System (IDS); ontology; wireless sensor networks
description: Cybercriminals are becoming increasingly targeted by the rapid expansion of the Internet of Things (IoT), leading to an increase in cyberattacks targeting IoT devices and their communication channels These attacks, if failure to detect may result in significant service disruption, financial loss or damage to sensitive data. Real-time intrusion detection is essential to ensure reliability, security and profitability of IoT applications. This study introduces a new intrusion detection system designed for IoT devices that uses deep learning (DL). Utilizing ontology in wireless sensor networks (WSN), this intelligent system detects suspicious activities that pose a threat to connected IoT devices with configuration-neutral design provides ease of use, while the test performance analysis is simulated and real-world. It highlights its strong performance in determining admissions. The effectiveness of the system against many types of attacks such as denial of service, workholes, blackholes, opportunistic service attacks, etc. is confirmed by experimental research and furthermore, the system achieves F1 scores, accuracy and the number of memories. This advanced deep learning intrusion detection system shows great promise to improve IoT network security due to its high detection rate. 2024 IEEE.
creator: Sheela M.S.; Venkateswarlu M.; Donald C.; Kadiri P.; Al-Hameed M.R.; Safi H.; Saadoun O.N.
source: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 391-394.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036016-5
format: Online
language: English
type: Conference paper
coverage: Sheela M.S., Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology, Ece, Chennai, Avadi, India; Venkateswarlu M., Kampala International University, Faculty of Science and Technology, Department of Computing, Western Campus, Uganda; Donald C., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, India; Kadiri P., Mohan Babu University, School of Computing, Department of Aiml, Andhra Pradesh, Tirupati, India; Al-Hameed M.R., National University of Science and Technology, College of Health and Medical Technology, Dhi Qar, 64001, Iraq; Safi H., Al-Farahidi University, Baghdad, Iraq; Saadoun O.N., Al-kunooze University College, Basra, Iraq