Exploring the Opportunities of AI Integral with DL and ML Models in Financial and Accounting Systems
accounting; case studies; empirical analysis; ethical considerations; Explainable AI; financial decisionmaking; interpretability; regulatory compliance; transparency; XAI techniques
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.
Mishra R.K.; Srivastava S.; Singh S.; Upadhyay M.K.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 999-1003.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICACITE60783.2024.10616847 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201811077&doi=10.1109%2fICACITE60783.2024.10616847&partnerID=40&md5=b08cfd72a7ae7de7adb7fbe1717062b2
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
Enhanced Learning in IoT-Based Intelligent Plant Irrigation System for Optimal Growth and Water Management
ANN; CNN; Machine Learning; Precision Agriculture; Smart Irrigation; SVM
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.
Singh J.; Shelke N.A.; Hasan D.S.; Sajid M.; Alsahlanee A.T.R.; Upreti K.
Lecture Notes in Networks and Systems, Vol-1050 LNNS, pp. 231-240.
Springer Science and Business Media Deutschland GmbH
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
The Various Challenges Involved in Sensor Based Cloud System to Protect the Data and to Avoid Attacks: A Technical Review
Cloud Computing; Data Confidentiality; Data Integrity, Access Control; Security Model; Sensor Network
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.
Sharon Sophia J.; Thakur G.; Kapoor M.; Bhanu S.; Shnain A.H.; Bhavya K.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 924-928.
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
Harnessing Medical Databases and Data Mining in the Big Data Era: Advancements and Applications in Healthcare
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.
Ranjith Kumar G.; Nassa V.K.; Manikandan K.; Suribabu P.; Pandian P.M.; Acharjee P.B.
Smart Innovation, Systems and Technologies, Vol-390, pp. 153-165.
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
Exploring Ethical Considerations: Privacy and Accountability in Conversational Agents like ChatGPT
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 Pte Ltd. 2024.
Deshmukh R.; Gaikwad S.B.; Reddy K.S.; Joseph C.; Arnone G.; Kalaivaani P.C.D.
Smart Innovation, Systems and Technologies, Vol-390, pp. 193-201.
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
AI Healthcare Industry in Life Science Industry: A Perspective View
AI; communication; healthcare; innovation system; life science; policymakers; resources; socio-technical; technology; TIS approach
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.
Sakthivel S.; Reddy M.U.; Arunkumar B.; Srisainath R.; Ashok J.; Desai P.M.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 5-11.
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
An Intelligent Model forPost Covid Hearing Loss
covid-19; mathematical model; ordinary differential equations; stability; Sudden sensorineural hearing loss (SSHL)
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.
Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 39-46.
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
Popularity Prediction of Online Social Media Content: A Bibliometric Analysis
Bibliometric; Online social networks; Popularity; Prediction; VOS viewer
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.
Anand G.; Srivastava S.; Shandilya A.; Tipathi A.
Lecture Notes in Networks and Systems, Vol-994 LNNS, pp. 311-325.
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
A Structured Design of 5G Based Assisted MTC System using Mission-Critical System
5G; connectivity network; D2D linkages; Internet of Things; IoT applications; LTE networks; machine-type telecommunications; mc MTC; urban communications; vehicular connection
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.
Mohan C.; Agrawal S.; Francis G.A.; Sheril S.; Al-Farouni M.; Rosaline S.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 354-358.
https://doi.org/10.1109/ICACITE60783.2024.10617249 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201813805&doi=10.1109%2fICACITE60783.2024.10617249&partnerID=40&md5=ce22c8aadeb9996a91cabb88ab983053
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
Analysing Crypto Trends: Unveiling Ethereum and Bitcoin Price Forecasts Through Analytics-Driven Weighted Moving Averages
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.
Dhaku C.R.; Shivarudrasetty D.; Arumugam S.K.; Uma V.R.
Lecture Notes in Networks and Systems, Vol-1048 LNNS, pp. 450-457.
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
Revolutionising Tumour Diagnosis: How Clinical Application of Artificial Intelligence and Machine Learning Enhances Accuracy and Efficiency
Artificial intelligence; Machine learning; Tumour diagnosis
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.
Malathy V.; Pasupulla A.P.; Srivastava D.; Hymavathi J.; Pandian P.M.; Acharjee P.B.
Smart Innovation, Systems and Technologies, Vol-390, pp. 141-151.
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
The Integration of HMS using IOMT and CE Through ANFIS
ANFIS; cloud; healthcare; heart disease; IoMT; monitoring system
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.
Sreelakshmy R.; Donald C.; Rajaram G.; Putta P.; Thajeel S.K.; Hameed H.; Zaidan D.T.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 445-449.
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
Application of Regression Analysis of Student Failure Rate
Machine learning; Regression; Student failure
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.
Lecture Notes in Networks and Systems, Vol-994 LNNS, pp. 897-910.
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
A Way Towards Next-Gen Networking System for the Development of 6G Communication System
6G; AI; challenges; communication services; core services; design plan; networking; performance indices; solutions; supporting technologies
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.
Mohan C.; Singh D.P.; Singh K.R.; Divya N.; Ramkumar R.; Abirami S.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 276-281.
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
CeLaTis: A Large Scale Multimodal Dataset with Deep Region Network to Diagnose Cervical Cancer
cervical cancer; colposcope image; dataset; Deep learning; segmentation
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.
Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 154-163.
Mukku L., CHRIST (Deemed to be University), Bangalore, India; Thomas J., CHRIST (Deemed to be University), Bangalore, India
The Creation of Intelligent Surfaces for the Purpose of Next-Gen Wireless Networks
5G; field programmable gate arrays; line-of-sight; liquid crystal; meta-surfaces; reconfigurable intelligent surfaces; smart reflect-arrays; wireless communications
In preparation of the changing environment of 5th wave (5G) and prospective networks of cells, this study explores new methods to meet challenges that result from the erratic character of the communication medium. Traditionally viewed as a chance factor, the relationship between broadcast radio waves with surrounding factors lowers signal quality in modern times of wireless communications. This paper performs a full literature review on customizable autonomous surfaces (RISs) alongside their uses, stressing the chance for network managers to control radio wave features and minimize environmental spread problems. RISs allow effective control over waveform parameters, including the amplitude, phase, number, and polarization, that without needing complex encoder, decoder, or radio wave processing methods. Leveraging technical developments, metasurfaces, reflectarrays, phase shifts, and liquid crystals appear as potential options for RIS application, placing them as pioneers in the realization of 5G as well as subsequent networks. The study dives into current actions in the RIS-operated mobile phone network area and covers core research issues that deserve exploration to feed unlocking the full promise of RISs at wireless communication networks. 2024 IEEE.
Sowmiya S.; Senthilkumar P.; Kuppuraj T.; Mishra A.; Muralidharan P.; Rosaline S.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 229-234.
https://doi.org/10.1109/ICACITE60783.2024.10616689 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201821543&doi=10.1109%2fICACITE60783.2024.10616689&partnerID=40&md5=a623389f6a4af5ee8ab9bbcb7c113288
Sowmiya S., Prince Shri Venkateshwara Padmavathy Engineering College, TN, India; Senthilkumar P., Nehru Institute of Engineering and Technology, Tamil Nadu, T.M.Palayam, India; Kuppuraj T., Karpagam Academy of Higher Education, TN, India; Mishra A., Ies Institute of Technology and Management, Ies University, M.P, Bhopal, India; Muralidharan P., Christ University, Bangalore, India; Rosaline S., R.M.K.Engineering College, TamilNadu, Kavaraipettai, India
Pandemic Pulse: Unveiling Insights with the Global Health Tracker Through AI and ML
Business intelligence; COVID-19; Dashboard; Data exploration; Data visualization; Data visualization techniques; Tableau
The current study highlights the importance of data analysis by applying data visualization tools to help you understand the pandemic disease informational component, and how it can be converted into knowledge that might enhance decision-making processes. In Tableau, a software for displaying data, researchers have incorporated a pandemic disease informational component from Coursera to improve assessment and selection. After becoming familiar with the data and the data visualization technological advances, some of it will be expected to conduct an initial investigation to identify significant changes in the data that is under consideration, compile and present this pandemic disease informational component, and enhance the corporate decision-making process. This issue for inquiry highlights the significance of knowledge examination via the use of communication visualization applications to aid in your comprehension of the pandemic disease informational component as well as how it may be changed into knowledge that may enhance the process of arriving at decisions. The creators of the knowledge representation computation application scenario used data from Coursera to improve their studies and make decisions. One will need to conduct an exploratory inquiry to find notable trends within the data after familiarizing oneself with it by utilizing visualization programs to compile and distribute this data to improve the company's decision-making procedures. This specific software is designed to be utilized in an early administrative duties course, an undergraduate accounting data structure course, or a data analytics-only educational program as a basic introduction to an informative visualization computer application. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Upreti K.; Verma V.S.; Verma A.; Vats P.; Patil S.; Kuwar V.
Lecture Notes in Networks and Systems, Vol-991 LNNS, pp. 191-203.
Upreti K., CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Verma V.S., Bhagwan Parshuram Institute of Technology, Delhi, Rohini, India; Verma A., Dr. Akhilesh Das Gupta Institute of Professional Studies, Delhi, India; Vats P., Computer Science and Engineering, Manipal University Jaipur, Rajasthan, Jaipur, India; Patil S., Dr. D. Y. Patil Vidyapeeth, Centre for Online Learning, Pimpri, Pune, India; Kuwar V., Dr. D. Y. Patil Vidyapeeth, Centre for Online Learning, Pimpri, Pune, India
Optimal Management of Resources in Cloud Infrastructure through Energy Aware Collaborative Model
Decentralized EnergyAware Collaborative Model (DEACM); Destination based VM Migration Algorithm; Resource Allocation; Virtual Machine Selection
As the infrastructures of cloud computing provides paramount services to worldwide users, persistent applications are congregated using large scale data centres at the customer sides. For such wide platforms, virtualization technique has been incorporated for multiplexing the essential sources available. Due to the extensive application variations in the workloads, it is significant to handle the resource allocation methodologies of the virtual machines (VM) for assuring the Quality of Service (QoS) of cloud. On concentrating this, the paper proposed a Decentralized Energy-Aware Collaborative Model (DEACM) for effectively managing the data centres in cloud infrastructures. Initially, the optimal model for system management and power management are declared. Then, functions of workload vectors and data collection about workloads has been carried out for optimal selection of virtual machines to migrate for balancing loads efficiently. This can be further applied for Target-based VM Migration Algorithm for determining the migrating target for VM. Moreover, the algorithm involved in energy utilization with managed QoS. The developed DEACM is evaluated using CloudSim platform and the results are discussed. The results exemplify that the DEACM can balance the workload across variety of machines optimally and provide reduced energy consumption to the complete system efficiently. 2024 IEEE.
Rajagopal M.; Karuppasamy S.K.; Hemalatha S.; Sivasakthivel R.
Proceedings - 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2024
Rajagopal M., Christ (Deemed to Be University), School of Business and Management, Karnataka, Bengaluru, India; Karuppasamy S.K., Alliance University - Central Campus, Computer Science and Engineering, Alliance College of Engineering and Design, Anekal Main Road,Karnataka, Bangalore, India; Hemalatha S., Karpagam Academy of Higher Education, Department of Computer Science, Coimbatore, India; Sivasakthivel R., Christ (Deemed to Be University), School of Sciences, Department of Computer, Science, Karnataka, Bengaluru, India
Exploring BERT and Bi-LSTM for Toxic Comment Classification: A Comparative Analysis
BERT; bi-LSTM; natural language processing; toxicity
This study analyzes on the classification of toxic comments in online conversations using advanced natural language processing (NLP) techniques. Leveraging advanced natural language processing (NLP) techniques and classification models, including BERT and Bi-LSTM models to classify comments into 6 types of toxicity: toxic, obscene, threat, insult, severe toxic and identity hate. The study achieves competitive performance. Specifically, fine-tuning BERT using TensorFlow and Hugging Face Transformers resulted in an AUC ROC rate of 98.23%, while LSTM yielded a binary accuracy of 96.07%. The results demonstrate the effectiveness of using transformer-based models like BERT for toxicity classification in text data. The study discusses the methodology, model architectures, and evaluation metrics, highlighting the effectiveness of each approach in identifying and classifying toxic language. Additionally, the paper discusses the implementation of a userfriendly interface for real-time toxic comment detection, leveraging the trained models for efficient moderation of online content. 2024 IEEE.
Tarun V.G.; Sivasakthivel R.; Ramar G.; Rajagopal M.; Sivaraman G.
2nd IEEE International Conference on Data Science and Information System, ICDSIS 2024
https://doi.org/10.1109/ICDSIS61070.2024.10594466 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200159414&doi=10.1109%2fICDSIS61070.2024.10594466&partnerID=40&md5=7aa5f4344bab4bf065edd20db9257776
Tarun V.G., Christ (Deemed To Be University), School Of Sciences, Department Of Computer Science, Karnataka, Bengaluru, India; Sivasakthivel R., Christ (Deemed To Be University), School Of Sciences, Department Of Computer Science, Karnataka, Bengaluru, India; Ramar G., Christ (Deemed To Be University), School Of Sciences, Department Of Computer Science, Karnataka, Bengaluru, India; Rajagopal M., Christ (Deemed To Be University), School Of Business And Management, Department Of Lean Operations And Systems, Karnataka, Bengaluru, India; Sivaraman G., M.G.R. College, Department Of Computer Science, Tamil Nadu, Hosur, India
MARS: Manual andAutomatic Robotic Sanitization onSocial Milieu
Sanitization is not a new term, but with the evolution of deadly COVID-19, the process came into the limelight quickly. The process was already utilized widely in hospitals, vaccination centers, food processing units, and medicine industries and suddenly became crucial in every domain related to our lives. Even though sanitization is considered the first line of defense against pandemic viruses like COVID-19, it is highly difficult to sanitize every nook and corner of bigger buildings and external structures like airports, railway stations, theaters, institutions, and hospitals. Slight carelessness to eliminate the virus from the sanitization process can reciprocate in the pandemic spread. Our proposed work deals with utilizing the accuracy and precision of robots to effectively sanitize bigger structures. The multi-faceted methodology of the work manages the comprehensive investigation of the robotic unit for the social setting. The concentrate additionally stretches out to refine the standard human behavioral reaction for modern robotic consideration in our lives. This will ease up the process and, at the same time, will reduce the chance of human error. The robotic structure is powered by a 12 V rechargeable battery, which has manual and automation cleaning modes. During manual mode, we control the robot with an Android application installed on the phone and connected with the robot through Bluetooth wireless connectivity. During automation, the mode robot moves in different directions and cleans and sanitizes the area independently. There is an ESP8266-based IoT connection unit to update the overall process for the cloud. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Singh R.K.; Bhardwaj P.; Annapurna B.; Prasad S.V.G.V.A.; Arokia Paul Rajan R.; Kiran S.
Lecture Notes in Networks and Systems, Vol-974, pp. 283-291.
Singh R.K., Department of CSE, Indian Institue of Technology, Punjab, Ropar, India; Bhardwaj P., Department of CSE, Indian Institue of Technology, Punjab, Ropar, India; Annapurna B., Department of CSE, Aditya College of Engineering, Andhra Pradesh, Surampalem, India; Prasad S.V.G.V.A., Department of Physics, PR Government College (A), Andhra Pradesh, Kakinada, India; Arokia Paul Rajan R., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Kiran S., Department of Computer Science, Rajiv Gandhi College of Engineering, Puducherry, India