News-Buffet: A Global News Aggregator for Real-Time, Location-Based Updates
Keywords:
Android Mobile Application, Market Performance, API Application Programming, Scroll the news, Countermeasures, Interface, Designing Part, User Clicks on the View, New Media Industries
Abstract
Mobile devices have become indispensable instruments for staying current, thanks to the revolutionary change brought about by the rapid rise of technology. With an emphasis on giving users real-time access to worldwide news in an engaging and aesthetically pleasing format, this Android news app is meant to bring the latest news from more than 120 newspapers across 50+ countries. This app fills a need in the media application industry by providing quick access to news stories, which is becoming increasingly important as mobile internet usage continues to rise. One of the app's strongest points is how well it compiles and displays news stories from around the world. To keep the platform current and relevant, it has an admin interface for managing news that writers or admins can use to add, edit, or remove content. Also, users can get news stories that are particular to their area thanks to a module that uses their current location. This function, together with the app's built-in ad choices, shows how the app may be a great tool for brand marketing by letting companies advertise their wares within the news stories themselves. In order to guarantee the system's ongoing growth and relevance, this research application system conducts literature studies, market research, and comparative analysis to examine future development trends and strategies for media apps.
References
1. A. Charmchian Langroudi, M. Charmchian Langroudi, F. Arasli, and I. Rahman, “Challenges and strategies for knowledge transfer in multinational corporations: The case of hotel ‘Maria the great,’” Journal of Hospitality & Tourism Cases, 2024.
2. A. Csiszer, ”The Effects of Advertisements on Our Changing Society”. Acta Sociologica Vol 4. No 1. pp. 19-30. 2011.
3. A. Csiszer, ” Social Communication and Its Means in the Context of the Partnership Agreement”. Spring Wind Vol 4. Doktoranduszok Országos Szövetsége, Debrecen, pp.37-42. 2014.
4. A. Csiszer, ”Dimensions of Corporate Social Responsibility in View of Social Trust” In: Proceedings of the International Business Conference: Creativity, Innovation and Entrepreneurship. Vilnius, Lithuania pp. 135-149. 2017
5. A. Csiszer, ”Regional Dimensions of Social Responsibility in View of Social Trust” East-West Cohesion Conference Paper . Vol 1. 2015
6. A. Csiszer, ”The Diffusion of Social Trust and the Triple Helix Concept” In: Callos, Nagib et al. (eds) The 10th International Conference On Society and Information Technologies Proceedings. IIIS Orlando, Florida, USA pp. 31-36. 2019
7. A. Csiszer, ”The Interconnections of Research and Design in Context of Social Trust and the Triple Helix Concept”. Journal of Systemics, Cybernetics and Informatics 17 (1). pp. 106-116. 2019
8. A. Csiszer, ”Trust as Social Connectivity” In: Sustaining Development – Connecting Business and Society in Emerging Economies Vol 2. No2. Lebanese International University Publisher. 2017
9. A. G. Usman et al., “Environmental modelling of CO concentration using AI-based approach supported with filters feature extraction: A direct and inverse chemometrics-based simulation,” Sustain. Chem. Environ., vol. 2, p. 100011, 2023.
10. A. Gbadamosi et al., “New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system,” Int. J. Hydrogen Energy, vol. 50, pp. 1326–1337, 2024.
11. A. J. Obaid, S. Suman Rajest, S. Silvia Priscila, T. Shynu, and S. A. Ettyem, “Dense convolution neural network for lung cancer classification and staging of the diseases using NSCLC images,” in Proceedings of Data Analytics and Management, Singapore; Singapore: Springer Nature, pp. 361–372, 2023.
12. Alabdullah, T. T. Y., Alfadhl, M. M. A., Yahya, S., & Rabi, A. M. A. (2014). The Role of Forensic Accounting in Reducing Financial Corruption: A Study in Iraq. International Journal of Business and Management, 9 (1), 26.
13. Alabdullah, T. T. Y., AL-Qallaf, A. J. M. (2023). The Impact Of Ethical Leadership On Firm Performance In Bahrain: Organizational Culture As A Mediator. Current Advanced Research On Sharia Finance And Economic Worldwide, 2(4), 482-498.
14. Alabdullah, T. T. Y., Naseer, H. K. (2023). Corporate Governance Strategic Performance As A Significant Strategic Management To Promoting Profitability: A Study In Uae. Journal Of Humanities, Social Sciences And Business, 2 (4), 620- 635.
15. Alabdullah, T.T.Y. (2023). How Do Sustainability Assurance, Internal Control, Audit Failures Influence Auditing Practices? Journal of Management, Accounting, General Finance and International Economic Issues, 2 (3), 671-688.
16. B. S. . Rao, K. . Meenakshi, K. . Kalaiarasi, R. . Babu P., J. . Kavitha, and V. . Saravanan, “Image Caption Generation Using Recurrent Convolutional Neural Network ”, Int J Intell Syst Appl Eng, vol. 12, no. 7s, pp. 76–80, Dec. 2023.
17. B. S. Alotaibi et al., “Sustainable Green Building Awareness: A Case Study of Kano Integrated with a Representative Comparison of Saudi Arabian Green Construction,” Buildings, vol. 13, no. 9, 2023.
18. Bayas, E., Kumar, P., & Deshmukh, K. (1869). Review of process parameter’s effect on 3D printing. GIS Science Journal, 10(3), 834–845.
19. Bayas, E., Kumar, P., & Harne, M. (2023). Impact of Process Parameters on Mechanical Properties of FDM 3D-Printed Parts: A Comprehensive Review. Eur. Chem. Bull, 12(S5), 708–725.
20. Bayas, Eknath, Kumar, P., & Deshmukh, K. (2023). A comprehensive review: Process parameters impact on tensile strength of 3D printed PLA parts. International Journal of Advanced Research in Science, Communication and Technology, 3(2) 233–239.
21. Beedkar, S. D., Khobragade, C. N., Chobe, S. S., Dawane, B. S., & Yemul, O. S. (2012). Novel thiazolo-pyrazolyl derivatives as xanthine oxidase inhibitors and free radical scavengers. International journal of biological macromolecules, 50(4), 947-956.
22. C. Dumitru Tabacaru, “Impact of non-formal education on the efficacy of school learning,” Studia Universitatis Moldaviae-Științe ale Educației, vol. 9, no. 119, pp. 38–39, 2018.
23. C. Dumitru, “Distance learning and higher education hybridization,” in Advances in Distance Learning in Times of Pandemic, Boca Raton: Chapman and Hall/CRC, 2023, pp. 237–271.
24. C. Dumitru, “Importance of communication in educational process,” Psihologia. Pedagogia specială. Asistenţa sociala, vol. 44, no. 3, pp. 3–15, 2016.
25. C. Dumitru, “Inclusion of students with disabilities in higher education,” in The Palgrave Handbook of Global Social Change, Cham: Springer International Publishing, 2023, pp. 1–29.
26. C. Dumitru, “New literacy instruction strategies considering higher education hybridization,” in Developing Curriculum for Emergency Remote Learning Environments, IGI Global, USA, 2022, pp. 1–20.
27. C. Dumitru, “Play interventions for hospitalized children with disability,” in Advances in Psychology, Mental Health, and Behavioral Studies, IGI Global, USA, 2022, pp. 170–185.
28. Chobe, S. S., Adole, V. A., Deshmukh, K. P., Pawar, T. B., & Jagdale, B. S. (2014). Poly (ethylene glycol)(PEG-400): A green approach towards synthesis of novel pyrazolo [3, 4-d] pyrimidin-6-amines derivatives and their antimicrobial screening. Archives of Applied Science Research, 6(2), 61-66.
29. Chobe, S. S., Dawane, B. S., Tumbi, K. M., Nandekar, P. P., & Sangamwar, A. T. (2012). An ecofriendly synthesis and DNA binding interaction study of some pyrazolo [1, 5-a] pyrimidines derivatives. Bioorganic & medicinal chemistry letters, 22(24), 7566-7572.
30. Chobe, S. S., Kamble, R. D., Patil, S. D., Acharya, A. P., Hese, S. V., Yemul, O. S., & Dawane, B. S. (2013). Green approach towards synthesis of substituted pyrazole-1, 4-dihydro, 9-oxa, 1, 2, 6, 8-tetrazacyclopentano [b] naphthalene-5-one derivatives as antimycobacterial agents. Medicinal Chemistry Research, 22, 5197-5203.
31. D. . Kavitha, S. . Shobana, S. . Rajkumar, G. R. . Reddy, R. . Babu P., and P. . Mukherjee, “Hyperspectral Image Classification Using Dimensionality Reduction Deep Networks”, Int J Intell Syst Appl Eng, vol. 12, no. 7s, pp. 81–86, Dec. 2023.
32. D. C. J. W. . Wise, S. . Ambareesh, R. . Babu P., D. . Sugumar, J. P. . Bhimavarapu, and A. S. . Kumar, “Latent Semantic Analysis Based Sentimental Analysis of Tweets in Social Media for the Classification of Cyberbullying Text”, Int J Intell Syst Appl Eng, vol. 12, no. 7s, pp. 26–35, Dec. 2023.
33. Dawane, B. S., Konda, S. G., Khandare, N. T., Chobe, S. S., Shaikh, B. M., Bodade, R. G., & Joshi, V. D. (2010). Synthesis and antimicrobial evaluation of 2-(2-butyl-4-chloro-1H-imidazol-5-yl-methylene)-substituted-benzofuran-3-ones. Organic communications, 3(2), 22.
34. Dawane, B. S., Konda, S. G., Shaikh, B. M., Chobe, S. S., Khandare, N. T., Kamble, V. T., & Bhosale, R. B. (2010). Synthesis and in vitro antimicrobial activity of some new 1-thiazolyl-2-pyrazoline derivatives. Synthesis, 1(009).
35. Dawane, B. S., Shaikh, B. M., Khandare, N. T., Kamble, V. T., Chobe, S. S., & Konda, S. G. (2010). Eco-friendly polyethylene glycol-400: a rapid and efficient recyclable reaction medium for the synthesis of thiazole derivatives. Green Chemistry Letters and Reviews, 3(3), 205-208.
36. E Bayas, P. (2024). Impact of Slicing Software on Geometric Correctness For FDM Additive Manufacturing. International Development Planning Review, 23(1), 704–711.
37. E. Geo Francis and S. Sheeja, “A Novel RDAE Based PSR-QKD Framework for Energy Efficient Intrusion Detection," 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, 2022, pp. 1-6.
38. E. Geo Francis and S. Sheeja, “An optimized intrusion detection model for wireless sensor networks based on MLP-CatBoost algorithm,” Multimedia Tools and Applications, 2024.
39. E. Geo Francis and S. Sheeja, “Intrusion detection system and mitigation of threats in IoT networks using AI techniques: A review,” Engineering and Applied Science Research, 2023, vol. 50, no. 6, pp. 633–645, https://ph01.tci-thaijo.org/index.php/easr/article/view/250974
40. E. Geo Francis and S. Sheeja, “SHAKE-ESDRL-based energy efficient intrusion detection and hashing system,” Annals of Telecommunications, 2023,
41. E. Geo Francis and S. Sheeja, “Towards an Optimal Security Using Multifactor Scalable Lightweight Cryptography for IoT," 2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4), Bangalore, India, 2022, pp. 1-6.
42. E. Geo Francis, S. Sheeja and E. F. Antony John, “IoT Intrusion Detection Using Two-Tier-Convolutional Deep-Learning Model," 2023 International Conference on IoT, Communication and Automation Technology (ICICAT), Gorakhpur, India, 2023, pp. 1-7.
43. E. Geo Francis, S. Sheeja and Joseph Jismy, “A Three-layer Convolution Neural Network Approach for Intrusion Detection in IoT," 2023 Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt, 2023, pp. 261-268.
44. E. S. Soji and T. Kamalakannan, “Efficient Indian sign language recognition and classification using enhanced machine learning approach,” Int. J. Crit. Infrastruct., vol. 20, no. 2, pp. 125–138, 2024.
45. E. S. Soji and T. Kamalakannan, “Indian sign language recognition using surf feature extraction and MDAE for patient disability discussion,” in Computational Intelligence for Clinical Diagnosis, Cham: Springer International Publishing, 2023, pp. 445–459.
46. E. S. Soji and T. Kamalakannan, “Machine learning approaches to intelligent sign language recognition and classification,” Int. J. Syst. Syst. Eng., vol. 13, no. 2, pp. 109–122, 2023.
47. E. Zanardo and B. Martini, “Secure and Authorized Data Sharing among different IoT Network Domains using Beez blockchain,” in 2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN), 2024, pages 122–129.
48. E. Zanardo, “Bitnocolo -anti money laundering (AML) tool for blockchain transactions.” Unpublished, 2020.
49. E. Zanardo, “Learningchain. A novel blockchain-based meritocratic marketplace for training distributed machine learning models,” in Software Engineering Application in Systems Design, Cham: Springer International Publishing, 2023, pp. 152–169.
50. E. Zanardo, B. Martini, and D. Bellisario, “Tokenized intelligence: Redefine network optimization in softwarized networks,” in 2024 IEEE 10th International Conference on Network Softwarization (NetSoft), 2024.
51. E. Zanardo, G. P. Domiziani, E. Iosif, and K. Christodoulou, “Identification of illicit blockchain transactions using hyperparameters auto-tuning,” in Principles and Practice of Blockchains, Cham: Springer International Publishing, 2023, pp. 27–38.
52. F. Nechita, A. Candrea, A. Csiszer, H. Tanaka ”Valorising Intangible Cultural Heritage Through Community Based Turism in Lapus Land, Transylvania”. Transilvania University of Brasov Bulletin VII: Social Sciences, Law 11:1 pp. 65-74. 2018
53. F. Nechita, A. Candrea, A. Csiszer, H. Tanaka ”Valorising Intangible Cultural Herizage Through Community Based Turism in Lapus land, Transylvania” In: Banks, M (ed). Interpret Europe Conference: Proceedings. pp: 2019-220. 2018
54. G. Gnanaguru, S. S. Priscila, M. Sakthivanitha, S. Radhakrishnan, S. S. Rajest, and S. Singh, “Thorough analysis of deep learning methods for diagnosis of COVID-19 CT images,” in Advances in Medical Technologies and Clinical Practice, IGI Global, pp. 46–65, 2024.
55. G. Gowthami and S. S. Priscila, “Tuna swarm optimisation-based feature selection and deep multimodal-sequential-hierarchical progressive network for network intrusion detection approach,” Int. J. Crit. Comput.-based Syst., vol. 10, no. 4, pp. 355–374, 2023.
56. Hampiholi, N. (2023). 21st Century Geriatric Care - Matching Advancing Devices to the Needs of the Aging Population, Journal of Emerging Technologies and Innovative Research 10 (10), 760-766.
57. Hampiholi, N. (2023). Medical Imaging Enhancement With Ai Models For Automatic Disease Detection And Classification Based On Medical Images. International Journal of Engineering Applied Sciences and Technology 8 (5), 31-37.
58. Hampiholi, N. (2023). Real-World Deployments of AR In Medical Training and Surgery, Journal of Emerging Technologies and Innovative Research 10 (10), 213-220.
59. Hampiholi, N. (2023). Through The Lens Of Principled Data Practice A Groundbreaking Exploration Into Ethical Healthcare Platforms. International Journal of Engineering Applied Sciences and Technology 8 (5), 26-30.
60. Hampiholi, N. (2024). Advancements In Genomic Medicine Harnessing Cutting-Edge Technologies For Health And Disease Insights - Privacy And Ethical Concerns, International Journal Of Medical Sciences (IJMS) 2 (1), 48-56.
61. Hampiholi, N. (2024). Computational Oncology with Advanced Healthcare Technologies - Enhancing Predictive Modelling and Survival Analysis - AI-Powered Imaging – Radiomics, International Journal of Artificial Intelligence In Medicine, 2 (1), 17-26.
62. Hampiholi, N. (2024). Elevating emergency healthcare - technological advancements and challenges in smart ambulance systems and advanced monitoring and diagnostic tools. International Journal of Computer Trends and Technology, 72(1), 1–7.
63. Hampiholi, N. (2024). Revolutionizing AI and Computing the Neuromorphic Engineering Paradigm in Neuromorphic Chips, International Journal of Computer Trends and Technology 72 (1), 92-98.
64. Haroun, M., Chobe, S. S., Alavala, R. R., Mathure, S. M., Jamullamudi, R. N., Nerkar, C. K., ... & Anwer, M. K. (2022). 1, 5-benzothiazepine derivatives: green synthesis, in silico and in vitro evaluation as anticancer agents. Molecules, 27(12), 3757.
65. Hassan, M. M., & Ahmed, D. (2023). Bayesian Deep Learning Applied To Lstm Models For Predicting Covid-19 Confirmed Cases In Iraq. Science Journal of University of Zakho, 11(2), 170–178.
66. Hassan, M.M. (2018). Bayesian Sensitivity Analysis to Quantifying Uncertainty in a Dendroclimatology Model. 2018 International Conference on Advanced Science and Engineering (ICOASE), 363-368.
67. Hassan, M.M. (2020). A Fully Bayesian Logistic Regression Model for Classification of ZADA Diabetes Dataset. Science Journal of University of Zakho, 8, 105-111.
68. Hassan, M.M., & Taher, S.A. (2022). Analysis and Classification of Autism Data Using Machine Learning Algorithms. Science Journal of University of Zakho, 10, 206-2012.
69. I. Abdulazeez, S. I. Abba, J. Usman, A. G. Usman, and I. H. Aljundi, “Recovery of Brine Resources Through Crown-Passivated Graphene, Silicene, and Boron Nitride Nanosheets Based on Machine-Learning Structural Predictions,” ACS Appl. Nano Mater., 2023.
70. Ismail, H.R., & Hassan, M.M. (2023). Bayesian deep learning methods applied to diabetic retinopathy disease: a review. Indonesian Journal of Electrical Engineering and Computer Science, 30, 1167-1177.
71. J. Usman, S. I. Abba, N. Baig, N. Abu-Zahra, S. W. Hasan, and I. H. Aljundi, “Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH2) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater,” ACS Appl. Mater. Interfaces, Mar. 2024.
72. J. W. Galbraith and G. Tkacz, “Nowcasting with Payments SystemData,” International Journal of Forecasting, vol. 34, no.2, pp. 366–376, 2018.
73. M. . Srinivasan, K. . HC, S. . Govindasamy, M. A. . Rasheed, R. . Babu P., and P. . Sultana, “Energy Efficient Routing Using Support Vector Machine in Wireless Sensor Networks”, Int J Intell Syst Appl Eng, vol. 12, no. 7s, pp. 320–325, Dec. 2023.
74. M. A. Yassin et al., “Advancing SDGs : Predicting Future Shifts in Saudi Arabia ’ s Terrestrial Water Storage Using Multi-Step-Ahead Machine Learning Based on GRACE Data,” 2024.
75. M. A. Yassin, A. G. Usman, S. I. Abba, D. U. Ozsahin, and I. H. Aljundi, “Intelligent learning algorithms integrated with feature engineering for sustainable groundwater salinization modelling: Eastern Province of Saudi Arabia,” Results Eng., vol. 20, p. 101434, 2023.
76. M. E. S. Soji, D. T. Kamalakannan, A conceptual AI-system model for home automation and smart monitoring based on vision,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 10, pp. 3099–3102, 2019.
77. M. G. Hariharan, S. Saranya, P. Velavan, E. S. Soji, S. S. Rajest, and L. Thammareddi, “Utilization of artificial intelligence algorithms for advanced cancer detection in the healthcare domain,” in Advances in Medical Technologies and Clinical Practice, IGI Global, 2024, pp. 287–302.
78. M. Gandhi, C. Satheesh, E. S. Soji, M. Saranya, S. S. Rajest, and S. K. Kothuru, “Image recognition and extraction on computerized vision for sign language decoding,” in Explainable AI Applications for Human Behavior Analysis, IGI Global, 2024, pp. 157–173.
79. M. M. Islam and L. Liu, “Deep learning accelerated topology optimization with inherent control of image quality,” Structural and Multidisciplinary Optimization, vol. 65, no. 11, Nov. 2022.
80. M. M. Islam and L. Liu, “Topology optimization of fiber-reinforced structures with discrete fiber orientations for additive manufacturing,” Computers & Structures, vol. 301, pp. 107468–107468, Sep. 2024.
81. M. M. Kirmani and S. I. Ansarullah, “Classification models on cardiovascular disease detection using Neural Networks, Naïve Bayes and J48 Data Mining Techniques,” International Journal of Advanced Research in Computer Science, vol. 7, no. 5, 2016.
82. M. S. Valli and G. T. Arasu, “An Efficient Feature Selection Technique of Unsupervised Learning Approach for Analyzing Web Opinions,” Journal of Scientific & Industrial Research, vol. 75, no.4, pp. 221–224, 2016.
83. M. Senbagavalli and G. T. Arasu, “Opinion Mining for Cardiovascular Disease using Decision Tree based Feature Selection,” Asian J. Res. Soc. Sci. Humanit., vol. 6, no. 8, p. 891, 2016.
84. M. Senbagavalli and S. K. Singh, “Improving patient health in smart healthcare monitoring systems using IoT,” in 2022 International Conference on Futuristic Technologies (INCOFT), pp. 1-7, Belgaum, India, 2022.
85. M. Srinivasan and E. S. Soji, “Kidney Tumour Segmentation and Classification Using Deep Learning”,” Central Asian Journal of Medical and Natural Science, vol. 3, no. 6, pp. 247–269, 2022.
86. M.M. Kirmani, & S.I. Ansarullah, “Prediction of heart disease using decision tree a data mining technique”. Int. J. Comput. Sci. Netw, 5(6), 2016.
87. Naeem, A. B., Senapati, B., Bhuva, D., Zaidi, A., Bhuva, A., Sudman, M. S. I., & Ahmed, A. E. M. (2024). Heart disease detection using feature extraction and artificial neural networks: A sensor-based approach. IEEE Access: Practical Innovations, Open Solutions, 12, 37349–37362.
88. P. M. Natarajan, V. R. Umapathy, A. Murali, and B. Swamikannu, “Computational simulations of identified marine-derived natural bioactive compounds as potential inhibitors of oral cancer,” Future Sci. OA, vol. 8, no. 3, 2022.
89. P. Natarajan, V. Rekha, A. Murali, and B. Swamikannu, “Newer congeners of doxycycline – do they hold promise for periodontal therapy?,” Arch. Med. Sci. - Civiliz. Dis., vol. 7, no. 1, pp. 16–23, 2022.
90. P. P. Anand, U. K. Kanike, P. Paramasivan, S. S. Rajest, R. Regin, and S. S. Priscila, “Embracing Industry 5.0: Pioneering Next-Generation Technology for a Flourishing Human Experience and Societal Advancement,” FMDB Transactions on Sustainable Social Sciences Letters, vol.1, no. 1, pp. 43–55, 2023.
91. P. Ramesh Babu, R. Nandhi Kesavan, A. Sivaramakrishnan, G. Sai Chaitanya Kumar, “EmoGAN Label-Changing Approach for Emotional State Analysis in Mobile Communication using Monkey Algorithm”, ICTACT Journal on Communication Technology, vol. 14, no. 4, pp. 3050–3056, Dec. 2023
92. Putchanuthala, R.B. and Reddy, E.S., “Image retrieval using locality preserving projections,” IET Journal of Engineering, Vol. 2020 Issue. 10, pp. 889-892, 2020.
93. R. B. P, P. T. Anitha, W. Dibaba and R. Boddu, "Mitigation of Attacks Using Cybersecurity Deep Models in Cloud Servers," 2023 International Conference on Disruptive Technologies (ICDT), Greater Noida, India, 2023, pp. 202-205.
94. R. Regin, Shynu, S. R. George, M. Bhattacharya, D. Datta, and S. S. Priscila, “Development of predictive model of diabetic using supervised machine learning classification algorithm of ensemble voting,” Int. J. Bioinform. Res. Appl., vol. 19, no. 3, 2023.
95. Ramesh Babu P, E. Sreenivasa Reddy, “A Comprehensive Survey on Semantic based Image Retrieval Systems for Cyber Forensics,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.245-250, 2018.
96. Ramesh Babu P, E. Sreenivasa Reddy, “A Design of Eigenvalue based CNN tool for Image Retrieval,” International Journal of Engineering and Advanced Technology, Vol.8, Issue.6, pp.2230-2236, 2019.
97. Ramesh Babu P, E. Sreenivasa Reddy, “A Novel Framework design for Semantic based Image retrieval as a Cyber Forensic Tool,” International Journal of Innovative Technology and Exploring Engineering, Vol.8, Issue.10, pp.2801-2808, 2019.
98. Reddy, A. R. P. (2021). Machine Learning Models for Anomaly Detection in Cloud Infrastructure Security. NeuroQuantology, 19(12), 755-763.
99. Reddy, A. R. P. (2021). The Role of Artificial Intelligence in Proactive Cyber Threat Detection In Cloud Environments. NeuroQuantology, 19(12), 764-773.
100. Reddy, A. R. P. (2022). The Future of Cloud Security: Ai-Powered Threat Intelligence and Response. International Neurourology Journal, 26(4), 45-52.
101. Reddy, A. R. P. (2023). Navigating the Cloud's Security Maze: AI and ML as Guides. International Neurourology Journal, 27(4), 1613-1620.
102. Reddy, A. R. P., & Ayyadapu, A. K. R. (2020). Automating Incident Response: Ai-Driven Approaches To Cloud Security Incident Management. Chelonian Research Foundation, 15(2), 1-10.
103. S. Agarwal, "Machine Learning Based Personalized Treatment Plans for Chronic Conditions," 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 2024, pp. 1127-1132.
104. S. Agarwal, “Optimizing product choices through A/B testing and data analytics: A comprehensive review,” International Journal of Advanced Research in Science, Communication and Technology, vol.3, no.1, pp. 550–555, 2023.
105. S. Agarwal, “The Interplay between Natural Language Processing (NLP) and Clinical Data Mining in Healthcare: A Review”,” Int J Intell Syst Appl Eng, vol. 12, no. 3, pp. 4161–4169, 2024.
106. S. Agarwal, “Validating Clinical Applications of Digital Health Solutions and Managing Associated Risk Management,” FMDB Transactions on Sustainable Management Letters., vol. 1, no. 4, pp. 134-143, 2023.
107. S. I. Abba et al., “Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm,” Water (Switzerland), vol. 15, no. 19, 2023.
108. S. I. Abba, A. G. Usman, and S. IŞIK, “Simulation for response surface in the HPLC optimization method development using artificial intelligence models: A data-driven approach,” Chemom. Intell. Lab. Syst., vol. 201, no. April, 2020.
109. S. I. Abba, J. Usman, and I. Abdulazeez, “Enhancing Li + recovery in brine mining : integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical nanomaterials,” pp. 15129–15142, 2024.
110. S. I. Ansarullah, S. M. Saif, P. Kumar, and M. M. Kirmani, “Significance of visible non-invasive risk attributes for the initial prediction of heart disease using different machine learning techniques,” Comput. Intell. Neurosci., vol. 2022, pp. 1–12, 2022.
111. S. I. Ansarullah, S. Mohsin Saif, S. Abdul Basit Andrabi, S. H. Kumhar, M. M. Kirmani, and D. P. Kumar, “An intelligent and reliable hyperparameter optimization machine learning model for early heart disease assessment using imperative risk attributes,” J. Healthc. Eng., vol. 2022, pp. 1–12, 2022.
112. S. Park et al., “Universal Carbonizable Filaments for 3D Printing,” Advanced Functional Materials, Jun. 2024.
113. S. R. Baker, N. Bloom, S. J. Davis, and K. Kost, “Policy news and stock market volatility,” SSRN Electron. J., vol.89, no.5, pp. 102304, 2019.
114. S. R. S. Steffi, R. Rajest, T. Shynu, and S. S. Priscila, “Analysis of an Interview Based on Emotion Detection Using Convolutional Neural Networks,” Central Asian Journal of Theoretical and Applied Science, vol. 4, no. 6, pp. 78–102, 2023.
115. S. Ruth, V. Srividhya Raghavan, J. Smrithi, and S. Banu, Spatial Preference Newsfeed System For Android Mobile Users”, IJCSITS, Vol.6, no.1, 2016.
116. S. S. Priscila and A. Jayanthiladevi, “A study on different hybrid deep learning approaches to forecast air pollution concentration of particulate matter,” in 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2023.
117. S. S. Priscila and S. S. Rajest, “An Improvised Virtual Queue Algorithm to Manipulate the Congestion in High-Speed Network”,” Central Asian Journal of Medical and Natural Science, vol. 3, no. 6, pp. 343–360, 2022.
118. S. S. Priscila, D. Celin Pappa, M. S. Banu, E. S. Soji, A. T. A. Christus, and V. S. Kumar, “Technological frontier on hybrid deep learning paradigm for global air quality intelligence,” in Cross-Industry AI Applications, IGI Global, pp. 144–162, 2024.
119. S. S. Priscila, E. S. Soji, N. Hossó, P. Paramasivan, and S. Suman Rajest, “Digital Realms and Mental Health: Examining the Influence of Online Learning Systems on Students,” FMDB Transactions on Sustainable Techno Learning, vol. 1, no. 3, pp. 156–164, 2023.
120. S. S. Priscila, S. S. Rajest, R. Regin, and T. Shynu, “Classification of Satellite Photographs Utilizing the K-Nearest Neighbor Algorithm,” Central Asian Journal of Mathematical Theory and Computer Sciences, vol. 4, no. 6, pp. 53–71, 2023.
121. S. S. Priscila, S. S. Rajest, S. N. Tadiboina, R. Regin, and S. András, “Analysis of Machine Learning and Deep Learning Methods for Superstore Sales Prediction,” FMDB Transactions on Sustainable Computer Letters, vol. 1, no. 1, pp. 1–11, 2023.
122. S. S. Rajest, S. Silvia Priscila, R. Regin, T. Shynu, and R. Steffi, “Application of Machine Learning to the Process of Crop Selection Based on Land Dataset,” International Journal on Orange Technologies, vol. 5, no. 6, pp. 91–112, 2023.
123. S. Silvia Priscila, S. Rajest, R. Regin, T. Shynu, and R. Steffi, “Classification of Satellite Photographs Utilizing the K-Nearest Neighbor Algorithm,” Central Asian Journal of Mathematical Theory and Computer Sciences, vol. 4, no. 6, pp. 53–71, 2023.
124. Senapati, B., & Rawal, B. S. (2023a). Adopting a deep learning split-protocol based predictive maintenance management system for industrial manufacturing operations. In Lecture Notes in Computer Science (pp. 22–39). Singapore: Springer Nature Singapore.
125. Senapati, B., & Rawal, B. S. (2023b). Quantum communication with RLP quantum resistant cryptography in industrial manufacturing. Cyber Security and Applications, 1(100019), 100019.
126. Senapati, B., Naeem, A. B., Ghafoor, M. I., Gulaxi, V., Almeida, F., Anand, M. R., Jaiswal, C. (2024). Wrist crack classification using deep learning and X-ray imaging. In Proceedings of the Second International Conference on Advances in Computing Research (ACR’24) (pp. 60–69). Cham: Springer Nature Switzerland.
127. Shaikh, S. A., Labhade, S. R., Kale, R. R., Pachorkar, P. Y., Meshram, R. J., Jain, K. S., ... & Boraste, D. R. (2024). Thiadiazole-Thiazole Derivatives as Potent Anti-Tubercular Agents: Synthesis, Biological Evaluation, and In Silico Docking Studies. European Journal of Medicinal Chemistry Reports, 100183.
128. Shaikh, S. A., Labhade, S. R., Kale, R. R., Pachorkar, P. Y., Meshram, R. J., Jain, K. S., ... & Wakchaure, S. N. (2024). Synthesis, Biological and Molecular Docking Studies of Thiazole‐Thiadiazole derivatives as potential Anti‐Tuberculosis Agents. Chemistry & Biodiversity, 21(6), e202400496.
129. Sunil Kumar Sehrawat, “Empowering the Patient Journey: The Role of Generative AI in Healthcare”, International Journal of Sustainable Development Through AI, ML and IoT, vol. 2, no. 2, p. 1-18, 2023.
130. Sunil Kumar Sehrawat, “The Role of Artificial Intelligence in ERP Automation: State-of-the-Art and Future Directions”, Transactions on Latest Trends in Artificial Intelligence, vol. 4, no. 4, 2023.
131. Sunil Kumar Sehrawat, “Transforming Clinical Trials: Harnessing the Power of Generative AI for Innovation and Efficiency”, Transactions on Recent Developments in Health Sectors, vol. 6, no. 6, p. 1-20, 2023.
132. T. Shynu, A. J. Singh, B. Rajest, S. S. Regin, and R. Priscila, “Sustainable intelligent outbreak with self-directed learning system and feature extraction approach in technology,” International Journal of Intelligent Engineering Informatics, vol. 10, no. 6, pp.484-503, 2022.
133. Tsarev, R., Kuzmich, R., Anisimova, T., Senapati, B., Ikonnikov, O., Shestakov, V., Kapustina, S. (2024). Automatic generation of an algebraic expression for a Boolean function in the basis ∧, ∨, ¬. In Data Analytics in System Engineering (pp. 128–136). Cham: Springer International Publishing.
134. Tsarev, R., Senapati, B., Alshahrani, S. H., Mirzagitova, A., Irgasheva, S., & Ascencio, J. (2024). Evaluating the effectiveness of flipped classrooms using linear regression. In Data Analytics in System Engineering (pp. 418–427). Cham: Springer International Publishing.
135. V. R. Umapathy et al., “Current trends and future perspectives on dental nanomaterials – An overview of nanotechnology strategies in dentistry,” J. King Saud Univ. Sci., vol. 34, no. 7, p. 102231, 2022.
136. V. R. Umapathy et al., “Emerging biosensors for oral cancer detection and diagnosis—A review unravelling their role in past and present advancements in the field of early diagnosis,” Biosensors (Basel), vol. 12, no. 7, p. 498, 2022.
137. V. R. Umapathy, P. M. Natarajan, and B. Swamikannu, “Comprehensive review on development of early diagnostics on oral cancer with a special focus on biomarkers,” Appl. Sci. (Basel), vol. 12, no. 10, p. 4926, 2022.
138. V. R. Umapathy, P. M. Natarajan, and B. Swamikannu, “Review insights on salivary proteomics biomarkers in oral cancer detection and diagnosis,” Molecules, vol. 28, no. 13, p. 5283, 2023.
139. V. R. Umapathy, P. M. Natarajan, and B. Swamikannu, “Review of the role of nanotechnology in overcoming the challenges faced in oral cancer diagnosis and treatment,” Molecules, vol. 28, no. 14, p. 5395, 2023.
140. V. Rekha U, P. Mn, and Bhuminathan., “Review on Anticancer properties of Piperine in Oral cancer: Therapeutic Perspectives,” Res. J. Pharm. Technol., pp. 3338–3342, 2022.
141. Y. K. . Aluri, B. . Aruna Devi, N. A. . Balaji, V. . Dakshinamurthi, R. . Babu P., and S. . Rajeyyagari, “Dry Eye Disease Classification Using AlexNet Classifier”, Int J Intell Syst Appl Eng, vol. 12, no. 7s, pp. 263–271, Dec. 2023.
2. A. Csiszer, ”The Effects of Advertisements on Our Changing Society”. Acta Sociologica Vol 4. No 1. pp. 19-30. 2011.
3. A. Csiszer, ” Social Communication and Its Means in the Context of the Partnership Agreement”. Spring Wind Vol 4. Doktoranduszok Országos Szövetsége, Debrecen, pp.37-42. 2014.
4. A. Csiszer, ”Dimensions of Corporate Social Responsibility in View of Social Trust” In: Proceedings of the International Business Conference: Creativity, Innovation and Entrepreneurship. Vilnius, Lithuania pp. 135-149. 2017
5. A. Csiszer, ”Regional Dimensions of Social Responsibility in View of Social Trust” East-West Cohesion Conference Paper . Vol 1. 2015
6. A. Csiszer, ”The Diffusion of Social Trust and the Triple Helix Concept” In: Callos, Nagib et al. (eds) The 10th International Conference On Society and Information Technologies Proceedings. IIIS Orlando, Florida, USA pp. 31-36. 2019
7. A. Csiszer, ”The Interconnections of Research and Design in Context of Social Trust and the Triple Helix Concept”. Journal of Systemics, Cybernetics and Informatics 17 (1). pp. 106-116. 2019
8. A. Csiszer, ”Trust as Social Connectivity” In: Sustaining Development – Connecting Business and Society in Emerging Economies Vol 2. No2. Lebanese International University Publisher. 2017
9. A. G. Usman et al., “Environmental modelling of CO concentration using AI-based approach supported with filters feature extraction: A direct and inverse chemometrics-based simulation,” Sustain. Chem. Environ., vol. 2, p. 100011, 2023.
10. A. Gbadamosi et al., “New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system,” Int. J. Hydrogen Energy, vol. 50, pp. 1326–1337, 2024.
11. A. J. Obaid, S. Suman Rajest, S. Silvia Priscila, T. Shynu, and S. A. Ettyem, “Dense convolution neural network for lung cancer classification and staging of the diseases using NSCLC images,” in Proceedings of Data Analytics and Management, Singapore; Singapore: Springer Nature, pp. 361–372, 2023.
12. Alabdullah, T. T. Y., Alfadhl, M. M. A., Yahya, S., & Rabi, A. M. A. (2014). The Role of Forensic Accounting in Reducing Financial Corruption: A Study in Iraq. International Journal of Business and Management, 9 (1), 26.
13. Alabdullah, T. T. Y., AL-Qallaf, A. J. M. (2023). The Impact Of Ethical Leadership On Firm Performance In Bahrain: Organizational Culture As A Mediator. Current Advanced Research On Sharia Finance And Economic Worldwide, 2(4), 482-498.
14. Alabdullah, T. T. Y., Naseer, H. K. (2023). Corporate Governance Strategic Performance As A Significant Strategic Management To Promoting Profitability: A Study In Uae. Journal Of Humanities, Social Sciences And Business, 2 (4), 620- 635.
15. Alabdullah, T.T.Y. (2023). How Do Sustainability Assurance, Internal Control, Audit Failures Influence Auditing Practices? Journal of Management, Accounting, General Finance and International Economic Issues, 2 (3), 671-688.
16. B. S. . Rao, K. . Meenakshi, K. . Kalaiarasi, R. . Babu P., J. . Kavitha, and V. . Saravanan, “Image Caption Generation Using Recurrent Convolutional Neural Network ”, Int J Intell Syst Appl Eng, vol. 12, no. 7s, pp. 76–80, Dec. 2023.
17. B. S. Alotaibi et al., “Sustainable Green Building Awareness: A Case Study of Kano Integrated with a Representative Comparison of Saudi Arabian Green Construction,” Buildings, vol. 13, no. 9, 2023.
18. Bayas, E., Kumar, P., & Deshmukh, K. (1869). Review of process parameter’s effect on 3D printing. GIS Science Journal, 10(3), 834–845.
19. Bayas, E., Kumar, P., & Harne, M. (2023). Impact of Process Parameters on Mechanical Properties of FDM 3D-Printed Parts: A Comprehensive Review. Eur. Chem. Bull, 12(S5), 708–725.
20. Bayas, Eknath, Kumar, P., & Deshmukh, K. (2023). A comprehensive review: Process parameters impact on tensile strength of 3D printed PLA parts. International Journal of Advanced Research in Science, Communication and Technology, 3(2) 233–239.
21. Beedkar, S. D., Khobragade, C. N., Chobe, S. S., Dawane, B. S., & Yemul, O. S. (2012). Novel thiazolo-pyrazolyl derivatives as xanthine oxidase inhibitors and free radical scavengers. International journal of biological macromolecules, 50(4), 947-956.
22. C. Dumitru Tabacaru, “Impact of non-formal education on the efficacy of school learning,” Studia Universitatis Moldaviae-Științe ale Educației, vol. 9, no. 119, pp. 38–39, 2018.
23. C. Dumitru, “Distance learning and higher education hybridization,” in Advances in Distance Learning in Times of Pandemic, Boca Raton: Chapman and Hall/CRC, 2023, pp. 237–271.
24. C. Dumitru, “Importance of communication in educational process,” Psihologia. Pedagogia specială. Asistenţa sociala, vol. 44, no. 3, pp. 3–15, 2016.
25. C. Dumitru, “Inclusion of students with disabilities in higher education,” in The Palgrave Handbook of Global Social Change, Cham: Springer International Publishing, 2023, pp. 1–29.
26. C. Dumitru, “New literacy instruction strategies considering higher education hybridization,” in Developing Curriculum for Emergency Remote Learning Environments, IGI Global, USA, 2022, pp. 1–20.
27. C. Dumitru, “Play interventions for hospitalized children with disability,” in Advances in Psychology, Mental Health, and Behavioral Studies, IGI Global, USA, 2022, pp. 170–185.
28. Chobe, S. S., Adole, V. A., Deshmukh, K. P., Pawar, T. B., & Jagdale, B. S. (2014). Poly (ethylene glycol)(PEG-400): A green approach towards synthesis of novel pyrazolo [3, 4-d] pyrimidin-6-amines derivatives and their antimicrobial screening. Archives of Applied Science Research, 6(2), 61-66.
29. Chobe, S. S., Dawane, B. S., Tumbi, K. M., Nandekar, P. P., & Sangamwar, A. T. (2012). An ecofriendly synthesis and DNA binding interaction study of some pyrazolo [1, 5-a] pyrimidines derivatives. Bioorganic & medicinal chemistry letters, 22(24), 7566-7572.
30. Chobe, S. S., Kamble, R. D., Patil, S. D., Acharya, A. P., Hese, S. V., Yemul, O. S., & Dawane, B. S. (2013). Green approach towards synthesis of substituted pyrazole-1, 4-dihydro, 9-oxa, 1, 2, 6, 8-tetrazacyclopentano [b] naphthalene-5-one derivatives as antimycobacterial agents. Medicinal Chemistry Research, 22, 5197-5203.
31. D. . Kavitha, S. . Shobana, S. . Rajkumar, G. R. . Reddy, R. . Babu P., and P. . Mukherjee, “Hyperspectral Image Classification Using Dimensionality Reduction Deep Networks”, Int J Intell Syst Appl Eng, vol. 12, no. 7s, pp. 81–86, Dec. 2023.
32. D. C. J. W. . Wise, S. . Ambareesh, R. . Babu P., D. . Sugumar, J. P. . Bhimavarapu, and A. S. . Kumar, “Latent Semantic Analysis Based Sentimental Analysis of Tweets in Social Media for the Classification of Cyberbullying Text”, Int J Intell Syst Appl Eng, vol. 12, no. 7s, pp. 26–35, Dec. 2023.
33. Dawane, B. S., Konda, S. G., Khandare, N. T., Chobe, S. S., Shaikh, B. M., Bodade, R. G., & Joshi, V. D. (2010). Synthesis and antimicrobial evaluation of 2-(2-butyl-4-chloro-1H-imidazol-5-yl-methylene)-substituted-benzofuran-3-ones. Organic communications, 3(2), 22.
34. Dawane, B. S., Konda, S. G., Shaikh, B. M., Chobe, S. S., Khandare, N. T., Kamble, V. T., & Bhosale, R. B. (2010). Synthesis and in vitro antimicrobial activity of some new 1-thiazolyl-2-pyrazoline derivatives. Synthesis, 1(009).
35. Dawane, B. S., Shaikh, B. M., Khandare, N. T., Kamble, V. T., Chobe, S. S., & Konda, S. G. (2010). Eco-friendly polyethylene glycol-400: a rapid and efficient recyclable reaction medium for the synthesis of thiazole derivatives. Green Chemistry Letters and Reviews, 3(3), 205-208.
36. E Bayas, P. (2024). Impact of Slicing Software on Geometric Correctness For FDM Additive Manufacturing. International Development Planning Review, 23(1), 704–711.
37. E. Geo Francis and S. Sheeja, “A Novel RDAE Based PSR-QKD Framework for Energy Efficient Intrusion Detection," 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, 2022, pp. 1-6.
38. E. Geo Francis and S. Sheeja, “An optimized intrusion detection model for wireless sensor networks based on MLP-CatBoost algorithm,” Multimedia Tools and Applications, 2024.
39. E. Geo Francis and S. Sheeja, “Intrusion detection system and mitigation of threats in IoT networks using AI techniques: A review,” Engineering and Applied Science Research, 2023, vol. 50, no. 6, pp. 633–645, https://ph01.tci-thaijo.org/index.php/easr/article/view/250974
40. E. Geo Francis and S. Sheeja, “SHAKE-ESDRL-based energy efficient intrusion detection and hashing system,” Annals of Telecommunications, 2023,
41. E. Geo Francis and S. Sheeja, “Towards an Optimal Security Using Multifactor Scalable Lightweight Cryptography for IoT," 2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4), Bangalore, India, 2022, pp. 1-6.
42. E. Geo Francis, S. Sheeja and E. F. Antony John, “IoT Intrusion Detection Using Two-Tier-Convolutional Deep-Learning Model," 2023 International Conference on IoT, Communication and Automation Technology (ICICAT), Gorakhpur, India, 2023, pp. 1-7.
43. E. Geo Francis, S. Sheeja and Joseph Jismy, “A Three-layer Convolution Neural Network Approach for Intrusion Detection in IoT," 2023 Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt, 2023, pp. 261-268.
44. E. S. Soji and T. Kamalakannan, “Efficient Indian sign language recognition and classification using enhanced machine learning approach,” Int. J. Crit. Infrastruct., vol. 20, no. 2, pp. 125–138, 2024.
45. E. S. Soji and T. Kamalakannan, “Indian sign language recognition using surf feature extraction and MDAE for patient disability discussion,” in Computational Intelligence for Clinical Diagnosis, Cham: Springer International Publishing, 2023, pp. 445–459.
46. E. S. Soji and T. Kamalakannan, “Machine learning approaches to intelligent sign language recognition and classification,” Int. J. Syst. Syst. Eng., vol. 13, no. 2, pp. 109–122, 2023.
47. E. Zanardo and B. Martini, “Secure and Authorized Data Sharing among different IoT Network Domains using Beez blockchain,” in 2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN), 2024, pages 122–129.
48. E. Zanardo, “Bitnocolo -anti money laundering (AML) tool for blockchain transactions.” Unpublished, 2020.
49. E. Zanardo, “Learningchain. A novel blockchain-based meritocratic marketplace for training distributed machine learning models,” in Software Engineering Application in Systems Design, Cham: Springer International Publishing, 2023, pp. 152–169.
50. E. Zanardo, B. Martini, and D. Bellisario, “Tokenized intelligence: Redefine network optimization in softwarized networks,” in 2024 IEEE 10th International Conference on Network Softwarization (NetSoft), 2024.
51. E. Zanardo, G. P. Domiziani, E. Iosif, and K. Christodoulou, “Identification of illicit blockchain transactions using hyperparameters auto-tuning,” in Principles and Practice of Blockchains, Cham: Springer International Publishing, 2023, pp. 27–38.
52. F. Nechita, A. Candrea, A. Csiszer, H. Tanaka ”Valorising Intangible Cultural Heritage Through Community Based Turism in Lapus Land, Transylvania”. Transilvania University of Brasov Bulletin VII: Social Sciences, Law 11:1 pp. 65-74. 2018
53. F. Nechita, A. Candrea, A. Csiszer, H. Tanaka ”Valorising Intangible Cultural Herizage Through Community Based Turism in Lapus land, Transylvania” In: Banks, M (ed). Interpret Europe Conference: Proceedings. pp: 2019-220. 2018
54. G. Gnanaguru, S. S. Priscila, M. Sakthivanitha, S. Radhakrishnan, S. S. Rajest, and S. Singh, “Thorough analysis of deep learning methods for diagnosis of COVID-19 CT images,” in Advances in Medical Technologies and Clinical Practice, IGI Global, pp. 46–65, 2024.
55. G. Gowthami and S. S. Priscila, “Tuna swarm optimisation-based feature selection and deep multimodal-sequential-hierarchical progressive network for network intrusion detection approach,” Int. J. Crit. Comput.-based Syst., vol. 10, no. 4, pp. 355–374, 2023.
56. Hampiholi, N. (2023). 21st Century Geriatric Care - Matching Advancing Devices to the Needs of the Aging Population, Journal of Emerging Technologies and Innovative Research 10 (10), 760-766.
57. Hampiholi, N. (2023). Medical Imaging Enhancement With Ai Models For Automatic Disease Detection And Classification Based On Medical Images. International Journal of Engineering Applied Sciences and Technology 8 (5), 31-37.
58. Hampiholi, N. (2023). Real-World Deployments of AR In Medical Training and Surgery, Journal of Emerging Technologies and Innovative Research 10 (10), 213-220.
59. Hampiholi, N. (2023). Through The Lens Of Principled Data Practice A Groundbreaking Exploration Into Ethical Healthcare Platforms. International Journal of Engineering Applied Sciences and Technology 8 (5), 26-30.
60. Hampiholi, N. (2024). Advancements In Genomic Medicine Harnessing Cutting-Edge Technologies For Health And Disease Insights - Privacy And Ethical Concerns, International Journal Of Medical Sciences (IJMS) 2 (1), 48-56.
61. Hampiholi, N. (2024). Computational Oncology with Advanced Healthcare Technologies - Enhancing Predictive Modelling and Survival Analysis - AI-Powered Imaging – Radiomics, International Journal of Artificial Intelligence In Medicine, 2 (1), 17-26.
62. Hampiholi, N. (2024). Elevating emergency healthcare - technological advancements and challenges in smart ambulance systems and advanced monitoring and diagnostic tools. International Journal of Computer Trends and Technology, 72(1), 1–7.
63. Hampiholi, N. (2024). Revolutionizing AI and Computing the Neuromorphic Engineering Paradigm in Neuromorphic Chips, International Journal of Computer Trends and Technology 72 (1), 92-98.
64. Haroun, M., Chobe, S. S., Alavala, R. R., Mathure, S. M., Jamullamudi, R. N., Nerkar, C. K., ... & Anwer, M. K. (2022). 1, 5-benzothiazepine derivatives: green synthesis, in silico and in vitro evaluation as anticancer agents. Molecules, 27(12), 3757.
65. Hassan, M. M., & Ahmed, D. (2023). Bayesian Deep Learning Applied To Lstm Models For Predicting Covid-19 Confirmed Cases In Iraq. Science Journal of University of Zakho, 11(2), 170–178.
66. Hassan, M.M. (2018). Bayesian Sensitivity Analysis to Quantifying Uncertainty in a Dendroclimatology Model. 2018 International Conference on Advanced Science and Engineering (ICOASE), 363-368.
67. Hassan, M.M. (2020). A Fully Bayesian Logistic Regression Model for Classification of ZADA Diabetes Dataset. Science Journal of University of Zakho, 8, 105-111.
68. Hassan, M.M., & Taher, S.A. (2022). Analysis and Classification of Autism Data Using Machine Learning Algorithms. Science Journal of University of Zakho, 10, 206-2012.
69. I. Abdulazeez, S. I. Abba, J. Usman, A. G. Usman, and I. H. Aljundi, “Recovery of Brine Resources Through Crown-Passivated Graphene, Silicene, and Boron Nitride Nanosheets Based on Machine-Learning Structural Predictions,” ACS Appl. Nano Mater., 2023.
70. Ismail, H.R., & Hassan, M.M. (2023). Bayesian deep learning methods applied to diabetic retinopathy disease: a review. Indonesian Journal of Electrical Engineering and Computer Science, 30, 1167-1177.
71. J. Usman, S. I. Abba, N. Baig, N. Abu-Zahra, S. W. Hasan, and I. H. Aljundi, “Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH2) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater,” ACS Appl. Mater. Interfaces, Mar. 2024.
72. J. W. Galbraith and G. Tkacz, “Nowcasting with Payments SystemData,” International Journal of Forecasting, vol. 34, no.2, pp. 366–376, 2018.
73. M. . Srinivasan, K. . HC, S. . Govindasamy, M. A. . Rasheed, R. . Babu P., and P. . Sultana, “Energy Efficient Routing Using Support Vector Machine in Wireless Sensor Networks”, Int J Intell Syst Appl Eng, vol. 12, no. 7s, pp. 320–325, Dec. 2023.
74. M. A. Yassin et al., “Advancing SDGs : Predicting Future Shifts in Saudi Arabia ’ s Terrestrial Water Storage Using Multi-Step-Ahead Machine Learning Based on GRACE Data,” 2024.
75. M. A. Yassin, A. G. Usman, S. I. Abba, D. U. Ozsahin, and I. H. Aljundi, “Intelligent learning algorithms integrated with feature engineering for sustainable groundwater salinization modelling: Eastern Province of Saudi Arabia,” Results Eng., vol. 20, p. 101434, 2023.
76. M. E. S. Soji, D. T. Kamalakannan, A conceptual AI-system model for home automation and smart monitoring based on vision,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 10, pp. 3099–3102, 2019.
77. M. G. Hariharan, S. Saranya, P. Velavan, E. S. Soji, S. S. Rajest, and L. Thammareddi, “Utilization of artificial intelligence algorithms for advanced cancer detection in the healthcare domain,” in Advances in Medical Technologies and Clinical Practice, IGI Global, 2024, pp. 287–302.
78. M. Gandhi, C. Satheesh, E. S. Soji, M. Saranya, S. S. Rajest, and S. K. Kothuru, “Image recognition and extraction on computerized vision for sign language decoding,” in Explainable AI Applications for Human Behavior Analysis, IGI Global, 2024, pp. 157–173.
79. M. M. Islam and L. Liu, “Deep learning accelerated topology optimization with inherent control of image quality,” Structural and Multidisciplinary Optimization, vol. 65, no. 11, Nov. 2022.
80. M. M. Islam and L. Liu, “Topology optimization of fiber-reinforced structures with discrete fiber orientations for additive manufacturing,” Computers & Structures, vol. 301, pp. 107468–107468, Sep. 2024.
81. M. M. Kirmani and S. I. Ansarullah, “Classification models on cardiovascular disease detection using Neural Networks, Naïve Bayes and J48 Data Mining Techniques,” International Journal of Advanced Research in Computer Science, vol. 7, no. 5, 2016.
82. M. S. Valli and G. T. Arasu, “An Efficient Feature Selection Technique of Unsupervised Learning Approach for Analyzing Web Opinions,” Journal of Scientific & Industrial Research, vol. 75, no.4, pp. 221–224, 2016.
83. M. Senbagavalli and G. T. Arasu, “Opinion Mining for Cardiovascular Disease using Decision Tree based Feature Selection,” Asian J. Res. Soc. Sci. Humanit., vol. 6, no. 8, p. 891, 2016.
84. M. Senbagavalli and S. K. Singh, “Improving patient health in smart healthcare monitoring systems using IoT,” in 2022 International Conference on Futuristic Technologies (INCOFT), pp. 1-7, Belgaum, India, 2022.
85. M. Srinivasan and E. S. Soji, “Kidney Tumour Segmentation and Classification Using Deep Learning”,” Central Asian Journal of Medical and Natural Science, vol. 3, no. 6, pp. 247–269, 2022.
86. M.M. Kirmani, & S.I. Ansarullah, “Prediction of heart disease using decision tree a data mining technique”. Int. J. Comput. Sci. Netw, 5(6), 2016.
87. Naeem, A. B., Senapati, B., Bhuva, D., Zaidi, A., Bhuva, A., Sudman, M. S. I., & Ahmed, A. E. M. (2024). Heart disease detection using feature extraction and artificial neural networks: A sensor-based approach. IEEE Access: Practical Innovations, Open Solutions, 12, 37349–37362.
88. P. M. Natarajan, V. R. Umapathy, A. Murali, and B. Swamikannu, “Computational simulations of identified marine-derived natural bioactive compounds as potential inhibitors of oral cancer,” Future Sci. OA, vol. 8, no. 3, 2022.
89. P. Natarajan, V. Rekha, A. Murali, and B. Swamikannu, “Newer congeners of doxycycline – do they hold promise for periodontal therapy?,” Arch. Med. Sci. - Civiliz. Dis., vol. 7, no. 1, pp. 16–23, 2022.
90. P. P. Anand, U. K. Kanike, P. Paramasivan, S. S. Rajest, R. Regin, and S. S. Priscila, “Embracing Industry 5.0: Pioneering Next-Generation Technology for a Flourishing Human Experience and Societal Advancement,” FMDB Transactions on Sustainable Social Sciences Letters, vol.1, no. 1, pp. 43–55, 2023.
91. P. Ramesh Babu, R. Nandhi Kesavan, A. Sivaramakrishnan, G. Sai Chaitanya Kumar, “EmoGAN Label-Changing Approach for Emotional State Analysis in Mobile Communication using Monkey Algorithm”, ICTACT Journal on Communication Technology, vol. 14, no. 4, pp. 3050–3056, Dec. 2023
92. Putchanuthala, R.B. and Reddy, E.S., “Image retrieval using locality preserving projections,” IET Journal of Engineering, Vol. 2020 Issue. 10, pp. 889-892, 2020.
93. R. B. P, P. T. Anitha, W. Dibaba and R. Boddu, "Mitigation of Attacks Using Cybersecurity Deep Models in Cloud Servers," 2023 International Conference on Disruptive Technologies (ICDT), Greater Noida, India, 2023, pp. 202-205.
94. R. Regin, Shynu, S. R. George, M. Bhattacharya, D. Datta, and S. S. Priscila, “Development of predictive model of diabetic using supervised machine learning classification algorithm of ensemble voting,” Int. J. Bioinform. Res. Appl., vol. 19, no. 3, 2023.
95. Ramesh Babu P, E. Sreenivasa Reddy, “A Comprehensive Survey on Semantic based Image Retrieval Systems for Cyber Forensics,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.245-250, 2018.
96. Ramesh Babu P, E. Sreenivasa Reddy, “A Design of Eigenvalue based CNN tool for Image Retrieval,” International Journal of Engineering and Advanced Technology, Vol.8, Issue.6, pp.2230-2236, 2019.
97. Ramesh Babu P, E. Sreenivasa Reddy, “A Novel Framework design for Semantic based Image retrieval as a Cyber Forensic Tool,” International Journal of Innovative Technology and Exploring Engineering, Vol.8, Issue.10, pp.2801-2808, 2019.
98. Reddy, A. R. P. (2021). Machine Learning Models for Anomaly Detection in Cloud Infrastructure Security. NeuroQuantology, 19(12), 755-763.
99. Reddy, A. R. P. (2021). The Role of Artificial Intelligence in Proactive Cyber Threat Detection In Cloud Environments. NeuroQuantology, 19(12), 764-773.
100. Reddy, A. R. P. (2022). The Future of Cloud Security: Ai-Powered Threat Intelligence and Response. International Neurourology Journal, 26(4), 45-52.
101. Reddy, A. R. P. (2023). Navigating the Cloud's Security Maze: AI and ML as Guides. International Neurourology Journal, 27(4), 1613-1620.
102. Reddy, A. R. P., & Ayyadapu, A. K. R. (2020). Automating Incident Response: Ai-Driven Approaches To Cloud Security Incident Management. Chelonian Research Foundation, 15(2), 1-10.
103. S. Agarwal, "Machine Learning Based Personalized Treatment Plans for Chronic Conditions," 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 2024, pp. 1127-1132.
104. S. Agarwal, “Optimizing product choices through A/B testing and data analytics: A comprehensive review,” International Journal of Advanced Research in Science, Communication and Technology, vol.3, no.1, pp. 550–555, 2023.
105. S. Agarwal, “The Interplay between Natural Language Processing (NLP) and Clinical Data Mining in Healthcare: A Review”,” Int J Intell Syst Appl Eng, vol. 12, no. 3, pp. 4161–4169, 2024.
106. S. Agarwal, “Validating Clinical Applications of Digital Health Solutions and Managing Associated Risk Management,” FMDB Transactions on Sustainable Management Letters., vol. 1, no. 4, pp. 134-143, 2023.
107. S. I. Abba et al., “Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm,” Water (Switzerland), vol. 15, no. 19, 2023.
108. S. I. Abba, A. G. Usman, and S. IŞIK, “Simulation for response surface in the HPLC optimization method development using artificial intelligence models: A data-driven approach,” Chemom. Intell. Lab. Syst., vol. 201, no. April, 2020.
109. S. I. Abba, J. Usman, and I. Abdulazeez, “Enhancing Li + recovery in brine mining : integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical nanomaterials,” pp. 15129–15142, 2024.
110. S. I. Ansarullah, S. M. Saif, P. Kumar, and M. M. Kirmani, “Significance of visible non-invasive risk attributes for the initial prediction of heart disease using different machine learning techniques,” Comput. Intell. Neurosci., vol. 2022, pp. 1–12, 2022.
111. S. I. Ansarullah, S. Mohsin Saif, S. Abdul Basit Andrabi, S. H. Kumhar, M. M. Kirmani, and D. P. Kumar, “An intelligent and reliable hyperparameter optimization machine learning model for early heart disease assessment using imperative risk attributes,” J. Healthc. Eng., vol. 2022, pp. 1–12, 2022.
112. S. Park et al., “Universal Carbonizable Filaments for 3D Printing,” Advanced Functional Materials, Jun. 2024.
113. S. R. Baker, N. Bloom, S. J. Davis, and K. Kost, “Policy news and stock market volatility,” SSRN Electron. J., vol.89, no.5, pp. 102304, 2019.
114. S. R. S. Steffi, R. Rajest, T. Shynu, and S. S. Priscila, “Analysis of an Interview Based on Emotion Detection Using Convolutional Neural Networks,” Central Asian Journal of Theoretical and Applied Science, vol. 4, no. 6, pp. 78–102, 2023.
115. S. Ruth, V. Srividhya Raghavan, J. Smrithi, and S. Banu, Spatial Preference Newsfeed System For Android Mobile Users”, IJCSITS, Vol.6, no.1, 2016.
116. S. S. Priscila and A. Jayanthiladevi, “A study on different hybrid deep learning approaches to forecast air pollution concentration of particulate matter,” in 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2023.
117. S. S. Priscila and S. S. Rajest, “An Improvised Virtual Queue Algorithm to Manipulate the Congestion in High-Speed Network”,” Central Asian Journal of Medical and Natural Science, vol. 3, no. 6, pp. 343–360, 2022.
118. S. S. Priscila, D. Celin Pappa, M. S. Banu, E. S. Soji, A. T. A. Christus, and V. S. Kumar, “Technological frontier on hybrid deep learning paradigm for global air quality intelligence,” in Cross-Industry AI Applications, IGI Global, pp. 144–162, 2024.
119. S. S. Priscila, E. S. Soji, N. Hossó, P. Paramasivan, and S. Suman Rajest, “Digital Realms and Mental Health: Examining the Influence of Online Learning Systems on Students,” FMDB Transactions on Sustainable Techno Learning, vol. 1, no. 3, pp. 156–164, 2023.
120. S. S. Priscila, S. S. Rajest, R. Regin, and T. Shynu, “Classification of Satellite Photographs Utilizing the K-Nearest Neighbor Algorithm,” Central Asian Journal of Mathematical Theory and Computer Sciences, vol. 4, no. 6, pp. 53–71, 2023.
121. S. S. Priscila, S. S. Rajest, S. N. Tadiboina, R. Regin, and S. András, “Analysis of Machine Learning and Deep Learning Methods for Superstore Sales Prediction,” FMDB Transactions on Sustainable Computer Letters, vol. 1, no. 1, pp. 1–11, 2023.
122. S. S. Rajest, S. Silvia Priscila, R. Regin, T. Shynu, and R. Steffi, “Application of Machine Learning to the Process of Crop Selection Based on Land Dataset,” International Journal on Orange Technologies, vol. 5, no. 6, pp. 91–112, 2023.
123. S. Silvia Priscila, S. Rajest, R. Regin, T. Shynu, and R. Steffi, “Classification of Satellite Photographs Utilizing the K-Nearest Neighbor Algorithm,” Central Asian Journal of Mathematical Theory and Computer Sciences, vol. 4, no. 6, pp. 53–71, 2023.
124. Senapati, B., & Rawal, B. S. (2023a). Adopting a deep learning split-protocol based predictive maintenance management system for industrial manufacturing operations. In Lecture Notes in Computer Science (pp. 22–39). Singapore: Springer Nature Singapore.
125. Senapati, B., & Rawal, B. S. (2023b). Quantum communication with RLP quantum resistant cryptography in industrial manufacturing. Cyber Security and Applications, 1(100019), 100019.
126. Senapati, B., Naeem, A. B., Ghafoor, M. I., Gulaxi, V., Almeida, F., Anand, M. R., Jaiswal, C. (2024). Wrist crack classification using deep learning and X-ray imaging. In Proceedings of the Second International Conference on Advances in Computing Research (ACR’24) (pp. 60–69). Cham: Springer Nature Switzerland.
127. Shaikh, S. A., Labhade, S. R., Kale, R. R., Pachorkar, P. Y., Meshram, R. J., Jain, K. S., ... & Boraste, D. R. (2024). Thiadiazole-Thiazole Derivatives as Potent Anti-Tubercular Agents: Synthesis, Biological Evaluation, and In Silico Docking Studies. European Journal of Medicinal Chemistry Reports, 100183.
128. Shaikh, S. A., Labhade, S. R., Kale, R. R., Pachorkar, P. Y., Meshram, R. J., Jain, K. S., ... & Wakchaure, S. N. (2024). Synthesis, Biological and Molecular Docking Studies of Thiazole‐Thiadiazole derivatives as potential Anti‐Tuberculosis Agents. Chemistry & Biodiversity, 21(6), e202400496.
129. Sunil Kumar Sehrawat, “Empowering the Patient Journey: The Role of Generative AI in Healthcare”, International Journal of Sustainable Development Through AI, ML and IoT, vol. 2, no. 2, p. 1-18, 2023.
130. Sunil Kumar Sehrawat, “The Role of Artificial Intelligence in ERP Automation: State-of-the-Art and Future Directions”, Transactions on Latest Trends in Artificial Intelligence, vol. 4, no. 4, 2023.
131. Sunil Kumar Sehrawat, “Transforming Clinical Trials: Harnessing the Power of Generative AI for Innovation and Efficiency”, Transactions on Recent Developments in Health Sectors, vol. 6, no. 6, p. 1-20, 2023.
132. T. Shynu, A. J. Singh, B. Rajest, S. S. Regin, and R. Priscila, “Sustainable intelligent outbreak with self-directed learning system and feature extraction approach in technology,” International Journal of Intelligent Engineering Informatics, vol. 10, no. 6, pp.484-503, 2022.
133. Tsarev, R., Kuzmich, R., Anisimova, T., Senapati, B., Ikonnikov, O., Shestakov, V., Kapustina, S. (2024). Automatic generation of an algebraic expression for a Boolean function in the basis ∧, ∨, ¬. In Data Analytics in System Engineering (pp. 128–136). Cham: Springer International Publishing.
134. Tsarev, R., Senapati, B., Alshahrani, S. H., Mirzagitova, A., Irgasheva, S., & Ascencio, J. (2024). Evaluating the effectiveness of flipped classrooms using linear regression. In Data Analytics in System Engineering (pp. 418–427). Cham: Springer International Publishing.
135. V. R. Umapathy et al., “Current trends and future perspectives on dental nanomaterials – An overview of nanotechnology strategies in dentistry,” J. King Saud Univ. Sci., vol. 34, no. 7, p. 102231, 2022.
136. V. R. Umapathy et al., “Emerging biosensors for oral cancer detection and diagnosis—A review unravelling their role in past and present advancements in the field of early diagnosis,” Biosensors (Basel), vol. 12, no. 7, p. 498, 2022.
137. V. R. Umapathy, P. M. Natarajan, and B. Swamikannu, “Comprehensive review on development of early diagnostics on oral cancer with a special focus on biomarkers,” Appl. Sci. (Basel), vol. 12, no. 10, p. 4926, 2022.
138. V. R. Umapathy, P. M. Natarajan, and B. Swamikannu, “Review insights on salivary proteomics biomarkers in oral cancer detection and diagnosis,” Molecules, vol. 28, no. 13, p. 5283, 2023.
139. V. R. Umapathy, P. M. Natarajan, and B. Swamikannu, “Review of the role of nanotechnology in overcoming the challenges faced in oral cancer diagnosis and treatment,” Molecules, vol. 28, no. 14, p. 5395, 2023.
140. V. Rekha U, P. Mn, and Bhuminathan., “Review on Anticancer properties of Piperine in Oral cancer: Therapeutic Perspectives,” Res. J. Pharm. Technol., pp. 3338–3342, 2022.
141. Y. K. . Aluri, B. . Aruna Devi, N. A. . Balaji, V. . Dakshinamurthi, R. . Babu P., and S. . Rajeyyagari, “Dry Eye Disease Classification Using AlexNet Classifier”, Int J Intell Syst Appl Eng, vol. 12, no. 7s, pp. 263–271, Dec. 2023.
Published
2024-08-24
How to Cite
Rajest, S. S., & Regin, R. (2024). News-Buffet: A Global News Aggregator for Real-Time, Location-Based Updates. International Journal on Orange Technologies, 6(3), 22-39. https://doi.org/10.31149/ijot.v6i3.5306
Section
Articles
.png)
