iosr-JCE   Volume-26 ~ Issue-4 ~ Jul. - Aug. 2024

Abstract: Wireless Sensor Network (WSN) is an emergent and fast growing technology that proves great in the future age of computing applications. WSNs are employed in various areas of human endeavour and high risk environments such as health sectors in monitoring patient medical history, military force applications deployed in battleground, environmental or ecological monitoring in oil and gas industries, factory monitoring and agricultural or farming monitoring. Because of the sensitive nature of message or information that these applications are meant to.....

Keywords- Wireless Sensor Network, WSN, Security, Threat, Challenges, Countermeasures

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[3]. Y. W. Law, S. Dulman, S. Etalle, and P. Havinga, Assessing security-critical energy efficient sensor networks. In: Sensor network operations. Canada: Wiley & Sons, Inc., 2006.
[4]. K. Koffka, G. Wayne, and R. Diana "Security in Wireless Sensor Networks," Global Journal of Computer Science and Technology Network, Web & Security, vol. 11, no. 16, pp. 43 -50,2012.
[5]. S. William, Network Security Essentials: Applications and Standards. 4th edn. USA: Pearson Education, Inc., 2011

Abstract: The development of technology has a profound effect on the academic performance of students. Smartphone usage has become popular among the undergraduate students of Ebonyi state University because of its educational and entertaining options. Students are increasingly using Smartphone. However excessive Smartphone usage usually makes the students......

Keywords—Smartphone, Addiction, Performance, Students, EBSU

[1]. Aljomaa, S. S., Qudah, M. F. A., Albursan, I. S., Bakhiet, S. F., & Abduljabbar, A. S. (2016). Smartphone addiction among university students in the light of some variables. Computers in Human Behavior, 61, 155–164.
[2]. Augner, C., & Hacker, G. W. (2012). Associations between problematic mobile phone use and psychological parameters in young adults. International Journal of Public Health, 57(2), 437–441.
[3]. Chiu, S.-I. (2014). The relationship between life stress and smartphone addiction on Taiwanese university student: A mediation model of learning self-efficacy and social self-efficacy. Computers in Human Behavior, 34, 49–57.
[4]. G¨okc¸earslan, S¸., Mumcu, F. K., Has¸laman, T., & C¸ evik, Y. D. (2016). Modelling smartphone addiction: The role of smartphone usage, self-regulation, general self-efficacy and cyberloafing in university students. Computers in Human Behavior, 63, 639–649.

Abstract: As we know, technology has transformed how we communicate, work, learn, and access information, making life easier in many ways. How can Artificial Intelligence contribute to mental health wellness? Our research is important to promote increased understanding of mental health in India. Despite recent initiatives like the 2022 National Tele Mental Health Programme, challenges persist, including stigma, limited awareness, a shortage of professionals, underfunded services, rural-urban disparities, cultural barriers, and integration gaps with primary healthcare. Addressing these issues is crucial for a more inclusive and effective mental health support system. This study addresses these issues by using an online mental health wellness platform featuring support services and....

Keywords—Artificial Intelligence, Generative AI, Machine Learning, Data Visualization, Natural Language Processing, Mental Health Services, Mental Health Assessment, Self-Wellness Monitoring, Emotion Detection Technology, Mental Disorders, Stigma, Virtual Therapy, Report, EEG, Sentiment Analysis, GPT-3.5 Turbo Instruct, Catboost Classifier

[1]. Rajendran, R., Venkatraman, A., Raghavan, V., & Subramanian, V. (2023). Smart Healthcare: A Comprehensive Review of Wearable Devices and IoT Applications. Journal of Medical Systems.
[2]. Sharma, A., Mehra, R., Kumar, A., & Singh, P. (2023). Telemedicine in Mental Health: A Review. Journal of Telemedicine and Telecare.
[3]. Mehendale, N. (2020). Facial Emotion Recognition using Convolutional Neural Networks (FERC). Springer link Volume 2, article number 446,
[4]. Meghrajani, V. R., Marathe, M., Sharma, R., Potdukhe, A., Wanjari, M. B., & Taksande, A. B. (2023). A Comprehensive Analysis of Mental Health Problems in India and the Role of Mental Asylums. Cureus.; 15(7): e42559.
[5]. Zhang, P., & Kamel Boulos, M. N. (2023). Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges. Future Internet, 15(9), 286.

Abstract: The major purpose of technique of classifying the network traffic is to identify various kinds of applications or traffic data. The analysis of received data packets is performed due to its necessity in communication networks nowadays. There are diverse phases to classify the network traffic such as to pre-process the data, extract the attributes and perform the classification. The dataset is utilized for the input in the classification stage. This paper studies diverse ML techniques in order to classify the network traffic..

Keywords—Network Traffic, Machine learning, Feature Extraction

[1]. L. Garcia, G. Bartlett, S. Ravi, H. Ibrahim, W. Hardaker and E. Kline, "Explaining Deep Learning Models for Per-packet Encrypted Network Traffic Classification," 2022 IEEE International Symposium on Measurements & Networking (M&N), Padua, Italy, 2022, pp. 1-6, doi: 10.1109/MN55117.2022.9887744.
[2]. L. Garcia, G. Bartlett, S. Ravi, H. Ibrahim, W. Hardaker and E. Kline, "Explaining Deep Learning Models for Per-packet Encrypted Network Traffic Classification," 2022 IEEE International Symposium on Measurements & Networking (M&N), Padua, Italy, 2022, pp. 1-6, doi: 10.1109/MN55117.2022.9887744.
[3]. J. Zhang, F. Li and F. Ye, "Sustaining the High Performance of AI-Based Network Traffic Classification Models," in IEEE/ACM Transactions on Networking, vol. 31, no. 2, pp. 816-827, April 2023, doi: 10.1109/TNET.2022.3203227.
[4]. R. Ghanavi, B. Liang and A. Tizghadam, "Generative Adversarial Classification Network with Application to Network Traffic Classification," 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021, pp. 1-6, doi: 10.1109/GLOBECOM46510.2021.9685899.
[5]. P. Khandait, N. Hubballi and B. Mazumdar, "Efficient Keyword Matching for Deep Packet Inspection based Network Traffic Classification," 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS), Bengaluru, India, 2020, pp. 567-570, doi: 10.1109/COMSNETS48256.2020.9027353.

Abstract: An innovative feature selection method for improving diabetic retinopathy (DR) model classification accuracy is introduced in this research. A high-dimensional dataset is created while evaluating a wide variety of patient health characteristics for a diabetes diagnosis, which poses a challenge to computing efficiency and model performance. The goal of the study is to minimise the dimensionality of the dataset while keeping the most useful features for classification tasks by using an optimised feature selection technique. In order to choose the smallest possible collection of characteristics that maximises classification accuracy, this method combines cutting-edge machine learning algorithms with statistical analysis. Results from experiments conducted using diabetes datasets that are accessible to the....

KEYWORDS: Diabetes mellitus (DM), diabetic retinopathy, RNN and CNN

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