iosr-JCE   Volume-25 ~ Issue-6 ~ Nov. - Dec. 2023

Abstract: One of the most rapidly growing wireless technologies is the so called voice over internet protocol (VoIP). VoIP does not only find application in wireless networks but also in wired networks. This is majorly because of the fact that VoIP is very much compatible with devices of IEEE 802.11 standard. Besides, its compatibility with the carrier sense multiple access with collision avoidance (CSMA/CA) makes VoIP a preferred technique for use in wireless communications. This is also because it functions well in 2nd layer of data link layer. Among the various algorithms used for VoIP is the distributed coordination function (DCF). But this algorithm suffers from packet collision.....

Key Word: Artificial neural network, Distributed coordination function, Packet loss, VoIP

[1]. Alrammahi M.A.M. & Kim H.A.H.H.A. (2018). Enhancement And Performance Analysis Of Voip Algorthms In Wireless Networks, International Journal Of Computer Techniques, 5(2), 74-82
[2]. Kim J., Niyaz Q, & Javaid A.Y. (2014). Performance Evaluation Of Voip Broadcasting Over Lte For Varying Speeds And Distance Of Mobile Nodes. Ieee International Symposium On Broadband Multimedia Systems And Broadcasting. 1-5.
[3]. Kumar S. & Anil K.V. (2015). Position Based Routing Protocols In Vanet: A Survey. Wireless Personal Communications.
[4]. Affonso E.T., Nunes R.D., Rosa R.L., Pivaro G.F. & Rodriguez D.Z. (2018). Speech Quality Assessment In Wireless Voip Communication Using Deep Belief Network, Ieee Access, 6, 77022-77032. Doi: 10.1109/Access.2018.2871072
[5]. Li F.F. (2004). Speech Intelligibility Of Voip To Pstn Interworking-A Key Index For The Qos. Printed And Published By The Iee (The Institute Of Electrical Engineers), 104-108

Abstract: Weather forecasting is a scientific estimation of future weather conditions that applies science and technology to predict the state of the atmosphere for a given time and location. Rainfall forecasting is one of the most difficult and important weather forecasting tasks for national meteorology services worldwide. The current statistical method used for rainfall forecasting in Zambia relies on a relationship between sea surface temperatures and station rainfall observations and ignores other factors that affect rainfall. Such statistical models have limitations for long range forecasts as correlations change over time and lose their significance. Furthermore, the station point rainfall observations used in the current method also have high spatial variability.....

Key Word: Artificial Intelligence; Artificial Neural Networks; Feed-Forward Back-Propagation

[1] Deepa Bisht, Mahesh C Joshi, And Ashish Mehta. Prediction Of Monthly Rainfall Of Nainital Region Using Artificial Neural Network (Ann) And Support Vector Machine (Svm). Ijariie. 2015 1(3):400–406.
[2] John Abbot And Jennifer Marohasy. Application Of Artificial Neural Networks To Rainfall Forecasting In Queensland, Australia. Advances In Atmospheric Sciences. 2012 (2943):717–730.
[3] Kumar Abhishek, Abhay Kumar, Rajeev Ranjan, And Sarthak Kumar. A Rainfall Prediction Model Using Artificial Neural Network. Ieee Control And System Graduate Research Colloquium (Icsgrc). 2012 4:82–87.
[4] Lubna Shaikh And Kirti Sawlani. A Rainfall Prediction Model Using Artificial Neural Network. International Journal Of Technical Research And Applications. 2017 5(2):45–48.
[5] Jignesh Patel And Falguni Parekh. Forecasting Rainfall Using Adaptive Neuro-Fuzzy Inference System (Anfis). Ijaiem. 2014 3(6):262–269.

Abstract: The extensive utilization of mobile devices, sensors, wireless sensor networks, and the growing Internet of Things (IoT) has inspired the medical and healthcare communities to recruit IoT to measure, gather data, and communicate with patients. Wireless body area networks are prominent in the medical field. Wireless body area networks comprise several wirelessly communicating wear- able or implanted devices. These conventionally assemble the physical facts of the wearer and verbalize them to a server. Recently, many protocols have been proposed with security-assisted health data transfer techniques. As a result, we propose a reliable authentication technique for WBAN. In this technique, hash and X-OR operation topologies are being utilized. And we have also shown that the Burrows-Abadi-Needham (BAN) logic accuracy of the mutual authentication between the patient and medical server is demonstrated using common sense.

Key Word: Internet of medical Things (IoMT), authentication, security, Wire- less body area networks.

[1] Mohammad Yaghoubi, Khandakar Ahmed, And Yuan Miao. Wireless Body Area Network (Wban): A Survey On Architecture, Technologies, Energy Consumption, And Security Challenges. Journal Of Sensor And Actuator Networks, 11(4):67, 2022.
[2] Okundu Omeni, Alan Chi Wai Wong, Alison J Burdett, And Christofer Toumazou. Energy Efficient Medium Access Protocol For Wireless Medical Body Area Sensor Networks. IEEE Transactions On Biomedical Circuits And Systems, 2(4):251–259, 2008.
[3] A Vinny Mary And S Jerine. Wireless Body Area Network Transmissions For Iot- Based Healthcare Network: A Review. In IOP Conference Series: Materials Science And Engineering, Volume 983, Page 012017. IOP Publishing, 2020.
[4] Bahae Abidi, Abdelillah Jilbab, And Mohamed EL Haziti. Wireless Sensor Net- Works In Biomedical: Wireless Body Area Networks. In Europe And MENA Cooper- Ation Advances In Information And Communication Technologies, Pages 321–329. Springer, 2017.
[5] M Raj Kumar Naik And P Samundiswary. Wireless Body Area Network Security Issues—Survey. In 2016 International Conference On Control, Instrumentation, Communication And Computational Technologies (ICCICCT), Pages 190–194. IEEE, 2016..

Abstract: Major technology companies like Amazon, Google, and Microsoft now provide cloud computing services that include artificial intelligence and machine learning functionalities. The cloud platforms of these corporations Azure, Google Cloud Platform, and Amazon Web Services (AWS) provide advanced AI and ML capabilities. Natural language processing (NLP), computer vision (CV), speech recognition (SR), and predictive analytics are among the many tasks that their pre-trained AI models, customised machine learning frameworks, and intuitive APIs can do. Amazon SageMaker, AWS......

Key Word: Cloud Computing, Cloud Based AI & ML

[1]. Abbadi, IM & Ruan, A (2013), “Towards trustworthy resource scheduling in clouds”, Vol. 8, Issue. 6, pp.973-984.
[2]. Yan, Zhenget al., (2011), “Advances in Mobile Cloud Computing and Big Data”, Springer Computational Intelligence and Complexity, ISBN 978- 3-319-45143-5.
[3]. PengLi; Song Guo; Toshiaki Miyazaki; Miao Xie; Jiankun Hu; WeihuaZhuang (2016), “Privacy- Preserving Access to Big Data in the Cloud”, IEEE Cloud Computing, Vol.3, No.5, pp.34 -42.
[4]. Yue-Qin, Fan, (2017), “Security and Privacy Challenges in Mobile Cloud Computing: Survey and Way Ahead”.
[5]. Lin, Guoyuanet,(2014),“Cloud service selection: State-of-the-art and future research directions", Elsevier, pp.134-150..

Abstract: Federated Learning (FL) is considered to be a suitable mechanism for privacy-preserving and distributed machine learning. While preserving decentralized data, it simultaneously protects global parameter updates. However, with all this advantage, FL reduces many securityprovisions thus opening a gateway for adversary AI. Here the adversary can manipulate local model updates or poison decentralized training data, hijacking the system. Some challenges that comeupincludemodelpoisoning,backdoorattacks......

Key Word: Federated Learning, Adversarial AI, Model Poisoning, Backdoor Attack, Gradient Inversion, Robust Aggregation, Anomaly Detection, Differential Privacy, Explainable AI (XAI), Trustworthy AI.

[1]. Ma, C., Li, J., Wei, K., Liu, B., Ding, M., Yuan, L., ...& Poor, H. V. (2023). Trusted AI in multiagent systems: An overview of privacy and security for distributed learning. Proceedings of the IEEE, 111(9), 1097-1132.
[2]. Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., ...& Yu, P. S. (2022). Privacy and robustness in federated learning: Attacks and defenses. IEEE Transactions on Neural Networks and Learning Systems.
[3]. Raza, A. (2023). Secure and privacy-preserving federated learning with explainable artificial intelligence for smart healthcare system. University of Kent (United Kingdom).
[4]. Kumar, K. N., Mohan, C. K., &Cenkeramaddi, L. R. (2023). The impact of adversarial attacks on federated learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(5), 2672-2691.
[5]. Gittens, A., Yener, B., & Yung, M. (2022). An adversarial perspective on accuracy, robustness, fairness, and privacy: Multilateral tradeoffs in trustworthy ML. IEEE Access, 10, 120850-120865.