Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors by Abubakar Wakili, Sara Bakkali, Ahmed El Hilali Alaoui

“…This paper presents a novel machine learning-based framework to enhance RPL's security and QoS. …”
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Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques by Zheshu Jia, Deyun Chen

“…The segmenting function is distinguished by a high level of uniformity between anatomy and the neighboring brain tissue. ELM is a type of learning algorithm consisting of one or more layers of hidden nodes. …”
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Federated Reinforcement Learning in Stock Trading Execution: The FPPO Algorithm for Information Security by Haogang Feng, Yue Wang, Shida Zhong, Tao Yuan, Zhi Quan

“…Stock trading execution is a critical component in the complex financial market landscape, and the development of a robust trade execution framework is essential for financial institutions pursuing profitability. This paper presents the Federated Proximal Policy Optimization (FPPO) algorithm, an adaptive trade execution framework that leverages joint reinforcement learning. …”
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An Auction-Based Bid Prediction Mechanism for Fog-Cloud Offloading Using Q-Learning by Reza Besharati, Mohammad Hossein Rezvani, Mohammad Mehdi Gilanian Sadeghi

“…We propose a bid prediction mechanism using Q-learning. Nodes participating in the auction announce a bid value to the auctioneer entity, and the node with the highest bid value is the auction winner. …”
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A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime by Abhay R. Gaidhani, Amol D. Potgantwar

“…Most nodes may be reliably expected to operate for the duration of their batteries. …”
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Open-World Semi-Supervised Learning for fMRI Analysis to Diagnose Psychiatric Disease by Chang Hu, Yihong Dong, Shoubo Peng, Yuehan Wu

“…In the context of graph-based mental disorder classification, open-world semi-supervised learning for node classification aims to classify unlabeled nodes into known classes or potentially new classes, presenting a practical yet underexplored issue within the graph community. …”
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Research on Optimized Algorithm for Deep Learning Based Recognition of Sediment Particles in Turbulent Flow by WANG Hao, YANG Feiqi, ZHANG Lei, WU Wei, XIE Haonan, ZHAO Lin

“…Analyses of cumulative centroid counts and particle chain node counts reveal an ascending trend as the number of frames increases. …”
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Trust-driven approach to enhance early forest fire detection using machine learning by Tayyab Khan, Karan Singh, Bhoopesh Singh Bhati, Khaleel Ahmad, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…The detrimental impacts of forest fires, such as the exacerbation of the greenhouse effect, the hastening of global warming, and the modification of climatic patterns, underscore the urgent necessity for the creation of efficient detection systems. This study presents a real-time Universal Trust Model (UTM) that is specifically designed for the early forest fires detection (FFD) using an intelligent wireless sensor network and machine learning approaches. …”
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Reinforcement learning based route optimization model to enhance energy efficiency in internet of vehicles by Quadeer Hussain, Ahmad Shukri Mohd Noor, Muhammad Mukhtar Qureshi, Jianqiang Li, Atta-ur Rahman, Aghiad Bakry, Tariq Mahmood, Amjad Rehman

“…Specifically, our model extends the network lifetime, delays the occurrence of the first dead node, and maintains a higher residual energy rate. …”
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Reservoir Computing for Few-Mode Fiber Channel OSNR Monitoring in Deep Learning Frameworks by Jianjun Li, Tianfeng Zhao, Baojian Wu, Kun Qiu, Feng Wen

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Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data by Sheraz Aslam, Alejandro Navarro, Andreas Aristotelous, Eduardo Garro Crevillen, Alvaro Martınez-Romero, Álvaro Martínez-Ceballos, Alessandro Cassera, Kyriacos Orphanides, Herodotos Herodotou, Michalis P. Michaelides

“…Towards this end, this study presents a machine learning (ML)-based approach for predicting faults in CHE to improve equipment reliability and overall port performance. …”
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Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach by Dan Ben Ami, Kobi Cohen, Qing Zhao

“…Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchanging the data. …”
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Lessons learned from hybrid surgery with preoperative coil embolization for an aberrant artery in pulmonary sequestration by Keita Nakanishi, Masaki Goto, Shota Nakamura, Toyofumi Fengshi Chen-Yoshikawa

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Machine Learning for Anomaly Detection in Blockchain: A Critical Analysis, Empirical Validation, and Future Outlook by Fouzia Jumani, Muhammad Raza

“…These attacks can be detected by analyzing the anomalous node behavior of miner nodes in the network, and data analysis plays a vital role in detecting and overcoming these attacks to make a secure blockchain. …”
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Comparative analysis of the performance of regression machine learning models for indoor visible light positioning systems by Mohamed Hussien Moharam

“…Abstract This paper presents an Indoor Visible Light Positioning (VLP) system designed for deployment in enclosed environments, using four ceiling-mounted Light Emitting Diodes (LEDs) to serve both illumination and positioning functions. …”
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Influence maximization under imbalanced heterogeneous networks via lightweight reinforcement learning with prior knowledge by Kehong You, Sanyang Liu, Yiguang Bai

“…In this work, we introduce the Lightweight Reinforcement Learning algorithm with Prior knowledge (LRLP), which leverages the Struc2Vec graph embedding technique that captures the structural similarity of nodes to generate vector representations for nodes within the network. …”
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An Efficient and Hybrid Deep Learning-Driven Model to Enhance Security and Performance of Healthcare Internet of Things by Muhammad Babar, Muhammad Usman Tariq, Basit Qureshi, Zabeeh Ullah, Fahim Arif, Zahid Khan

“…Furthermore, reducing superfluous data and identifying effective fog nodes are difficult tasks. We provide a novel Software-Defined Networking (SDN)- driven Deep Learning (DL) approach to develop a secure, intelligent, and efficient framework for smart H-CIoT networks in order to address the aforementioned challenges. …”
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Cross-Project Defect Prediction Using Transfer Learning with Long Short-Term Memory Networks by Hongwei Tao, Lianyou Fu, Qiaoling Cao, Xiaoxu Niu, Haoran Chen, Songtao Shang, Yang Xian

“…Finally, the semantic features and metric features are combined and input into the logical regression (LR) classifier for training. The presented TLSTM model performs better on the f-measure indicator than other machine and deep learning models, according to the outcomes of several open-source projects of the PROMISE repository. …”
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Integrative machine learning model for subtype identification and prognostic prediction in lung squamous cell carcinoma by Guangliang Duan, Qi Huo, Wei Ni, Fei Ding, Yuefang Ye, Tingting Tang, Huiping Dai

“…The relationship between survival outcomes and these identified subtypes was validated in the testing sets using binary machine learning models and survival curve analysis. The impact of chemotherapy on the prognosis for subtypes was also presented. …”
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An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs by Zhen Wang, Jin Duan

“…To overcome this challenge and enhance network lifespan, this study presents FQ-UCR, a hybrid approach that merges unequal clustering based on fuzzy logic (FL) with routing optimized through Q-learning. …”
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