CRYPTO-RESISTANT METHODS AND RANDOM NUMBER GENERATORS IN INTERNET OF THINGS (IOT) DEVICES by Petro Klimushyn, Tetiana Solianyk, Oleksandr Mozhaiev, Yurii Gnusov, Oleksandr Manzhai, Vitaliy Svitlychny

“…The analysis of technologies and circuit solutions allowed to draw the following conclusions: protection of IoT solutions includes: security of IoT network nodes and their connection to the cloud using secure protocols, ensuring confidentiality, authenticity and integrity of IoT data by cryptographic methods, attack analysis and network cryptographic stability; the initial basis for the protection of IoT solutions is the true randomness of the formed RNG sequences and used in algorithms for cryptographic transformation of information to protect it; feature of IoT devices is their heterogeneity and geographical distribution, limited computing resources and power supply, small size; The most effective (reduce power consumption and increase the generation rate) for use in IoT devices are RNG exclusively on a digital basis, which implements a three-stage process: the initial digital circuit, normalizer and random number flow generator; Autonomous Boolean networks (ABN) allow to create RNG with unique characteristics: the received numbers are really random, high speed – the number can be received in one measure, the minimum power consumption, miniature, high (up to 3 GHz) throughput of Boolean chaos; a promising area of ABN development is the use of optical logic valves for the construction of optical ABN with a bandwidth of up to 14 GHz; the classification of known classes of RNG attacks includes: direct cryptanalytic attacks, attacks based on input data, attacks based on the disclosure of the internal state of RNG, correlation attacks and special attacks; statistical test packages to evaluate RNG sequences have some limitations or shortcomings and do not replace cryptanalysis; Comparison of cryptoaccelerators with cryptographic transformation software shows their significant advantages: for AES block encryption algorithm, speeds increase by 10-20 times in 8/16-bit cryptoaccelerators and 150 times in 32-bit, growth hashing of SHA-256 in 32-bit cryptoaccelerators more than 100 times, and for the NMAS algorithm - up to 500 times. …”
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OPTISTACK: A Hybrid Ensemble Learning and XAI-Based Approach for Malware Detection in Compressed Files by Khaled Mahmud Sujon, Rohayanti Binti Hassan, M. Abdullah-Al-Wadud, Jia Uddin

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A hybrid data-driven method for voltage state prediction and fault warning of Li-ion batteries by Yufeng Huang, Xuejian Gong, Zhiyu Lin, Lei Xu

“…As the extensive application of electrochemical energy storage (EES), Li-ion battery fault is a key factor reference to the reliable operation and system security, influencing by the environment temperature and battery voltage. …”
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Through-wall Human Pose Reconstruction and Action Recognition Using Four-dimensional Imaging Radar by Rui ZHANG, Hanqin GONG, Ruiyuan SONG, Yadong LI, Zhi LU, Dongheng ZHANG, Yang HU, Yan CHEN

“…Through-wall human pose reconstruction and behavior recognition have enormous potential in fields like intelligent security and virtual reality. However, existing methods for through-wall human sensing often fail to adequately model four-Dimensional (4D) spatiotemporal features and overlook the influence of walls on signal quality. …”
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Machine Learning and Deep Learning for Crop Disease Diagnosis: Performance Analysis and Review by Habiba Njeri Ngugi, Andronicus A. Akinyelu, Absalom E. Ezugwu

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Improving internet of vehicles research: A systematic preprocessing framework for the VeReMi datasetZenodo by Aparup Roy, Debotosh Bhattacharjee, Ondrej Krejcar

“…The optimized dataset is well-suited for ITS and IoV applications, such as anomaly detection and network security, underscoring the crucial role of preprocessing in overcoming real-world constraints and enhancing model performance.…”
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Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance by Firgiawan Faira, Dandy Pramana Hostiadi

“…Industry 4.0 requires secure networks as the advancements in IoT and AI exacerbate the challenges and vulnerabilities in data security. …”
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Survey of deep fake audio generation and detection techniques by ZENG Zhiping, ZHANG Xulong, QU Xiaoyang, XIAO Chunguang, WANG Jianzong

“…Subsequently, an in-depth analysis was conducted on both acoustic feature-based and end-to-end model-based fake audio detection strategies, delving into details such as deep acoustic feature detection, pre-trained neural network feature detection, end-to-end model optimization, generalization enhancement techniques, and the enhancement of real-time detection. …”
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InterAcT: A generic keypoints-based lightweight transformer model for recognition of human solo actions and interactions in aerial videos. by Mubashir Shah, Tahir Nawaz, Rab Nawaz, Nasir Rashid, Muhammad Osama Ali

“…To this end, this paper presents a generic lightweight and computationally efficient Transformer network-based model, referred to as InterAcT, that relies on extracted bodily keypoints using YOLO v8 to recognize human solo actions as well as interactions in aerial videos. …”
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Issues, Challenges, and Solution Options for On-Grid Multi-Microgrid Game Theory: A Systematic Review by Dimas Jalaluddin Ahmad, Nanang Hariyanto, Umar Khayam

“…This connection provides more flexibility in operation and various features, including multi-level interactions beyond peer-to-peer, asymmetric agents, the presence of distribution network operator, support and backup power from the grid, and an energy market pool. …”
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LiSA-MobileNetV2: an extremely lightweight deep learning model with Swish activation and attention mechanism for accurate rice disease classification by Yongqi Xu, Dongcheng Li, Changcheng Li, Zheming Yuan, Zhijun Dai

“…In the context of intelligent agriculture in China, rapid and accurate identification of crop diseases is essential for ensuring food security and improving crop yield. Although lightweight convolutional neural networks (CNNs) are widely adopted for plant disease recognition due to their computational efficiency, they often suffer from limited feature representation and classification accuracy. …”
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MA-YOLO: A Pest Target Detection Algorithm with Multi-Scale Fusion and Attention Mechanism by Yongzong Lu, Pengfei Liu, Chong Tan

“…To address the high computational complexity and inadequate feature representation in traditional convolutional networks, this study proposes MA-YOLO, an agricultural pest detection model based on multi-scale fusion and attention mechanisms. …”
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An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment by M. Karpagam, S. Sarumathi, A. Maheshwari, K. Vijayalakshmi, K. Jagadeesh, V. Bereznychenko, R. Narayanamoorthi

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Crop yield prediction using machine learning: An extensive and systematic literature review by Sarowar Morshed Shawon, Falguny Barua Ema, Asura Khanom Mahi, Fahima Lokman Niha, H.T. Zubair

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An intelligent optimized object detection system for disabled people using advanced deep learning models with optimization algorithm by Marwa Obayya, Fahd N. Al-Wesabi, Menwa Alshammeri, Huda G. Iskandar

“…Furthermore, the MobileNetV3 model is utilized for the feature extraction process. The temporal convolutional network (TCN) model is implemented for classification. …”
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PHRF-RTDETR: a lightweight weed detection method for upland rice based on RT-DETR by Xianjin Jin, Jinheng Zhang, Fei Wang, Mengyan Zhao, Yunshuang Wang, Jianping Yang, Jinfeng Wu, Bing Zhou

“…To address this issue, we enhanced the baseline model RT-DETR and proposed a lightweight weed detection model for upland rice, named PHRF-RTDETR.MethodsFirst, we propose a novel lightweight backbone network, termed PGRNet, to replace the original computationally intensive feature extraction network in RT-DETR. …”
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A Novel Deep 2D-CNN Model for ECG-Based Arrhythmia Diagnosis with Selective Attention Mechanism and CWT Integration by Hassanain Shakir Mansour, Morteza Valizadeh, Alaa Hussein Abdulaal, Mehdi Chehel Amirani

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A survey of data-driven fault-diagnosis methods for large-scale industrial production processes by Qianxiang YU, Qing LI, Linlin LI, Yixuan WANG

“…The usual fault-diagnosis methods for large-scale systems rely on centralized sensor network monitoring. Centralization necessitates consolidated data processing, which can create immense computational stress. …”
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Research on a machine vision-based electro-killing pheromone-baited intelligent agricultural pest monitoring method by Guozhi Li, Zhengbo Liu, Zhengbo Liu, Zelin Feng, Jun Lyu, Bin Li, Guo Chen, Qing Yao

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Efficient Anomaly Detection for Edge Clouds: Mitigating Data and Resource Constraints by Javad Forough, Hamed Haddadi, Monowar Bhuyan, Erik Elmroth

“…Anomaly detection plays a vital role in ensuring the security and reliability of edge clouds, which are decentralized computing environments with limited resources. …”
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