Innovative computing and blockchain technologies in agriculture for accelerating economic integration among Russia, China, and the Republic of Belarus by A. Yu. Mikhailov

“…Cloud computing refers to distributed computing technologies that use algorithms for network access to shared computing resources. …”
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Thermally Stable Ag2Se Nanowire Network as an Effective In‐Materio Physical Reservoir Computing Device by Takumi Kotooka, Sam Lilak, Adam Z. Stieg, James K. Gimzewski, Naoyuki Sugiyama, Yuichiro Tanaka, Takuya Kawabata, Ahmet Karacali, Hakaru Tamukoh, Yuki Usami, Hirofumi Tanaka

“…This study explored a material physical reservoir consisting of a material random network, called in‐materio physical reservoir computing (RC) to achieve efficient hardware systems. …”
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A Novel Image Encryption Algorithm Based on a Fractional-Order Hyperchaotic System and DNA Computing by Taiyong Li, Minggao Yang, Jiang Wu, Xin Jing

“…To further enhance the security of image, in this paper, we propose a novel algorithm that combines the fractional-order hyperchaotic Lorenz system and DNA computing (FOHCLDNA) for image encryption. Specifically, the algorithm consists of four parts: firstly, we use a fractional-order hyperchaotic Lorenz system to generate a pseudorandom sequence that will be utilized during the whole encryption process; secondly, a simple but effective diffusion scheme is performed to spread the little change in one pixel to all the other pixels; thirdly, the plain image is encoded by DNA rules and corresponding DNA operations are performed; finally, global permutation and 2D and 3D permutation are performed on pixels, bits, and acid bases. …”
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Transforming Precision Agriculture with Quantum Computing: A Novel Algorithm for Boosting Crop Yields and Optimizing Resources by Mukherjee Anshit, Basu Mallik Biswadip

“…The algorithm makes use of qubits to allow the real time data processing through IoT sensors to allow for real time monitoring and decision making. …”
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High‐Performance Synapse Arrays for Neuromorphic Computing via Floating Gate‐Engineered IGZO Synaptic Transistors by Junhyeong Park, Yumin Yun, Sunyeol Bae, Yuseong Jang, Seungyoon Shin, Soo‐Yeon Lee

“…Abstract Neuromorphic computing emulating the human brain offers a promising alternative to the Von Neumann architecture. …”
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Cryo-SIMPLY: A Reliable STT-MRAM-Based Smart Material Implication Architecture for In-Memory Computing by Tatiana Moposita, Esteban Garzón, Adam Teman, Marco Lanuzza

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Smart distributed data factory volunteer computing platform for active learning-driven molecular data acquisition by Tsolak Ghukasyan, Vahagn Altunyan, Aram Bughdaryan, Tigran Aghajanyan, Khachik Smbatyan, Garegin A. Papoian, Garik Petrosyan

“…SDDF uses volunteer computing, leveraging the processing power of personal computers worldwide to accelerate quantum chemistry (DFT) calculations. …”
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Polarity-tunable dye-sensitized optoelectronic artificial synapses for physical reservoir computing-based machine vision by Hiroaki Komatsu, Norika Hosoda, Takashi Ikuno

“…Abstract Conventional machine vision systems process huge time-series data per second, presenting significant challenges for edge-device applications due to limitations in data transfer and storage. …”
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Enhancing DDoS Attacks Mitigation Using Machine Learning and Blockchain-Based Mobile Edge Computing in IoT by Mahmoud Chaira, Abdelkader Belhenniche, Roman Chertovskih

“…In this paper, we present a novel approach to DDoS attack detection and mitigation that integrates state-of-the-art machine learning techniques with Blockchain-based Mobile Edge Computing (MEC) in IoT environments. Our solution leverages the decentralized and tamper-resistant nature of Blockchain technology to enable secure and efficient data collection and processing at the network edge. …”
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Transmission Line Anomaly Detection and Real-Time Monitoring System Combining Edge Computing and EfficientDet by Menghao Lin, Yang Ding, Tianle Wang, Yang Liu, Zewei Li

“…Secondly, in order to improve the speed of model detection, the convolution operation process for obtaining data features based on Ghost lightweight module is reconstructed. …”
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Risk management strategy for generative AI in computing education: how to handle the strengths, weaknesses, opportunities, and threats? by Niklas Humble

“…Findings of the study have both theoretical and practical implications for computing education specifically, and higher education in general. …”
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Dynamic Split Computing Framework for Multi-Task Learning Models: A Deep Reinforcement Learning Approach by Haneul Ko, Sangwon Seo, Sangheon Pack

“…Subsequently, an IoT device makes decisions regarding the split computing of the head and tail models. To minimize the average task completion time across multiple tasks and the energy consumption of the IoT device, we formulate a Markov decision process problem. …”
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A Systematic Literature Review on Load-Balancing Techniques in Fog Computing: Architectures, Strategies, and Emerging Trends by Danah Aldossary, Ezaz Aldahasi, Taghreed Balharith, Tarek Helmy

“…Fog computing has emerged as a promising paradigm to extend cloud services toward the edge of the network, enabling low-latency processing and real-time responsiveness for Internet of Things (IoT) applications. …”
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Communication-efficient and privacy-preserving federated learning for medical image classification in multi-institutional edge computing by Nithin Melala Eshwarappa, Hojjat Baghban, Ching-Hsien Hsu, Po-Yen Hsu, Ren-Hung Hwang, Mu-Yen Chen

“…This indicates reduced processing time, which is particularly crucial in the field of medical diagnostics. …”
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Hardware-friendly implementation of physical reservoir computing with CMOS-based time-domain analog spiking neurons by Nanako Kimura, Ckristian Duran, Zolboo Byambadorj, Ryosho Nakane, Tetsuya Iizuka

“…Moreover, another technical advantage thanks to the bottom–up integration is the capability of dynamically capturing every neuron state in the network, which can significantly contribute to finding guidelines on how to enhance the performance for various computational tasks in temporal information processing. …”
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Enhancing Vehicle Location Prediction Accuracy with Road-Aware Rectification for Multi-Access Edge Computing Applications by Asif Mehmood, Afaq Muhammad, Faisal Mehmood, Wang-Cheol Song

“…In future 6G networks, real-time and accurate vehicular data are key requirements for enhancing the data-driven multi-access edge computing (MEC) applications. Existing estimation techniques to forecast vehicle position aim to meet the real-time data needs but compromise accuracy due to a lack of context awareness. …”
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Developing an explainable machine learning and fog computing-based visual rating scale for the prediction of dementia progression by Zainab H. Ali, Esraa Hassan, Shimaa Elgamal, Nora El-Rashidy

“…Abstract Recently, dementia research has primarily concentrated on using Magnetic Resonance Imaging (MRI) to develop learning models in processing and analyzing brain data. However, these models often cannot provide early detection of affected brain regions. …”
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Machine learning for handover decision with mobile edge computing in 6G mobile network: a survey by Soule Issa Loutfi, Ibraheem Shayea, Ufuk Tureli, Ayman A. El-Saleh, Waheeb Tashan, Ramazan Caglar

“…The case becomes more critical with the implementation of Mobile Edge Computing (MEC), which will lead to making the handover decision process more challenging due to its characterization and high requirements. …”
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Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing by Ruijin Sun, Yao Wen, Nan Cheng, Wei Wang, Rong Chai, Yilong Hui

“…Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources. …”
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Multi-user joint task offloading and resource allocation based on mobile edge computing in mining scenarios by Siqi Li, Weidong Li, Wanbo Zheng, Yunni Xia, Kunyin Guo, Qinglan Peng, Xu Li, Jiaxin Ren

“…Given the limited computational capacity of terminal devices, this paper optimizes the offloading decision-making process between terminal devices and MEC servers. …”
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