SVM directed machine learning classifier for human action recognition network by Dharmanna Lamani, Pramod Kumar, A Bhagyalakshmi, J. Maria Shanthi, Lakshmana Phaneendra Maguluri, Mohammad Arif, C Dhanamjayulu, Sathish Kumar. K, Baseem Khan

“…However, existing approaches such as three-dimensional convolutional neural networks (3D CNN) and two-stream neural networks (2SNN) have computational hurdles due to the significant parameterization they require. …”
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An empirical study of LLaMA3 quantization: from LLMs to MLLMs by Wei Huang, Xingyu Zheng, Xudong Ma, Haotong Qin, Chengtao Lv, Hong Chen, Jie Luo, Xiaojuan Qi, Xianglong Liu, Michele Magno

“…Abstract The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. …”
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Graph Neural Network-Based Approach for Detecting False Data Injection Attacks on Voltage Stability by Shahriar Rahman Fahim, Rachad Atat, Cihat Kececi, Abdulrahman Takiddin, Muhammad Ismail, Katherine R. Davis, Erchin Serpedin

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Microstructure evolution and mechanical properties degradation behavior of nuclear power pressure vessel steel induced by prolonged thermal aging by Tengfei Wang, Junkang Gao, Shaohong Li, Jun Li, Junwan Li, Hengyong Bu, Huarong Qi, Zhimin Li, Mengnie Li

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Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis by Shahd M. Noman, Youssef M. Fadel, Mayar T. Henedak, Nada A. Attia, Malak Essam, Sarah Elmaasarawii, Fayrouz A. Fouad, Esraa G. Eltasawi, Walid Al-Atabany

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Leveraging two-dimensional pre-trained vision transformers for three-dimensional model generation via masked autoencoders by Muhammad Sajid, Kaleem Razzaq Malik, Ateeq Ur Rehman, Tauqeer Safdar Malik, Masoud Alajmi, Ali Haider Khan, Amir Haider, Seada Hussen

“…Masking autoencoding is a promising self-supervised learning approach that greatly advances computer vision and natural language processing. For robust 2D representations, pre-training with large image data has become standard practice. …”
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Design of High Performance Hybrid Type Digital-Feedback Low Drop-Out Regulator Using SSCG Technique by Muhammad Asif, Imran Ali, Danial Khan, Muhammad Riaz Ur Rehman, Younggun Pu, Sang-Sun Yoo, Kang-Yoon Lee

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SGCL-LncLoc: An Interpretable Deep Learning Model for Improving lncRNA Subcellular Localization Prediction with Supervised Graph Contrastive Learning by Min Li, Baoying Zhao, Yiming Li, Pingjian Ding, Rui Yin, Shichao Kan, Min Zeng

“…Additionally, we propose a computational method to map the attention weights of the graph nodes to the weights of nucleotides in the lncRNA sequence, allowing SGCL-LncLoc to serve as an interpretable deep learning model. …”
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Enhancing Drought Forecast Accuracy Through Informer Model Optimization by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu, Yang Guo

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Gradient diffusion entropy corrected ALNS optimization for vegetation topology interaction networks by Shengwei Wang, Hongquan Chen, Yulin Guo, Wenjing Su, Yurong Xu, Shuohao Cui, Zhiqiang Zhou

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Exploring the factors influencing the co-pyrolysis of walnut shells, pistachio shells, and polypropylene based on thermal behavior, kinetics and reaction mechanism by Zhigang Huang, Tong Zhang, Chen Wang, Wenbo Xiao, Zihan Wang, Obid Tursunov, Jiahui Wu, Yuguang Zhou, Xiaolong Yao, Gang Li

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Comprehensive Analysis of Word Embedding Models and Design of Effective Feature Vector for Classification of Amazon Product Reviews by B. Priya Kamath, M. Geetha, U. Dinesh Acharya, Dipesh Singh, Ayush Rao, Shwetha Rai, Roopashri Shetty

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Snake-DETR: a lightweight and efficient model for fine-grained snake detection in complex natural environments by Heng Wang, Shuai Zhang, Cong Zhang, Zheng Liu, Qiuxian Huang, Xinyi Ma, Yiming Jiang

“…Compared to other state-of-the-art models, Snake-DETR achieved an accuracy of 97.66%, a recall rate of 93.92%, mAP@0.5 of 95.23%, and mAP@0.5:0.95 of 72.15%, all outperforming other algorithms in the comparative tests. Furthermore, the computational load and parameter count of the model are reduced by 47.2 and 52.2%, respectively, compared to the benchmark model. …”
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Dynamic Key Replacement Mechanism for Lightweight Internet of Things Microcontrollers to Resist Side-Channel Attacks by Chung-Wei Kuo, Wei Wei, Chun-Chang Lin, Yu-Yi Hong, Jia-Ruei Liu, Kuo-Yu Tsai

“…The lightweight D-H key exchange ensures that even resource-constrained IoT devices can securely perform key exchanges without significant computational overhead. Experimental results demonstrate the practicality and security of the proposed mechanism, achieving key updates with minimal time overhead, ranging from 12 to 50 milliseconds per encryption transmission. …”
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THE DETERMINATION OF THE SHARPNESS DEPTH BORDERS AND CORRESPONDING PHOTOGRAPHY AND VIDEO RECORDING PARAMETERS FOR CONTEMPORARY VIDEO TECHNOLOGY by E. G. Zaytseva, A. A. Kisljuk

“…The method of determination of the sharpness depth borders was improved for contemporary video technology. The computer programme for determination of corresponding video recording parameters was created.…”
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The Boolean zeta function by Algirdas Javtokas

“…., xm) function known in theoretical computer science. …”
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Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential by Shuliang Wang, Kanokwan Malang, Hanning Yuan, Aniwat Phaphuangwittayakul, Yuanyuan Lv, Matthew David Lowdermilk, Jing Geng

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Zero-day exploits detection with adaptive WavePCA-Autoencoder (AWPA) adaptive hybrid exploit detection network (AHEDNet) by Ahmed A. Mohamed, Abdullah Al-Saleh, Sunil Kumar Sharma, Ghanshyam G. Tejani

“…Abstract This paper introduces a new probabilistic composite model for the detection of zero-day exploits targeting the capabilities of existing anomaly detection systems in terms of accuracy, computational time, and adaptability. To address the issues mentioned above, the proposed framework consisted of three novel elements. …”
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Knowledge Graph-Enhanced Digital Twin Framework for Optimized Job Shop Scheduling in Smart Manufacturing by Nehal Tarek, Abeer D. Algarni, Nancy A. El-Hefnawy, Hatem Abdel-Kader, Amira Abdelatey

“…The second algorithm uses the results of the first algorithm to calculate the running time and energy consumption for each machine in each production line, considering that the production lines of the factory vary in capacity and power, then computes the total energy consumption and presents the results to the decision maker via a dashboard. …”
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STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction by Wei Huo, Xiaodan Zhang, Shaojie You, Yongkun Zhang, Qiyuan Zhang, Naihao Hu

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