Data transmission system with encryption by chaotic sequences by R. L. Politans’kyy, P. M. Shpatar, A. V. Hres, A. D. Verigha

“…Xn transmission period depends on computer speed and distance between subscribers of the system. …”
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Artificial Intelligence Driven Human Identification by Purushottam Sharma, Aazar Imran Khan, Samyak Jain, Abhishek Srivastava

“…This is achieved by incorporating computer vision, digital image processing, vectorization, artificial intelligence, and multi-threading. …”
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Detection of kidney bean leaf spot disease based on a hybrid deep learning model by Yiwei Wang, Qianyu Wang, Yue Su, Binghan Jing, Meichen Feng

“…However, traditional machine learning methods face limitations in feature extraction, while deep learning approaches, despite their advantages, are computationally expensive and do not always yield optimal results. …”
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Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers by Raja Azlina Raja Mahmood, AmirHossien Abdi, Masnida Hussin

“…These evolutionary-based algorithms are known to be effective in solving optimization problems.  …”
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Mitigating Transmission Errors: A Forward Error Correction-Based Framework for Enhancing Objective Video Quality by Muhammad Babar Imtiaz, Rabia Kamran

“…This is achieved by combining H.264/AVC syntax-based encryption and decryption algorithms with error correction during the video coding process to provide end-to-end confidentiality. …”
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A critical evaluation of Pure Pursuit, MPC and MPCC: Balancing simplicity, performance and constraints by Kong Xiangyiming, Fisher Callen, Evans Benjamin

“…In contrast to the simulated findings, PP demonstrated superior performance in physical implementations compared to MPC and MPCC. The computational demands of optimization-based algorithms result in a computation delay where the algorithm calculates for the vehicle's position one iteration behind its actual movement. …”
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Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data by Rui Li MS, Xiaoyan Hao MS, Yanjun Diao MD, Liu Yang MS, Jiayun Liu MD

“…Shapley additive explanations (SHAP) plots indicated that FOBT, CEA, lymphocyte percentage (LYMPH%), and hematocrit (HCT) were the most significant features contributing to CRC diagnosis. Additionally, a computational tool for predicting CRC risk based on the optimal model was developed, designed for researchers with programming experience. …”
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Raising Awareness of Acute Kidney Injury: Unfolding the Truth by Aida Lydia

“…Considering the substantial impacts of AKI in ICU patients, early implementation of preventive measures should be an essential program which consists of developing AKI risk stratification in the ICU and encouraging the use of novel AKI biomarkers (TIMP-2, IGFBP-7, Cystatin C, IL-18,  KIM-1 and NGAL) as well as other risk stratification tools (clinical risk prediction scores, computer algorithms, furosemide stress test). Furthermore, after ICU patients have recovered, AKI survivors are more likely to develop chronic kidney disease (CKD) and end-stage kidney disease (ESKD), imposing significant morbidity in the future. …”
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Deep Reinforcement Learning-Based Impact Angle-Constrained Adaptive Guidance Law by Zhe Hu, Wenjun Yi, Liang Xiao

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Comparing the effect of pre-anesthesia clonidine and tranexamic acid on intraoperative bleeding volume in rhinoplasty: a machine learning approach by Zahra Asghari Varzaneh, Akram Hemmatipour, Hadi Kazemi-Arpanahi

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Enhancing Crop Yield Prediction Using IoT-Based Soil Moisture and Nutrient Sensors by Alsalami Zaid, Mohammed G., Srinivas Tummala

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Digital twin empowered lightweight and efficient blockchain for dynamic internet of vehicles by Haoye Chai, Supeng Leng, Jianhua He, Ke Zhang

“…The decentralized LPMA-DDPG algorithm is helpful for implementing the optimal DT construction strategy in practical ITS.…”
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Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor–Recipient Matching by Nahed Alowidi, Razan Ali, Munera Sadaqah, Fatmah M. A. Naemi

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Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening by Keisuke Uchikawa, Kairi Furui, Masahito Ohue

“…The exploration process is guided by iterative ligand docking simulations, with mutation strategies optimized either by a genetic algorithm or via random search. …”
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Enhancing Crowd Safety at Hajj: Real-Time Detection of Abnormal Behavior Using YOLOv9 by Amani A. Alsabei, Tahani M. Alsubait, Hosam H. Alhakami

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Efficient and real-time lane detection using CUDA-based implementation by El Boussaki Hoda, Latif Rachid, Saddik Amine

“…However, given its high computational demands, optimization of this process is essential for real-time applications. …”
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A Target Near-Field Scattering Measurement Technique Utilizing 3D Near-Field Imaging via Cylindrical Scanning by Zongkai Yang, Jingcheng Zhao, Weikang Si, Changyu Lou, Xin Zhao, Jungang Miao

“…Nevertheless, the efficiency of current precision three-dimensional (3D) imaging algorithms in near-field scattering measurements is restricted by their substantial computational complexity. …”
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Active Inference-Driven Multi-Armed Bandits: Superior Performance through Dynamic Correlation Adjustments by Lin Xiaoqi

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Matroidal Structure of Rough Sets Based on Serial and Transitive Relations by Yanfang Liu, William Zhu

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Survey of FPGA based recurrent neural network accelerator by Chen GAO, Fan ZHANG

“…Recurrent neural network(RNN) has been used wildly used in machine learning field in recent years,especially in dealing with sequential learning tasks compared with other neural network like CNN.However,RNN and its variants,such as LSTM,GRU and other fully connected networks,have high computational and storage complexity,which makes its inference calculation slow and difficult to be applied in products.On the one hand,traditional computing platforms such as CPU are not suitable for large-scale matrix operation of RNN.On the other hand,the shared memory and global memory of hardware acceleration platform GPU make the power consumption of GPU-based RNN accelerator higher.More and more research has been done on the RNN accelerator of the FPGA in recent years because of its parallel computing and low power consumption performance.An overview of the researches on RNN accelerator based on FPGA in recent years is given.The optimization algorithm of software level and the architecture design of hardware level used in these accelerator are summarized and some future research directions are proposed.…”
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