A Lightweight Neural Network for Cell Segmentation Based on Attention Enhancement by Shuang Xia, Qian Sun, Yiheng Zhou, Zhaoyuxuan Wang, Chaoxing You, Kainan Ma, Ming Liu

“…AttE-Unet enhances the network’s feature extraction capabilities through an attention mechanism and combines the strengths of deep learning with traditional image filtering algorithms, while substantially reducing computational and storage demands. …”
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FLSH: A Framework Leveraging Similarity Hashing for Android Malware and Variant Detection by Hassan Jalil Hadi, Alina Khalid, Faisal Bashir Hussain, Naveed Ahmad, Mohammed Ali Alshara

“…To address this, various techniques and algorithms have been employed to improve malware detection and classification. …”
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Prediction of land cover changes in an Urban City of Bangladesh using artificial neural network-based cellular automata by Tania Yeasmin, Sourav Karmaker, Md Shafiqul Islam, Irteja Hasan, Saifur Rahman, Mahmudul Hasan

“…Supervised classification algorithms and cellular automata model based on Artificial Neural Networks (ANN) were used to prepare LULC maps and future simulations. …”
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Optimization Design of Space Camera Enclosure Based on Bionics by Hongyu Li, Fu Li, Zhihua Zhao, Janfeng Yang, Juan Lv

“…The results indicate that the biomimicry-inspired space camera enclosure structure, modeled after the tendons of <i>Victoria lindl</i>, can reduce weight by 36.9% compared to traditional designs, while maintaining high stiffness and fundamental frequency. …”
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Order picking dataset from a warehouse of a footwear manufacturing companyMendeley Data by Rodrigo Furlan de Assis, William de Paula Ferreira, Mustapha Ouhimmou

“…Anonymization and randomization techniques were applied while retaining realistic operational patterns to preserve confidentiality. …”
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Advanced fault detection in photovoltaic panels using enhanced U-Net architectures by Khalfalla Awedat, Gurcan Comert, Mustafa Ayad, Abdulmajid Mrebit

“…Extensive experiments demonstrate that the proposed method significantly outperforms all benchmarked algorithms across key segmentation metrics, including standard U-Net, U-Net with ASPP, and DeepLabV3+. …”
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MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph by Yinfei Dai, Yuantong Zhang, Xiuzhen Zhou, Qi Wang, Xiao Song, Shaoqiang Wang

“…With the rapid development of autonomous driving technology, multi-agent trajectory prediction has become the core foundation of autonomous driving algorithms. Efficiently and accurately predicting the future trajectories of multiple agents is key to evaluating the reliability and safety of autonomous driving vehicles. …”
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Microcontroller-Based Platform for Lithium-Ion Battery Charging and Experimental Evaluation of Charging Strategies by Laurentiu Marius Baicu, Mihaela Andrei, Bogdan Dumitrascu

“…A current sensor module ensures continuous monitoring, while an LCD interface provides real-time feedback on charging parameters. …”
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Secrecy-Constrained UAV-Mounted RIS-Assisted ISAC Networks: Position Optimization and Power Beamforming by Weichao Yang, Yajing Wang, Dawei Wang, Yixin He, Li Li

“…Specifically, we propose a secure ISAC system supported by a UAV-mounted RIS, where an ISAC base station (BS) facilitates secure multi-user communication while simultaneously detecting potentially malicious radar targets. …”
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Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm by Gaetanino Paolone, Romolo Paesani, Francesco Pilotti, Jacopo Camplone, Andrea Piazza, Paolino Di Felice

“…In addition to that, the review delves into recent research works focusing on the usage of Machine Learning (ML) algorithms in an edge Cloud-based computing architecture. …”
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Research on load frequency control system attack detection method based on multi-model fusion by Feng Zheng, Weixun Li, Huifeng Li, Libo Yang, Zengjie Sun

“…Abstract Load frequency control (LFC) in power systems faces increasingly complex cyber-physical attack threats, while existing detection methods have limited capability to identify intelligent attacks. …”
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Burnout Risk Profiles in Psychology Students: An Exploratory Study with Machine Learning by M. Graça Pereira, Martim Santos, Renata Magalhães, Cláudia Rodrigues, Odete Araújo, Dalila Durães

“…Using machine learning algorithms, two distinct profiles were found: “Burnout Risk” and “No Risk”. …”
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Quality monitoring of hybrid welding processes: A comprehensive review by Solomon Habtamu Tessema, Dariusz Bismor

“…Furthermore, the review highlights the integration of sensor data with AI/ML algorithms and digital twin technologies, enhancing the capabilities of quality monitoring systems. …”
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Smart Agile Prioritization and Clustering: An AI-Driven Approach for Requirements Prioritization by Aya M. Radwan, Manal A. Abdel-Fattah, Wael Mohamed

“…Various machine learning algorithms are tested, with KNN and Random Forest demonstrating the highest accuracy and lowest Mean Squared Error (MSE), outperforming traditional prioritization techniques. …”
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Upper canopy and understory phenology of Brazilian Amazon forests seen by GEDI lasers by Pedro V C Oliveira, Xiaoyang Zhang

“…The results revealed that upper canopy PAI consistently increased during early dry seasons, while understory PAI likely increased due to sunlight that penetrated the upper canopy during its leaf abscission period in the late dry season. …”
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A Comprehensive Framework for Transportation Infrastructure Digitalization: TJYRoad-Net for Enhanced Point Cloud Segmentation by Zhen Yang, Mingxuan Wang, Shikun Xie

“…The proposed method addresses the challenge of accurately capturing the current state of infrastructure while minimizing redundancy and optimizing computational efficiency. …”
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DBN-BAAE: Enhanced Lightweight Anomaly Detection Mechanism with Boosting Adversarial Autoencoder by Yanru Chen, Bei Wu, Wang Zhong, Yanru Guo, Dizhi Wu, Yi Ren, Yuanyuan Zhang

“…Although current research has advanced anomaly detection, it is still challenging for algorithms to be capable of effectively balancing the interplay between training speed, computational cost, and accuracy while simultaneously exhibiting robust stability and adaptability. …”
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MSF-ACA: Low-Light Image Enhancement Network Based on Multi-Scale Feature Fusion and Adaptive Contrast Adjustment by Zhesheng Cheng, Yingdan Wu, Fang Tian, Zaiwen Feng, Yan Li

“…According to the results of the experiment, the parameter count of MSF-ACA is 0.02 M, and compared with today’s mainstream algorithms, the suggested model attains 21.53 dB in PSNR when evaluated on the LOL-v2-real evaluation dataset, and the BRI is as low as 16.04 on the unpaired dataset DICM, which provides a better detail clarity and color fidelity in visual enhancement, and it is a highly efficient and robust low-light image model.…”
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AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes by José E. Valerio, Guillermo de Jesús Aguirre Vera, Maria P. Fernandez Gomez, Jorge Zumaeta, Andrés M. Alvarez-Pinzon

“…Machine learning (ML) algorithms and big data analytics enable precise patient stratification, risk assessment, and outcome prediction, accelerating the development of novel therapeutic approaches. …”
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System Proposal for Supervision of Critical Adverse Processes in Patients with Implanted Ventricular Assist Devices by José R. C. Sousa Sobrinho, Marcosiris A. O. Pessoa, Fabrício Junqueira, Paulo E. Miyagi, Diolino J. Santos Filho

“…Once implanted, the patient becomes dependent on the VAD, making it essential to prevent situations that could harm the patient while receiving circulatory support. VADs are classified as critical systems (CS), and adverse events (AEs) can lead to serious consequences, including hospitalization or even death. …”
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