MXT-YOLOv7t: An Efficient Real-Time Object Detection for Autonomous Driving in Mixed Traffic Environments by Afdhal Afdhal, Khairun Saddami, Mirshal Arief, Sugiarto Sugiarto, Zahrul Fuadi, Nasaruddin Nasaruddin

“…The enhancements include refining the feature extraction network by integrating a lightweight attention mechanism into the ELAN blocks and replacing the activation function in each convolution layer with a sigmoid-weighted linear unit. …”
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NeXtMD: a new generation of machine learning and deep learning stacked hybrid framework for accurate identification of anti-inflammatory peptides by Chengzhi Xie, Yijie Wei, Xinwei Luo, Huan Yang, Hongyan Lai, Fuying Dao, Juan Feng, Hao Lv

“…The first stage generates preliminary predictions using four distinct encoding strategies and ML classifiers, while the second stage employs a multi-branch residual network (ResNeXt) to refine prediction outputs. Benchmark evaluations demonstrate that NeXtMD outperforms current state-of-the-art methods on multiple performance metrics. …”
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An AI explained systematic modular approach for enhanced Electricity Theft Detection for urbanized Smart Grid environment by Muhammad Ammar, Nadeem Javaid, Ali Arishi

“…Finally, the proposed SATBlend in the classification module utilizes AlexNet for feature extraction, ShuffleNet for efficient computation, and a temporal convolutional network for temporal correlation detection to enhance the reliability of advanced ETD systems. …”
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Estimation of Leaf, Spike, Stem and Total Biomass of Winter Wheat Under Water-Deficit Conditions Using UAV Multimodal Data and Machine Learning by Jinhang Liu, Wenying Zhang, Yongfeng Wu, Juncheng Ma, Yulin Zhang, Binhui Liu

“…Correlation analysis and Variance Inflation Factor (VIF) were employed for feature selection, and estimation models for leaf, spike, stem, and total AGB were constructed using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN) models. …”
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Identification of biomarkers for chronic renal fibrosis and their relationship with immune infiltration and cell death by Jiaqi Shi, Xinyue Qin, Haonan Sha, Rong Wang, Hao Shen, Yinhao Chen, Xiaolan Chen

“…Seven genes identified through Protein-Protein Interaction (PPI) network and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) analysis were validated using qRT-PCR in both in vivo and in vitro UUO experiments. …”
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Mask-UnMask Regions (MUMR) Framework for Classifying AMD Grades Using Inter-Regional Interaction Analysis by Ibrahim Abdelhalim, Namuunaa Nadmid, Mohamed Elsharkawy, Mohammed Ghazal, Ali H. Mahmoud, Ayman El-Baz

“…Additionally, we developed a simple, lightweight, yet efficient ResNet-like network for feature extraction and introduced a Region Interaction (RI) module. …”
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Navigating cognitive boundaries: the impact of CognifyNet AI-powered educational analytics on student improvement by Mrim M. Alnfiai, Faiz Abdullah Alotaibi, Mona Mohammed Alnahari, Nouf Abdullah Alsudairy, Asma Ibrahim Alharbi, Saad Alzahrani

“…While traditional machine learning approaches such as random forest and neural networks have been applied to educational data, they typically present trade-offs between interpretability and predictive capability, failing to capture student learning processes’ complex, multidimensional nature. …”
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Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database by Shengwei Lin, Wenbin Lu, Ting Wang, Ying Wang, Xueqian Leng, Lidan Chi, Peipei Jin, Jinjun Bian

“…Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). …”
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Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…Lipidomic data underwent univariate and multivariate analyses, including fold change (FC), <i>t</i>-tests, PLS-DA, and Elastic Network feature selection, to identify significant biomarker candidate lipids. …”
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XSShield: A novel dataset and lightweight hybrid deep learning model for XSS attack detection by Gia-Huy Luu, Minh-Khang Duong, Trong-Phuc Pham-Ngo, Thanh-Sang Ngo, Dat-Thinh Nguyen, Xuan-Ha Nguyen, Kim-Hung Le

“…Furthermore, we present a hybrid detection model that leverages the strengths of both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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Energy-Efficient Secure Cell-Free Massive MIMO for Internet of Things: A Hybrid CNN–LSTM-Based Deep-Learning Approach by Ali Vaziri, Pardis Sadatian Moghaddam, Mehrdad Shoeibi, Masoud Kaveh

“…However, the increasing demand for high-performance wireless networks necessitates advanced frameworks that optimize both energy efficiency (EE) and security. …”
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Improved whole-brain reconfiguration efficiency reveals mechanisms of speech rehabilitation in cleft lip and palate patients: an fMRI study by Wenjing Zhang, Qian Si, Zhongtian Guan, Lei Cao, Mengyue Wang, Cui Zhao, Liwei Sun, Xu Zhang, Zhixi Zhang, Chunlin Li, Weiqun Song

“…Recent studies suggest that the brain’s reconfiguration efficiency appears to be a key feature of its network dynamics and general cognitive abilities. …”
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YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles by Shimin Weng, Han Wang, Jiashu Wang, Changming Xu, Ende Zhang

“…Secondly, within the neck network, multi-scale feature extraction is facilitated through the design of novel composite convolutions, ConvX and MConv, based on a “split–differentiate–concatenate” paradigm. …”
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Employing machine learning for early detection of poly-victimization in rural children: a survey study in China’s Chaoshan region by Zicheng Cao, Yandong Luo, Ping Chang, Jiajun Zhou, Liping Li

“…Four machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Random Forest (RF), were employed to predict PV risk, and SHAP feature importance was utilized to evaluate risk factors. …”
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Enhancing EEG-Based Emotion Detection with Hybrid Models: Insights from DEAP Dataset Applications by Badr Mouazen, Ayoub Benali, Nouh Taha Chebchoub, El Hassan Abdelwahed, Giovanni De Marco

“…This study leverages the DEAP dataset to explore and evaluate various machine learning and deep learning techniques for emotion recognition, aiming to address these challenges. …”
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Smart Grid System Based on Blockchain Technology for Enhancing Trust and Preventing Counterfeiting Issues by Ala’a Shamaseen, Mohammad Qatawneh, Basima Elshqeirat

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Event-based landslide inventory through very high-resolution optical images and field surveys by P. Confuorto, R. Franceschini, L. Scarpitta, N. Casagli, S. Morelli, F. Raspini, V. Tofani, S. Moretti

“…A further objective is to evaluate the impact of these landslides on the built environment, particularly roads and buildings. …”
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SkelETT&#x2014;Skeleton-to-Emotion Transfer Transformer by Pedro Victor Vieira Paiva, Josue Junior Guimaraes Ramos, Marina Gavrilova, Marco Antonio Garcia de Carvalho

“…Comprising a series of encoder layers, SkelETT patches 2D body pose representations, it also includes multi-head self-attention mechanisms and position-wise feed-forward networks, providing a powerful framework for extracting hierarchical features from sequential body pose data. …”
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MuRelSGG: Multimodal Relationship Prediction for Neurosymbolic Scene Graph Generation by Muhammad Junaid Khan, Adil Masood Siddiqui, Hamid Saeed Khan, Faisal Akram, M. Jaleed Khan

“…Neurosymbolic Scene Graph Generation (SGG) is a promising approach that jointly leverages the perception capabilities of deep neural networks and the reasoning capabilities of symbolic techniques for scene understanding and visual reasoning. …”
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Exploring the Potential of Wi-Fi in Industrial Environments: A Comparative Performance Analysis of IEEE 802.11 Standards by Luis M. Bartolín-Arnau, Federico Orozco-Santos, Víctor Sempere-Payá, Javier Silvestre-Blanes, Teresa Albero-Albero, David Llacer-Garcia

“…The advent of Industry 4.0 brought about digitalisation and the integration of advanced technologies into industrial processes, with wireless networks emerging as a key enabler in the interconnection of smart devices, cyber–physical systems, and data analytics platforms. …”
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