Multimodal scene recognition using semantic segmentation and deep learning integration by Aysha Naseer, Mohammed Alnusayri, Haifa F. Alhasson, Mohammed Alatiyyah, Dina Abdulaziz AlHammadi, Ahmad Jalal, Jeongmin Park

“…These results demonstrate how the multimodal approach can improve scene detection and classification, with potential uses in fields including robotics, sports analysis, and security systems.…”
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Uncovering abnormal gray and white matter connectivity patterns in Alzheimer’s disease spectrum: a dynamic graph theory analysis for early detection by Juanjuan Jiang, Tao Kang, Ronghua Ling, Yingqian Liu, Jiuai Sun, Yiming Li, Xiaoou Li, Hui Yang, Bingcang Huang, the Alzheimer’s Disease Neuroimaging Initiative

“…Traditional static brain network analyses lack sensitivity to detect early functional disruptions in SMC. This study aimed to improve preclinical AD stratification by integrating dynamic gray-white matter functional connectivity (DFC) and machine learning.MethodsUsing multi-cohort ADNI data [N = 1,415 participants across cognitive normal[CN], SMC, and cognitive impairment [CI]groups],dynamic functional networks were constructed via sliding-window analysis (20–50 TR windows, 98% overlap) of 200 gray matter (Schaefer atlas) and 128 data-driven white matter nodes. …”
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A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction by Saba Taheri Kadkhoda, Babak Amiri

“…The first network reflects similarity across five features, while the second captures correlation in the most critical feature. …”
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Improving Wind Turbine Gearbox Reliability: A Hybrid Deep Learning Approach Using Bispectrum Image Analysis by Muhammad Zacky Asy'Ari, Jun Kondoh, Benyamin Kusumoputro

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Improving Vertebral Fracture Detection in C-Spine CT Images Using Bayesian Probability-Based Ensemble Learning by Abhishek Kumar Pandey, Kedarnath Senapati, Ioannis K. Argyros, G. P. Pateel

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Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets by Saeed Ullah, Junsheng Wu, Zhijun Lin, Mian Muhammad Kamal, Hala Mostafa, Muhammad Sheraz, Teong Chee Chuah

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Machine Learning and Deep Learning Approaches for Fake News Detection: A Systematic Review of Techniques, Challenges, and Advancements by Omar Bashaddadh, Nazlia Omar, Masnizah Mohd, Mohd Nor Akmal Khalid

“…Similarly, the GANM model demonstrated robust performance on the FakeNewsNet dataset by integrating text and social features. Transfer learning and multimodal models that incorporate user behaviour and network information significantly improve detection in diverse, low-resource environments. …”
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Research on Damage Detection of Dual-Rotor Synchronous Excitation Mine Screen Beams Based on Strain Mode Difference Vibration Mode Analysis by Xiaohao Li, Yahui Wang, Yang Zhou

“…Based on the characterization of the vibration response of mine screen frame beams with varying degrees of damage at the same location and with the same degree of damage but at different locations, this paper develops a method of strain modal difference vibration pattern analysis and damage feature extraction for the detection of structural damage in beams. …”
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Cyberbullying Detection, Prevention, and Analysis on Social Media via Trustable LSTM-Autoencoder Networks over Synthetic Data: The TLA-NET Approach by Alfredo Cuzzocrea, Mst Shapna Akter, Hossain Shahriar, Pablo García Bringas

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Advancing Alzheimer’s disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis by Md Nurul Ahad Tawhid, Siuly Siuly, Enamul Kabir, Yan Li

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K-Means Clustering and Classification of Breast Cancer Images Using Histogram of Oriented Gradients Features and Convolutional Neural Network Models: Diagnostic Image Analysis Stud... by Said Salloum

“…ObjectiveThis study aimed to develop an innovative hybrid technique for the classification of breast cancer images involving unsupervised analysis by K-means clustering, feature extraction using Histogram of Oriented Gradients (HOG), and classification of images through a convolutional neural network (CNN). …”
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Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach by Gagandeep Sharma, Tejbir Kaur, Sanjay Kumar Mangal, Amit Kohli

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Mining behavior pattern of mobile malware with convolutional neural network by Xin ZHANG, Weizhong QIANG, Yueming WU, Deqing ZOU, Hai JIN

“…The features extracted by existing malicious Android application detection methods are redundant and too abstract to reflect the behavior patterns of malicious applications in high-level semantics.In order to solve this problem,an interpretable detection method was proposed.Suspicious system call combinations clustering by social network analysis was converted to a single channel image.Convolution neural network was applied to classify Android application.The model trained was used to find the most suspicious system call combinations by convolution layer gradient weight classification activation mapping algorithm,thus mining and understanding malicious application behavior.The experimental results show that the method can correctly discover the behavior patterns of malicious applications on the basis of efficient detection.…”
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Secure Biometric Identification Using Orca Predators Algorithm With Deep Learning: Retinal Iris Image Analysis by Louai A. Maghrabi, Mohammed Altwijri, Sami Saeed Binyamin, Fouad Shoie Alallah, Diaa Hamed, Mahmoud Ragab

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DETECTION OF NON-MELANOMA SKIN CANCER BY DEEP CONVOLUTIONAL NEURAL NETWORK AND STOCHASTIC GRADIENT DESCENT OPTIMIZATION ALGORITHM by Premananda Sahu, Srikanta Kumar Mohapatra, Prakash Kumar Sarang, Jayashree Mohanty, Pradeepta Kumar Sarangi

“…Furthermore, we used HAM 10000 as the data set for training and testing purposes, as well as the feature extraction technique Principal Component Analysis. …”
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Diagnostic accuracy of MRI-based radiomic features for EGFR mutation status in non-small cell lung cancer patients with brain metastases: a meta-analysis by Yuqin Long, Rong Zhao, Xianfeng Du

“…The AUC for the receiver operating characteristic analysis was 0.91 (95% CI: 0.88-0.93). Subgroup analysis indicated that deep learning models and studies conducted in Asian showed higher diagnostic accuracy compared to their respective counterparts.ConclusionsMRI-based radiomic features demonstrate a high potential for accurately detecting EGFR mutations in NSCLC patients with brain metastases, particularly when advanced deep learning techniques were employed. …”
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Clinical features and genetic analysis of a family with t(5;9) (p15;p24) balanced translocation leading to Cri-du-chat syndrome in offspring by Jing Zhao, Ping Chen, Yijia Ren, Shurong Li, Weiyi Zhang, Yan Li, Fengyu Wang

“…We characterized individual clinical features and conducted a genetic analysis of the members of a family with t (5; 9) (p15; p24) balanced translocation leading to Cri-du-chat syndrome in the offspring.Study designWe performed a chromosomal karyotyping with high-resolution G-banding on the proband and her family members to detect their chromosomal configurations. …”
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Key frame extraction based abnormal vehicle identification technique using statistical distribution analysis by M. A. Y. Peer Mohamed Appa, V. Vanitha, Priti Rishi, Shrddha Sagar, M. Asha Paul

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Detection of Phosphorus in Water by Laser-Induced Breakdown Spectroscopy Based on Liquid-Solid Transformation of Graphite Substrate Combined with PLS-SVR Fusion Quantitative Analys... by Huijie Zhang, Yao Chen, Zongjie Bi, Xiaohua Che, Zhaoshuo Tian

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OCRClassifier: integrating statistical control chart into machine learning framework for better detecting open chromatin regions by Xin Lai, Xin Lai, Min Liu, Yuqian Liu, Xiaoyan Zhu, Jiayin Wang, Jiayin Wang

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