Deep learning-based detection and classification of acute lymphoblastic leukemia with explainable AI techniques by Debendra Muduli, Sourav Parija, Suhani Kumari, Asmaul Hassan, Harendra S. Jangwan, Abu Taha Zamani, Sk. Mohammed Gouse, Banshidhar Majhi, Nikhat Parveen

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Weld Pool Boundary Detection Based on the U-Net Algorithm and Weld Seam Tracking in Plasma Arc Welding by Jidong Lu, Satoshi Yamane, Weixi Wang, Ning Li, Siqi Wang, Yuxiong Xia

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Real-Time Runway Detection Using Dual-Modal Fusion of Visible and Infrared Data by Lichun Yang, Jianghao Wu, Hongguang Li, Chunlei Liu, Shize Wei

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Privacy-Aware Detection for Large Language Models Using a Hybrid BiLSTM-HMM Approach by Maryam Abbasalizadeh, Sashank Narain

“…Utilizing the Forward algorithm, our system quantifies privacy risks, enabling users to revise inputs prior to submission and thereby enhancing data privacy. …”
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Detection of Fake News Using Deep Learning and Machine Learning by Gabriela CHIRIAC, Ada Maria CATINA

“…Automatically identifying fake news is a complex challenge requiring detailed understanding of misinformation propagation and advanced data processing. Machine Learning and Deep Learning algorithms for detection demand continuous adaptation as disinformation tactics evolve. …”
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Leveraging assistive technology for visually impaired people through optimal deep transfer learning based object detection model by Mahir Mohammed Sharif Adam, Nojood O. Aljehane, Mohammed Yahya Alzahrani, Samah Al Zanin

“…In recent times, deep learning (DL) techniques have become a powerful approach for extracting feature representations from data, leading to significant advancements in the field of object detection. …”
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Supervised Learning-Based Fault Classification in Industrial Rotating Equipment Using Multi-Sensor Data by Aziz Kubilay Ovacıklı, Mert Yagcioglu, Sevgi Demircioglu, Tugberk Kocatekin, Sibel Birtane

“…This study employs supervised machine learning algorithms to apply multi-label classification for fault detection in rotating machinery, utilizing a real dataset from multi-sensor systems installed on a suction fan in a typical manufacturing industry. …”
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Hybrid Machine Learning-Based Fault-Tolerant Sensor Data Fusion and Anomaly Detection for Fire Risk Mitigation in IIoT Environment by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Shashi Bhushan, Vinay Rishiwal, Manish Kumar

“…The proposed approach also deploys machine learning algorithms to dynamically adjust probabilistic models based on real-time sensor reliability, thereby improving prediction accuracy even in the presence of unreliable sensor data. …”
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DualPFL: A Dual Sparse Pruning Method with Efficient Federated Learning for Edge-Based Object Detection by Shijin Song, Sen Du, Yuefeng Song, Yongxin Zhu

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Explainable Siamese Neural Networks for Detection of High Fall Risk Older Adults in the Community Based on Gait Analysis by Christos Kokkotis, Kyriakos Apostolidis, Dimitrios Menychtas, Ioannis Kansizoglou, Evangeli Karampina, Maria Karageorgopoulou, Athanasios Gkrekidis, Serafeim Moustakidis, Evangelos Karakasis, Erasmia Giannakou, Maria Michalopoulou, Georgios Ch Sirakoulis, Nikolaos Aggelousis

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An intelligent algorithm for identifying dropped blocks in wellbores by Qian Wang, Zixuan Yang, Chenxi Ye, Wenbao Zhai, Xiao Feng

“…An optimal machine learning algorithm was developed by training it with 10 machine learning algorithms and the block data collected in the field. …”
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Intrusion Detection Based on Sequential Information Preserving Log Embedding Methods and Anomaly Detection Algorithms by Czangyeob Kim, Myeongjun Jang, Seungwan Seo, Kyeongchan Park, Pilsung Kang

“…Contrary to other machine learning based system anomaly detection models, which borrow domain experts’ knowledge to extract significant features from the log data, raw log data are transformed into a fixed size of continuous vector regardless of their length, and these vectors are used to train the anomaly detection models. …”
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Design of an Efficient Model for Psychological Disease Analysis and Prediction Using Machine Learning and Genomic Data Samples by Alparthi Kumuda, Saroj Kumar Panigrahy

“…Therefore, this study developed the Psychological Disorders Machine Learning Genomic (PDMLG) model as an amalgamation of genetic algorithms and machine learning techniques in a predictive analysis model using genomic data samples. …”
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Federated Learning Framework Based on Distributed Storage and Diffusion Model for Intrusion Detection on IoT Networks by Ricardo Manzano, Marzia Zaman, Darshana Upadhyay, Nishith Goel, Srinivas Sampalli

“…The integration of Internet of Things (IoT) devices into smart environments has become increasingly prevalent, resulting in the collection of valuable user and service data. However, effectively utilizing this data often requires its aggregation on a central server to train algorithms capable of identifying and preventing malicious attacks, such as reconnaissance, DoS (Denial of service), DDoS (Distributed denial of service) within IoT networks. …”
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State-of-the-Art Fault Detection and Diagnosis in Power Transformers: A Review of Machine Learning and Hybrid Methods by Lebo Dina Mashifane, Bongumsa Mendu, Bessie Baakanyang Monchusi

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Addressing Data Scarcity in Crack Detection via CrackModel: A Novel Dataset Synthesis Approach by Jian Ma, Yuan Meng, Weidong Yan, Guoqi Liu, Xueyan Guo

“…This model is capable of extracting and storing crack information from hundreds of images of wooden structures with cracks and synthesizing the data with images of intact structures to generate high-fidelity data for training detection algorithms. …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…This study proposes a hybrid AI-driven framework that integrates structured (e.g., patient demographics, lab results) and unstructured data (e.g., clinical notes) to detect ADRs using advanced deep learning and NLP methods. …”
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Deep Learning for Weed Detection and Segmentation in Agricultural Crops Using Images Captured by an Unmanned Aerial Vehicle by Josef Augusto Oberdan Souza Silva, Vilson Soares de Siqueira, Marcio Mesquita, Luís Sérgio Rodrigues Vale, Thiago do Nascimento Borges Marques, Jhon Lennon Bezerra da Silva, Marcos Vinícius da Silva, Lorena Nunes Lacerda, José Francisco de Oliveira-Júnior, João Luís Mendes Pedroso de Lima, Henrique Fonseca Elias de Oliveira

“…The YOLOv8s variant achieved higher performance with an mAP50 of 97%, precision of 99.7%, and recall of 99% when compared to the other models. The data from this manuscript show that deep learning models can generate efficient results for automatic weed detection when trained with a well-labeled and large set. …”
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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities by Shoaib Hassan, Qianmu Li, Khursheed Aurangzeb, Affan Yasin, Javed Ali Khan, Muhammad Shahid Anwar

“…Abstract Over the past few years, the application and usage of Machine Learning (ML) techniques have increased exponentially due to continuously increasing the size of data and computing capacity. …”
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Fish Detection Using Deep Learning by Suxia Cui, Yu Zhou, Yonghui Wang, Lujun Zhai

“…The processing procedure can mimic human being’s learning routines. An advanced system with more computing power can facilitate deep learning feature, which exploit many neural network algorithms to simulate human brains. …”
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