A multi-factor integration-based semi-supervised learning for address resolution protocol attack detection in SDIIoT by Zhong Li, Huimin Zhuang

“…Recently, the idea of applying the software-defined networking paradigm to industrial Internet of things is proposed by many scholars since this paradigm has the advantages of flexible deployment of intelligent algorithms and global coordination capabilities. These advantages prompt us to propose a multi-factor integration-based semi-supervised learning address resolution protocol detection method deployed in software-defined networking, called MIS, to specially solve the problems of limited labeled training data and incomplete features extraction in the traditional address resolution protocol detection methods. …”
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Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review by Olga Adriana Caliman Sturdza, Florin Filip, Monica Terteliu Baitan, Mihai Dimian

“…Recent developments in COVID-19 diagnosis involve constructing expansive multi-noise information sets while creating clinical process-oriented AI algorithms and implementing distributed learning protocols for securing information security and system stability. …”
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Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review by Avneet Kaur, Gurjit S. Randhawa, Farhat Abbas, Mumtaz Ali, Travis J. Esau, Aitazaz A. Farooque, Rajandeep Singh

“…However, AI tools, for instance, Machine Learning (ML) and Deep Learning (DL), offer precise and well-timed solutions for disease detection, classification, and eradication. …”
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Real-Time Computing Strategies for Automatic Detection of EEG Seizures in ICU by Laura López-Viñas, Jose L. Ayala, Francisco Javier Pardo Moreno

“…We applied signal preprocessing and conducted a numerical quantitative analysis in the frequency domain. Various machine learning algorithms were assessed for their efficacy. …”
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A review of deep-learning-based models for afaan oromo fake news detection on social media networks by Kedir Lemma Arega, Kula Kekeba Tune, Asrat Mulatu Beyene, Wegderes Tariku, Nurhussen Menza Bune

“…It compares machine learning and deep learning algorithms, proposes future studies using picture data, and encourages more effective methodologies. …”
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Long-Range Wide Area Network Intrusion Detection at the Edge by Gonçalo Esteves, Filipe Fidalgo, Nuno Cruz, José Simão

“…This paper proposes the implementation of machine learning algorithms, specifically the K-Nearest Neighbours (KNN) algorithm, within an Intrusion Detection System (IDS) for LoRaWAN networks. …”
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A machine learning-based efficient anomaly detection system for enhanced security in compromised and maligned IoT Networks by Anita Punia, Manish Tiwari, Sourabh Singh Verma

“…Traditional machine learning approaches cannot detect these threats because IoT data is complex. …”
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Structural Damage Detection in the Wooden Bridge Using the Fourier Decomposition, Time Series Modeling and Machine Learning Methods by Younes Nouri, Farzad Shahabian, Hashem Shariatmadar, Alireza Entezami

“…The residuals of the time series model of both undamaged and damaged structures are extracted for detecting any damage. To ascertain the presence of damage, supervised classification machine learning algorithms are employed. …”
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Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning by Yingbin Wang, Yulong Huang, Xiaobao Liu, Chishan Kang, Wenjie Wu

“…To enable rapid analysis of complex SERS data, three ML algorithms—linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), and random forest (RF)—were employed. …”
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Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents by Yongxiang Zhang, Raymond Y. K. Lau

“…Chatbots, an automatic dialogue system empowered by deep learning-oriented AI technology, have gained increasing attention in healthcare e-services for their ability to provide medical information around the clock. …”
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A lightweight multi-deep learning framework for accurate diabetic retinopathy detection and multi-level severity identification by Amad Zafar, Kwang Su Kim, Muhammad Umair Ali, Jong Hyuk Byun, Jong Hyuk Byun, Seong-Han Kim

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Effective DDoS attack detection in software-defined vehicular networks using statistical flow analysis and machine learning. by Himanshi Babbar, Shalli Rani, Maha Driss

“…The proposed methodology aims to: (i) analyze statistical flow and compute entropy, and (ii) implement Machine Learning (ML) algorithms within SDN Intrusion Detection Systems for Internet of Things (IoT) environments. …”
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Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network by Aythem Khairi Kareem, Mohammed M. AL-Ani, Ahmed Adil Nafea

“… Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. …”
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LieRHRV system for remote lie detection using heart rate variability parameters by Moran Davoodi, Nitay Aspis, Yael Drori, Ido Weiser-Bitoun, Yael Yaniv

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Federated Deep Learning for Scalable and Privacy-Preserving Distributed Denial-of-Service Attack Detection in Internet of Things Networks by Abdulrahman A. Alshdadi, Abdulwahab Ali Almazroi, Nasir Ayub, Miltiadis D. Lytras, Eesa Alsolami, Faisal S. Alsubaei, Riad Alharbey

“…We need scalable, privacy-preserving, and resource-efficient IoT intrusion detection algorithms to solve this essential problem. …”
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Multi-Comparison of Different Ocular Imaging Modality-based Deep Learning Models for Visually Significant Cataract Detection by Jocelyn Hui Lin Goh, BEng, Xiaofeng Lei, MSc, Miao-Li Chee, MPH, Yiming Qian, PhD, Marco Yu, PhD, Tyler Hyungtaek Rim, MD, PhD, Simon Nusinovici, PhD, David Ziyou Chen, MBBS, FRCOphth, Kai Hui Koh, BSc, Samantha Min Er Yew, BSc, Yibing Chen, BEng, Victor Teck Chang Koh, MBBS, MMed, Charumathi Sabanayagam, MD, PhD, Tien Yin Wong, MD, PhD, Xinxing Xu, PhD, Rick Siow Mong Goh, PhD, Yong Liu, PhD, Ching-Yu Cheng, MD, PhD, Yih-Chung Tham, PhD

“…Researchers have utilized various imaging modalities, including slit beam, diffuse anterior segment, and retinal imaging, to develop deep learning (DL) algorithms for automated cataract analysis. …”
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Robust Semantic Segmentation of Wafer Transmission Electron Microscopy Image Using Multi-Task Learning With Edge Detection by Yongwon Jo, Jinsoo Bae, Hansam Cho, Sungsu Kim, Heejoong Roh, Kyunghye Kim, Munki Jo, Munuk Kim, Jaeung Tae, Seoung Bum Kim

“…While existing methods for automated measurement have used semantic segmentation algorithms, they often lead to inaccurate object boundary detection, resulting in over- or under-estimation. …”
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Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets by Catarina Lopes, Andreia Brandão, Manuel R. Teixeira, Mário Dinis-Ribeiro, Carina Pereira

“…Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. …”
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Federated learning based intelligent edge computing technique for video surveillance by Yu ZHAO, Jie YANG, Miao LIU, Jinlong SUN, Guan GUI

“…With the explosion of global data,centralized cloud computing cannot provide low-latency,high-efficiency video surveillance services.A distributed edge computing model was proposed,which directly processed video data at the edge node to reduce the transmission pressure of the network,eased the computational burden of the central cloud server,and reduced the processing delay of the video surveillance system.Combined with the federated learning algorithm,a lightweight neural network was used,which trained in different scenarios and deployed on edge devices with limited computing power.Experimental results show that,compared with the general neural network model,the detection accuracy of the proposed method is improved by 18%,and the model training time is reduced.…”
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Novel machine learning paradigms-enabled methods for smart building operations in data-challenging contexts: Progress and perspectives by Fan Cheng, Lei Yutian, Mo Jinhan, Wang Huilong, Wu Qiuting, Cai Jiena

“…Building operational data typically suffer from data quality problems, such as insufficient labeled and imbalanced data, making them incompatible with conventional machine learning algorithms. …”
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