Network and Dataset for Multiscale Remote Sensing Image Change Detection by Shenbo Liu, Dongxue Zhao, Yuheng Zhou, Ying Tan, Huang He, Zhao Zhang, Lijun Tang

“…This dataset consists of 842 pairs of images with a resolution of 1024 × 1024 pixels, featuring uniformly distributed change detection target sizes. Comparative experiments are conducted with 10 other state-of-the-art algorithms on the MSRS-CD dataset and another public dataset, LEVIR-CD. …”
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AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases by Hajar Nasir Tukur, Olivier Uwishema, Hatice Akbay, Dalal Sheikhah, Inês Filipa Silva Correia

“…These findings highlight AI’s potential to detect preclinical disease stages before significant neurological symptoms manifest. …”
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Coral reef detection using ICESat-2 and machine learning by Gabrielle A. Trudeau, Kim Lowell, Jennifer A. Dijkstra

“…Future work should refine algorithms and incorporate additional environmental variables to improve model performance across various reef types.…”
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MODEL BASED FAULT DETECTION FOR SAFETY MANAGEMENT OF THE INDUSTRIAL PROCESSES by Dumitru POPESCU, Pierre BORNE, Crina TOROUS

“…The paper presents a efficient detection solution concepts based on the dependency and the causal connections that exist between the process parameters and proposes techniques and algorithms for the safe evolution of the process. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Both algorithms demonstrated comparable performance across other modes. …”
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Detection of selfish nodes based on credit mechanism in opportunistic networks by Zuo-yong TANG, Yi-jia YUAN, Yong-qiang DONG, Guo-xin WU

“…The existence of selfish nodes seriously affects the routing performance of opportunistic networks(OppNet).To protect the OppNet against the nodes’ selfish behavior,a credit-based selfish nodes detection mechanism was proposed to make it possible to keep away from such nodes during the process of message forwarding.The mechanism leverages 2-ACK messages to observe the nodes’behavior.Then the credit value was calculated based on the observation information and accordingly acts as the metric to distinguish the selfish nodes.Simulation results show that,when coupled with various routing algorithms,the mechanism could detect selfish nodes out accurately,and improve network performance effectively in terms of delivery rate and traffic load.…”
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Detection of Fake Instagram Accounts via Machine Learning Techniques by Stefanos Chelas, George Routis, Ioanna Roussaki

“…After making the necessary feature additions to and removals from these data, they are fed into machine learning algorithms with the aim of detecting fake Instagram accounts. …”
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Android malware detection method based on deep neural network by Fan CHAO, Zhi YANG, Xuehui DU, Yan SUN

“…Android is increasingly facing the threat of malware attacks.It is difficult to effectively detect large-sample and multi-class malware for traditional machine learning methods such as support vector machine,method for Android malware detection and family classification based on deep neural network was proposed.Based on the comprehensive extraction of application components,Intent Filter,permissions,and data flow,the method performed an effective feature selection to reduce dimensions,and conducted a large-sample detection and multi-class classification for malware based on deep neural network.The experimental results show that the method can conduct an effective detection and classification.The accuracy of binary classification between benign and malicious Apps is 97.73%,and the accuracy of family multi-class classification can reach 93.54%,which is higher than other machine learning algorithms.…”
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Optical fiber eavesdropping detection method based on machine learning by Xiaolian CHEN, Yi QIN, Jie ZHANG, Yajie LI, Haokun SONG, Huibin ZHANG

“…Optical fiber eavesdropping is one of the major hidden dangers of power grid information security,but detection is difficult due to its high concealment.Aiming at the eavesdropping problems faced by communication networks,an optical fiber eavesdropping detection method based on machine learning was proposed.Firstly,seven-dimensions feature vector extraction method was designed based on the influence of eavesdropping on the physical layer of transmission.Then eavesdropping was simulated and experimental feature vectors were collected.Finally,two machine learning algorithms were used for classification detection and model optimization.Experiments show that the performance of the neural network classification is better than the K-nearest neighbor classification,and it can achieve 98.1% eavesdropping recognition rate in 10% splitting ratio eavesdropping.…”
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Using A One-Class SVM To Optimize Transit Detection by Jakob Roche

“…As machine learning algorithms become increasingly accessible, a growing number of organizations and researchers are using these technologies to automate the process of exoplanet detection. …”
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Applying Extensions of Evidence Theory to Detect Frauds in Financial Infrastructures by Luigi Coppolino, Salvatore D'Antonio, Valerio Formicola, Luigi Romano

“…In this work, we present a Fraud Detection System that classifies transactions in a Mobile Money Transfer infrastructure by using the data fusion algorithms derived from these new models. …”
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Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis by Raut Komal, Balpande Vijaya

“…This paper proposed several machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression and Support Vector Machine and design an ensemble of these models to detect and classify Parkinson's disease. …”
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Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence by Marco Zeppieri, Lorenzo Gardini, Carola Culiersi, Luigi Fontana, Mutali Musa, Fabiana D’Esposito, Pier Luigi Surico, Caterina Gagliano, Francesco Saverio Sorrentino

“…Through the fast and accurate analysis of massive amounts of imaging data, artificial intelligence (AI), in particular machine learning (ML) and deep learning (DL), has emerged as a promising method to improve the early detection and management of glaucoma. Aims: The purpose of this study is to examine the current uses of AI in the early diagnosis, treatment, and detection of glaucoma while highlighting the advantages and drawbacks of different AI models and algorithms. …”
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Traveling Wave and Wavelet Based Fault Location Detection in Microgrids by Muhammed Oğuz Korkmaz, Ahmet Küçüker

“…In order to allow the use of traveling wave-basedprotection in loop distribution systems, the system is separated into three zones.To each three separate zone a type D fault locator method was applied, and byensuring that these algorithms worked in accordance, fault location detection wascarried out in a looped microgrid.…”
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Predicting the generalization of computer aided detection (CADe) models for colonoscopy by Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg

“…Abstract Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. …”
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Siamese comparative transformer-based network for unsupervised landmark detection. by Can Zhao, Tao Wu, Jianlin Zhang, Zhiyong Xu, Meihui Li, Dongxu Liu

“…Current landmark detection algorithms often train a sophisticated image pose encoder by reconstructing the source image to identify landmarks. …”
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Exploring the Role of Artificial Intelligence in Detecting Advanced Persistent Threats by Pedro Ramos Brandao

“…This paper explores the pivotal role of Artificial Intelligence (AI) in enhancing the detection and mitigation of APTs. By leveraging machine learning algorithms and data analytics, AI systems can identify patterns and anomalies that are indicative of sophisticated cyber-attacks. …”
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Business process mining based insider threat detection system by Tai-ming ZHU, Yuan-bo GUO, An-kang JU, Jun MA

“…Current intrusion detection systems are mostly for detecting external attacks,but sometimes the internal staff may bring greater harm to organizations in information security.Traditional insider threat detection methods of-ten do not combine the behavior of people with business activities,making the threat detection rate to be improved.An insider threat detection system based on business process mining from two aspects was proposed,the implementation of insider threats and the impact of threats on system services.Firstly,the normal control flow model of business ac-tivities and the normal behavior profile of each operator were established by mining the training log.Then,the actual behavior of the operators was compared with the pre-established normal behavior contours during the operation of the system,which was supplemented by control flow anomaly detection and performance anomaly detection of business processes,in order to discover insider threats.A variety of anomalies were defined and the corresponding detection algorithms were given.Experiments were performed on the ProM platform.The results show the designed system is effective.…”
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Estimating the influence of accounting variables change on earnings management detection by Igor Pustylnick, Oksana Temchenko, Sergey Gubarkov

“…Neither of these two detection algorithms attempts to quantify earnings management and connect it with the infractions committed by the companies, charged by the regulator (in this case – U.S. …”
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Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods by Dorothée Coppieters ’t Wallant, Pierre Maquet, Christophe Phillips

“…To analyze spindle activity in a more robust way, automatic sleep spindle detection methods are essential. Various algorithms were developed, depending on individual research interest, which hampers direct comparisons and meta-analyses. …”
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