A systematic review of ulcer detection methods in wireless capsule endoscopy by Ahmmad Musha, Rehnuma Hasnat, Abdullah Al Mamun, Md Sohag Hossain, Md Jakir Hossen, Tonmoy Ghosh

“…However, manually reviewing images captured by WCE is a tedious and time-consuming process. Implementing a computer-aided ulcer detection system can facilitate the automatic evaluation of these images. …”
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Streamlining Deep Learning Network for Real-time Sea Turtle Detection by Muhamad Dwisnanto Putro, Yuliana Mose, Alex Copernikus Andaria, Jane Litouw, Vecky Canisius Poekoel, Xaverius Najoan

“…Monitoring turtle behavior is a conservation effort to preserve its habitat, and the detection process is a vital initial stage. On the other hand, robotics demands a deep learning network to automatically detect the presence of sea turtles that can operate in real-time. …”
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Detection and diagnosis of air compressor faults using weightless neural networks by Anubhab Bhattacharyya, Naveen Venkatesh Sridharan, Aravinth Sivakumar, Sugumaran Vaithiyanathan

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Selection and Application of Specific Nucleic Acid Aptamers for the Detection of Pseudomonas aeruginosa by LI Xu, CHENG Yangyang, YANG Kai, LIU Benkang, LI Cheng

“…Under optimized concentration of Apt13 and incubation time of P. aeruginosa, the fluorescence quenching intensity exhibited a linear relationship with the concentration of P. aeruginosa ranging from 101 to 108 CFU/mL. The limit of detection (LOD) was 2 CFU/mL, and the entire detection process took less than 2.0 h. …”
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Radiomics-based machine learning for automated detection of Pneumothorax in CT scans. by Hanieh Alimiri Dehbaghi, Karim Khoshgard, Hamid Sharini, Samira Jafari Khairabadi, Farhad Naleini

“…This study addresses the pressing need for improved diagnostic accuracy in CT scans by developing an intelligent model that leverages radiomics features and machine learning techniques. By enhancing the detection of pneumothorax, this research aims to mitigate diagnostic errors and accelerate the process of image interpretation, ultimately improving patient outcomes. …”
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Visual Positioning Detection of EMU Brake Pad Based on Deep Learning by GUAN Chunling, XU Yingjie, SONG Yuechao

“…Through the bilinear interpolation method, the misalignment errors of target features during ROI (region of interest) pooling quantization process are reduced. The proposed brake pad detection method achieves an average precision of 98.42% and an FPS (frames per second) of 27.77%.…”
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Performance Evaluation of a Visual Defects Detection System for Railways Monitoring by Radosavljevic Saša, Rivero Alain, Rodríguez Flórez Sergio, El Ouardi Abdelhafid, Michel Pauline, Bouamama Belkacem O., Vanheeghe Philippe

“…This study addresses visual defects detection that can be integrated in a multi-modal monitoring system. …”
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Modified QuEChERS for antiepileptic drugs detection in forensic toxicology by Ahmad Shekari, Jalal Hassan, Mohammad Kazem Koohi, Masoud Ghadipasha, Maryam Akhgari

“…One influencing parameter on the extraction process was the pH of sample. Extraction recoveries were in the range of 42–97 % for all analytes. …”
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An intelligent spam detection framework using fusion of spammer behavior and linguistic. by Amna Iqbal, Muhammad Younas, Muhammad Kashif Hanif, Muhammad Murad, Rabia Saleem, Muhammad Aater Javed

“…The proposed spam detection framework SD-FSL-CLSTM used the fusion of spammer behavior features and linguistic features which automatically detect and classify the spam reviews. …”
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Self-supervised change detection of heterogeneous images based on difference algorithms by Jinsha Wu, Shuwen Yang, Yikun Li, Yukai Fu, Zhuang Shi, Yao Zheng

“…Secondly, the hierarchical FCM clustering algorithm is improved to extract stable and correct self-supervised samples by difference images so that the clustering process is not overly dependent on thresholds. Then, the support vector machine classifier is trained based on the heterogeneous images, the fused images, and self-supervised sample sets, and the information from the fused images is utilized to increase the feature dimension for better detection of changes. …”
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Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information by Chi Zhang, Jin-Woo Jung

“…Graph anomaly detection aims at identifying rare, unusual entities in attributed networks with respect to their patterns or structures that deviate significantly from the majority within a graph. …”
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Developing an Optimize Deep Learning Framework for Brain Tumors Detection and Classification by Ghosh Sayani, Mazumder Souvik, Kundu Prasenjit, Dey Arpita

“…But, due to the asymptomatic nature and location of brain tumor, early detection through tissue diagnosis is difficult. This research design an empirical deep learningbased framework to detect and classify brain tumors from Magnetic Resonance Imaging (MRI) images datasets. …”
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Detection of Herpesviruses (Predominantly HHV-6) in Patients with Guillain–Barré Syndrome by Jéssica Gonçalves Pereira, Tainá Madeira Barros Pontes, Fernanda Martins Maia Carvalho, André Borges Ferreira Gomes, Rafael Santos Erbisti, Ivanildo Pedro de Sousa Junior, Jeová Keny Baima Colares, Danielle Malta Lima, Vanessa Salete de Paula

“…HHV-6 was the most frequent subtype detected in patients positive for herpesviruses (62.5%, 5/8). …”
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FEXGBIDS: Federated XGBoost-Based Intrusion Detection System for In-Vehicle Network by Jie Li, Yuanyuan Song, Ming-Gang Zheng, Shuo Zhang, Han Liang

“…Additionally, we employ BLS aggregate signatures to achieve efficient and verifiable parameter aggregation, and leverage the DDSketch distributed quantile estimation algorithm to optimize the feature bucketing process. Experiments on the Car Hacking dataset show that FEXGBIDS achieves a detection accuracy of 97.26%, comparable to centralized XGBoost, and improves computational efficiency by 65% over the traditional Paillier method. …”
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MicroRNAs in long COVID: roles, diagnostic biomarker potential and detection by Naomi-Eunicia Paval, Olga Adriana Căliman-Sturdza, Andrei Lobiuc, Mihai Dimian, Ioan-Ovidiu Sirbu, Mihai Covasa

“…While conventional miRNA detection techniques have been valuable, they face limitations in sensitivity, throughput, and detecting RNA modifications. …”
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(In)efficiency of Beneish M Score model in detecting fraud in financial statements by Slaviša Đorđević, Nebojša Mitić, Slaviša Vučurević

“…In the continuation of this article, we will try to explain in more detail the essence of Beneish's M-score model for detecting possible inconsistencies in the asset-financial and yield position of the observed accounting entity, the results that confirm its (in)efficiency, as well as the doubts that still exist and that can raise doubts about the (all) possibility of this model in detecting fraud in financial statements.…”
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Blockchain Enabled Federated Learning for Detection of Malicious Internet of Things Nodes by Rachid Alami, Anjanava Biswas, Varun Shinde, Ahmad Almogren, Ateeq Ur Rehman, Tahseen Shaikh

“…However, these networks are vulnerable to the presence of malicious nodes, which compromise the efficiency of the decision-making process in the IoT network. Many machine learning and artificial intelligence techniques are proposed to solve this issue. …”
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Sources, distribution, behavior, and detection techniques of microplastics in soil: A review by Yu-chen Yan, Zhong-fang Yang

“…This will result in composite pollution and harm to the ecosystem; (3) microplastics in soil not only can destroy the activity of key soil microorganisms, but also enter the body of crops and soil animals, affecting normal growth of crops and soil animals, and further threaten human health; (4) at present, there is no unified operating standard for the sampling, processing, and detection process of microplastics. …”
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An automatic approach to detect skin cancer utilizing active infrared thermography by Ricardo F. Soto, Sebastián E. Godoy

“…These features were implemented in a support-vector machine classifier to detect malignancy. Notably, our method addresses a common limitation in existing approaches—manual lesion selection—by automating the process using a U-Net convolutional neural network.We validated our system by comparing U-Net's performance with expert dermatologist segmentations, achieving a 17% improvement in the Jaccard index over a semi-automatic algorithm. …”
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Wind Turbine Blade Fault Detection Method Based on TROA-SVM by Zhuo Lei, Haijun Lin, Xudong Tang, Yong Xiong, He Wen

“…This method integrates the Tyrannosaurus Optimization Algorithm (TROA) with a support vector machine (SVM), aiming to enhance the accuracy and reliability of fault detection. The process begins with the careful preprocessing of raw noise signals collected from wind turbines during actual operational conditions. …”
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