Anomaly-Based Intrusion Detection System for the Internet of Medical Things by Eichie Franklin, Bernardi Pranggono

“…We proposed using hyperparameter-optimized Machine and Deep Learning models to address these concerns to build more robust security solutions. …”
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Smart deep learning model for enhanced IoT intrusion detection by Faisal S. Alsubaei

“…This paper addresses these limitations with large preprocessing steps followed by hyperparameter tuning of machine learning XGBoost and deep learning Sequential Neural Network (OSNN) algorithms through Grid Search for their best values to improve multiclass intrusion detection across varied datasets. …”
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Object Detection using YOLOv8 : A Systematic Review by Nugraha Asthra Megantara, Ema Utami

“…This study evaluates the performance of YOLOv8 based on precision, recall, F1-score, and mean average precision (mAP) metrics, and compares its advantages and limitations with previous YOLO versions and other object detection algorithms. Improvements in the YOLOv8 architecture, including attention mechanisms, improved feature extraction, and hyperparameter optimization, enable significant improvements in accuracy and computational efficiency, especially for small objects and low-light conditions. …”
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Indoor fire and smoke detection based on optimized YOLOv5. by Md Shafak Shahriar Sozol, M Rubaiyat Hossain Mondal, Achmad Husni Thamrin

“…This study aimed to address these challenges in fire and smoke detection in indoor settings. It presents a hyperparameter-optimized YOLOv5 (HPO-YOLOv5) model optimized by a genetic algorithm. …”
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Circle Detection with Adaptive Parameterization: A Bottom-Up Approach by Lin Han, Yan Zhuang, Ke Chen, Yuhua Xie, Guoliang Liao, Guangfu Yin, Jiangli Lin

“…The algorithm implements a local stochastic sampling strategy integrated with a bottom-up circular search mechanism, with all critical parameters in the algorithm derived adaptively based on λ and K, eliminating the need for repetitive hyperparameter search processes. Experiments demonstrate that our methodology achieves an exceptional Fscore of 85.5% on the public circle detection dataset, surpassing state-of-the-art approaches by approximately 7.3%. …”
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Advanced Financial Fraud Malware Detection Method in the Android Environment by Jaeho Shin, Daehyun Kim, Kyungho Lee

“…Five ML algorithms were used to evaluate the proposed approach, and the optimal hyperparameter values for each algorithm were obtained using a grid search method. …”
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Detecting Software Anomalies in Robots by Means of One-class Classifiers by Héctor Quintián, Esteban Jove, Francisco Zayas-Gato, Nuño Basurto, Carlos Cambra, Álvaro Herrero

“…Incorporating collaborative robots into daily life and industrial settings requires strong and dependable systems, especially concerning software. While most anomaly detection research has focused on hardware anomalies, this study addresses the underexplored challenge of software anomaly detection in component-based robotic systems. …”
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Genderly: a data-centric gender bias detection system by Wael Khreich, Jad Doughman

“…We also curated new datasets as community resources for bias detection and mitigation. Our experiments used diverse preprocessing, feature engineering, and hyperparameter optimization methods for traditional ML models and large language models (LLMs) as gender bias detectors, comparing these results to evaluate model performance. …”
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Transformer-based ECG classification for early detection of cardiac arrhythmias by Sunnia Ikram, Amna Ikram, Harvinder Singh, Malik Daler Ali Awan, Sajid Naveed, Isabel De la Torre Díez, Henry Fabian Gongora, Thania Candelaria Chio Montero

“…Electrocardiogram (ECG) classification plays a critical role in early detection and trocardiogram (ECG) classification plays a critical role in early detection and monitoring cardiovascular diseases. …”
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Detection of Electric Network Frequency in Audio Using Multi-HCNet by Yujin Li, Tianliang Lu, Shufan Peng, Chunhao He, Kai Zhao, Gang Yang, Yan Chen

“…With the increasing application of electrical network frequency (ENF) in forensic audio and video analysis, ENF signal detection has emerged as a critical technology. However, high-pass filtering operations commonly employed in modern communication scenarios, while effectively removing infrasound to enhance communication quality at reduced costs, result in a substantial loss of fundamental frequency information, thereby degrading the performance of existing detection methods. …”
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Implementation of Machine Vision Methods for Cattle Detection and Activity Monitoring by Roman Bumbálek, Tomáš Zoubek, Jean de Dieu Marcel Ufitikirezi, Sandra Nicole Umurungi, Radim Stehlík, Zbyněk Havelka, Radim Kuneš, Petr Bartoš

“…The goal of this research was to implement machine vision algorithms in a cattle stable to detect cattle in stalls and determine their activities. …”
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Optimizing Monkeypox Lesions Detection With a Lightweight Hybrid Model by Mehdhar S. A. M. Al-Gaashani, Reem Ibrahim Alkanhel, Dina S. M. Hassan, Abduljabbar S. Ba Mahel, Ahmed Aziz, Mashael M. Khayyat, Ammar Muthanna

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Multimodal Fusion Anomaly Detection Model for Agricultural Wireless Sensors by Zhenggui Zhou

“…The existence of anomalous data leads to the deterioration of the agricultural data quality, which hinders the efficient development of agricultural data analysis. In order to detect anomalous data accurately for agricultural wireless sensors, in this article, we propose an anomaly detection model that combines multimodal fusion and error reconstruction. …”
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Detection and Analysis of Malicious Software Using Machine Learning Models by Selman Hızal, Ahmet Öztürk

“…This paper introduces an innovative approach to enhance the detection of obfuscated malware through the integration of machine learning (ML). …”
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Instagram fake profile detection using an ensemble learning method by Bharti Goyal, Nasib Singh Gill, Preeti Gulia, Noha Alduaiji, Piyush Kumar Shukla, Shreyas J

“…Our method combines scale_pos_weight optimization technique with XGBoost, SMOTE with balanced classes, and GridSearchCV to fine-tune key hyperparameters of Random Forest for fine-tuning purposes, therefore achieving this goal. …”
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Plant leaf disease detection using vision transformers for precision agriculture by Murugavalli S, Gopi R

“…Abstract Plant diseases cause major crop losses worldwide, making early detection essential for sustainable farming. Traditional methods need large training datasets, are expensive, and may overfit. …”
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Efficient lung cancer detection using computational intelligence and ensemble learning. by Richa Jain, Parminder Singh, Mohamed Abdelkader, Wadii Boulila

“…Lung cancer emerges as a major factor in cancer-related fatalities in the current generation, and it is predicted to continue having a long-term impact. Detecting symptoms early becomes crucial for effective treatment, underscoring innovative therapy's necessity. …”
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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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Detecting small seamounts in multibeam data using convolutional neural networks by Tobias Ziolkowski, Colin W. Devey, Agnes Koschmider

“…Seamounts play a crucial role in marine ecosystems, ocean circulation, and plate tectonics, yet most remain unmapped due to limitations in detection methods. While satellite altimetry provides large-scale coverage, its resolution is insufficient for detecting smaller seamounts, necessitating high-resolution multibeam bathymetry. …”
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Recognition of Handwritten Characters in Birch-Bark Manuscripts via Object Detection by Ivan P. Malashin, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

“…This study adapts the YOLO-based object detection framework for detection and recognition of handwritten characters in digital photographs of Novgorod birch-bark fragments. …”
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