Detecting Anomalies in CPU Behavior Using Clustering Algorithms from the Scikit-Learn Library in Python Programming Language by Artem Turashev, Vladimir Sukhomlin

“…This article examines the problem of detecting anomalies in central processing unit (CPU) operation using time series clustering algorithms. …”
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A multilayer deep autoencoder approach for cross layer IoT attack detection using deep learning algorithms by K. Saranya, A. Valarmathi

“…This technology effectively safeguards against various cyber threats, including Man-in-the-Middle attacks at the network layer and Distributed Denial of Service (DDoS) attacks at the transport layer of IoT networks. To improve detection and adapt to emerging attack methods, the M-LDAE system employs deep learning algorithms such as RNNs, GNNs, and TCNs. …”
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An intelligent optimized object detection system for disabled people using advanced deep learning models with optimization algorithm by Marwa Obayya, Fahd N. Al-Wesabi, Menwa Alshammeri, Huda G. Iskandar

“…In this article, a novel Object Detection System for Disabled People Using Advanced Deep Learning Models and Sparrow Search Optimization (ODSDP-ADLMSSO) approach is proposed. …”
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Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms by Rakesh Salakapuri, Panduranga Vital Terlapu, Kishore Raju Kalidindi, Ramesh Naidu Balaka, D. Jayaram, T. Ravikumar

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Efficient sepsis detection using deep learning and residual convolutional networks by Ahmed S. Almasoud, Ghada Moh Samir Elhessewi, Munya A. Arasi, Abdulsamad Ebrahim Yahya, Menwa Alshammeri, Donia Badawood, Faisal Mohammed Nafie, Mohammed Assiri

“…In this article, we present a new deep learning model to detect the occurrence of sepsis and the African vulture optimization algorithm (AVOA) to enhance the model performance. …”
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Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review by Milad Yousefi, Matin Akhbari, Zhina Mohamadi, Shaghayegh Karami, Hediyeh Dasoomi, Alireza Atabi, Seyed Amirali Sarkeshikian, Mahdi Abdoullahi Dehaki, Hesam Bayati, Negin Mashayekhi, Shirin Varmazyar, Zahra Rahimian, Mahsa Asadi Anar, Daniel Shafiei, Alireza Mohebbi

“…This comprehensive systematic review discusses how machine learning (ML), can enhance early detection of these disorders, surpassing traditional diagnostics’ constraints.MethodsIn this review, databases were examined up to August 15th, 2023, for ML data on neurodegenerative and neurocognitive diseases using PubMed, Scopus, Google Scholar, and Web of Science. …”
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Improving Performance of the Convolutional Neural Networks for Electricity Theft Detection by using Cheetah Optimization Algorithm by Hassan Ghaedi, Seyed Reza Kamel Tabbakh, Reza Ghaemi

“…Also, in order to increase the accuracy of the network, abnormal data are clustered using the CHOA algorithm. ISSDA dataset is used to test and evaluate the results. …”
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DGA domain detection and botnet prevention using Q-learning for POMDP by Y. V. Bubnov, N. N. Ivanov

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K-Gen PhishGuard: an Ensemble Approach for Phishing Detection with K-Means and Genetic Algorithm by Ali Al-Hafiz, Adnan Jabir, Shamala Subramaniam

“…Additionally, the performance across four clusters demonstrates the positive impact of K-Means clustering in improving classification accuracy for specific data groups. As proven by the obtained results, integrating feature selection with ensemble learning is effective for phishing detection; moreover, the scalability and efficiency of such a solution in real-world applications are demonstrated. …”
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Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm by Hussein Ridha Sayegh, Wang Dong, Bahaa Hussein Taher, Muhanad Mohammed Kadum, Ali Mansour Al-madani

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Intrusion Detection-Data Security Protection Scheme Based on Particle Swarm-BP Network Algorithm in Cloud Computing Environment by Zhun Wang, Xue Chen

“…Aiming at the problems of low detection rate and high false detection rate of intrusion detection algorithms in the traditional cloud computing environment, an intrusion detection-data security protection scheme based on particle swarm-BP network algorithm in a cloud computing environment is proposed. …”
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Significance of Machine Learning-Driven Algorithms for Effective Discrimination of DDoS Traffic Within IoT Systems by Mohammed N. Alenezi

“…In this paper, the effectiveness of supervised machine learning (ML) classification and deep learning (DL) algorithms in detecting DDoS attacks on IoT networks was investigated by conducting an extensive analysis of network traffic dataset (legitimate and malicious). …”
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Safe AI for coral reefs: Benchmarking out-of-distribution detection algorithms for coral reef image surveys by Mathew Wyatt, Sharyn Hickey, Ben Radford, Manuel Gonzalez-Rivero, Nader Boutros, Nikolaus Callow, Nicole Ryan, Arjun Chennu, Mohammed Bennamoun, James Gilmour

“…We show with a comparative analysis that the performance of OOD detection algorithms is variable, and highly dependent on in-distribution and out-of-distribution data composition. …”
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Blockchain Enabled Secure Medical Data Transmission and Diagnosis Using Golden Jackal Optimization Algorithm with Deep Learning by Kiruthikadevi Kulandaivelu, Sivaraj Rajappan, Vijayakumar Murugasamy

“…Abstract The incorporation of deep learning (DL) and blockchain (BC) technologies in healthcare revolutionizes disease diagnoses and improves data security. …”
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Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm by Mosleh Hmoud Al-Adhaileh, Mosleh Hmoud Al-Adhaileh, Sultan Ahmad, Alhasan A. Alharbi, Mohammed Alarfaj, Mohammed Alarfaj, Mukta Dhopeshwarkar, Theyazn H. H. Aldhyani

“…An essential step that may help clinicians identify and treat epileptic seizures is the differentiation between epileptic and non-epileptic signals by use of epileptic seizure detection categorization.MethodsIn this work, we investigated Machine learning algorithms including Random Forest, Gradient Boosting, and K-Nearest Neighbors, alongside advanced DL architectures such as Long Short-Term Memory networks and Long-term Recurrent Convolutional Networks for detecting epileptic seizures in terms of difficulties and procedures evolved depending on EEG data. …”
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Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned by Marketa Ciharova, Khadicha Amarti, Ward van Breda, Ward van Breda, Martin J. Gevonden, Sina Ghassemi, Annet Kleiboer, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Sophia Trofimova, Alexander C. Cooper, Xianhua Peng, Xianhua Peng, Mieke Schulte, Mieke Schulte, Eirini Karyotaki, Eirini Karyotaki, Eirini Karyotaki, Pim Cuijpers, Pim Cuijpers, Pim Cuijpers, Heleen Riper, Heleen Riper

“…Stress monitoring may be supported by valid and reliable machine-learning algorithms. However, investigation of algorithms detecting stress severity on a continuous scale is missing due to high demands on data quality for such analyses. …”
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Cervical Cancer Prediction Based on Imbalanced Data Using Machine Learning Algorithms with a Variety of Sampling Methods by Mădălina Maria Muraru, Zsuzsa Simó, László Barna Iantovics

“…For instance, an experienced medical doctor may diagnose a case but need expert support that related to another medical specialty. Data imbalance is frequent in healthcare data and has a negative influence on predictions made using ML algorithms. …”
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Intelligent Detection of Oceanic Front in Offshore China Using EEFD-Net with Remote Sensing Data by Ruijie Kong, Ze Liu, Yifei Wu, Yong Fang, Yuan Kong

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Comparative analysis of machine learning models for the detection of fraudulent banking transactions by Pedro María Preciado Martínez, Ricardo Francisco Reier Forradellas, Luis Miguel Garay Gallastegui, Sergio Luis Náñez Alonso

“…This research presents a comparative analysis of machine learning models for detecting fraudulent banking transactions, a growing problem in the digital financial sector. …”
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AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING by Muhammad Anas, Muhammad Atif Imtiaz, Saad Khan, Arshad Ali, Noor Fatima Naghman, Hamayun Khan, Sami Albouq

“…The present work attempts to better predict this disease from the chest pain symptom, and classify it by designing an efficient machine learning system based on a dataset with 303 patient data made available to the public domain. …”
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