Advancements in cyberthreat intelligence through resource exhaustion attack detection using hybrid deep learning with heuristic search algorithms by S. Jayanthi, Swathi Sowmya Bavirthi, P. Murali, K. Vijaya Kumar, Hend Khalid Alkahtani, Mohamad Khairi Ishak, Samih M. Mostafa

“…The primary goal of the CREA-HDLMOA technique is to advance an effective method for DDoS attack detection using advanced optimization algorithms. Initially, the data normalization stage leverages linear scaling normalization (LSN) for converting input data into a beneficial format. …”
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IMPLEMENTATION OF MAPPING-BASED MACHINE LEARNING ALGORITHM AS NON-STRUCTURAL DISASTER MITIGATION TO DETECT LANDSLIDE SUSCEPTIBILITY IN TAKARI DISTRICT by Sefri Imanuel Fallo, Lidia Paskalia Nipu

“…A range of machine learning algorithms, including Support Vector Machine, Naive Bayes Classifier, Ordinal Logistic Regression, Random Forest, and Decision Tree, were harnessed to evaluate rainfall data within the context of landslide susceptibility. …”
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Research on Arthroscopic Images Bleeding Detection Algorithm Based on ViT-ResNet50 Integrated Model and Transfer Learning by Zewen Liu, Shaoyi Zhou, Jianqiao Chu, Zhiyuan Chai, Dongdong Chang, Yi Yuan, Jinling Qin, Xiancheng Wang

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An intelligent framework for sarcasm detection in Arabic tweets using deep learning with Al-Biruni earth radius optimization algorithm by Reema G. AL-anazi, Muhammad Swaileh A. Alzaidi, Majdy M. Eltahir, Wafa Sulaiman Almukadi, Samah Hazzaa Alajmani, Abdulbasit A. Darem, Mohammed Alshahrani, Nawaf Alhebaishi

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An intelligent identification for pest and disease detection in wheat leaf based on environmental data using multimodal data fusion by SHENG-HE XU, Sai Wang

“…First, deep - learning algorithms in image analysis detect early - stage pests and diseases on wheat leaves. …”
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Design and Optimization of Hybrid CNN-DT Model-Based Network Intrusion Detection Algorithm Using Deep Reinforcement Learning by Lu Qiu, Zhiping Xu, Lixiong Lin, Jiachun Zheng, Jiahui Su

“…With the rapid development of network technology, modern systems are facing increasingly complex security threats, which motivates researchers to continuously explore more advanced intrusion detection systems (IDSs). Even though they work effectively in some situations, the existing IDSs based on machine learning or deep learning still struggle with detection accuracy and generalization. …”
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Design of tomato picking robot detection and localization system based on deep learning neural networks algorithm of Yolov5 by Jianwei Zhao, Wei Bao, Leiyu Mo, Zhiting Li, Yushuo Liu, Jiaqing Du

“…In a greenhouse setting, 640 tomato images were collected and categorized into three classes: unobstructed, leaf-covered, and branch-covered.Data augmentation techniques, including rotation, translation, and CutMix, were applied to the collected images, and the YOLOv5 model was trained using a warmup strategy.Through a comparative analysis of different object detection algorithms on the tomato dataset, the feasibility of using the YOLOv5 deep learning algorithm for tomato detection was validated. …”
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VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection by Sanam Salman Kazi, Bhakti Palkar, Dhirendra Mishra

“…It also requires fewer parameters and takes minimum training time. • The major contribution of this study is the design of an optimized, efficient and enhanced deep learning technique for multiclass rice crop disease detection embracing with batch normalization, dropout and genetic optimization algorithm to improve generalization power and restrict the overlearning capability for seen and unseen data. • Proposed VCNet, a shallow model with deep feature extraction, employs VGG16 layers for initial extraction fused with custom CNN architecture to correctly detect the challenging classes of diseases like sheath rot in multiclass classification. • The most significant observation is that VCNet accurately predicts the rice disease for each class of diseases under study whereas the existing powerful models largely misclassified for some classes of diseases in multiclass classification.…”
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Coffee Leaf Rust Disease Detection and Implementation of an Edge Device for Pruning Infected Leaves via Deep Learning Algorithms by Raka Thoriq Araaf, Arkar Minn, Tofael Ahamed

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Metric-Based Few-Shot Transfer Learning Approach for Voice Pathology Detection by Jong-Ho Won, Deok-Hwan Kim

“…The main contributions of this study include the implementation of few-shot learning (FSL) in voice pathology detection, addressing data scarcities and class imbalances through its integration with transfer learning. …”
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Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy by Ansam Khraisat, Ammar Alazab, Moutaz Alazab, Areej Obeidat, Sarabjot Singh, Tony Jan

“…PEIoT-DS use federated learning to create a comprehensive intrusion detection model without necessitating the transmission of raw data to a central server. …”
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Adversarial patch defense algorithm based on PatchTracker by Zhenjie XIAO, Shiyu HUANG, Feng YE, Liqing HUANG, Tianqiang HUANG

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Automated detection of spreading depolarizations in electrocorticography by Sreekar Puchala, Ethan Muchnik, Anca Ralescu, Jed A. Hartings

“…Here we developed an automated method for SD detection by training machine-learning models on electrocorticography data from a 14-patient cohort that included 1,548 examples of SD direct-current waveforms as identified in expert manual scoring. …”
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Malware detection approach based on improved SOINN by Bin ZHANG, Lixun LI, Shuqin DONG

“…To deal with the problems of dynamic update of detection model and high computation costs in malware detection model based on batch learning,a novel malware detection approach is proposed by combing SOINN and supervised classifiers,to reduce computation costs and enable the detection model to update dynamically with the assistance of SOINN′s incremental learning characteristic.Firstly,the improved SOINN was given.According to the whole alignment algorithm,search the adjusted weights of neurons under all input sequences in the learning cycle and then calculate the average value of all adjusted weights as the final result,to avoid SOINN′s stability under different input sequences and representativeness of original data,therefore improve malware detection accuracy.Then a data preprocessing algorithm was proposed based on nonnegative matrix factor and Z-score normalization to transfer the malware behavior feature vector from high dimension and high order to low dimension and low order,to speed up and avoid overfitting and further improve detection accuracy.The results of experiments show that proposed approach supports dynamic updating of detection model and has a significantly higher accuracy of detecting unknown new samples and lower computation costs than tradition methods.…”
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RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection by Ismail Keshta, Pallavi Sagar Deshpande, Mohammad Shabaz, Mukesh Soni, Mohit kumar Bhadla, Yasser Muhammed

“…Abstract Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). …”
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Early Heart Attack Detection Using Hybrid Deep Learning Techniques by Niga Amanj Hussain, Aree Ali Mohammed

“…This study integrates a deep learning approach to predict and detect heart attacks early by classifying patient data as normal or abnormal. …”
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Investigating Catching Hotspots of Fishing Boats: A Framework Using BeiDou Big Data and Deep Learning Algorithms by Fen Wang, Xingyu Liu, Tanxue Chen, Hongxiang Feng, Qin Lin

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Deep learning outperforms existing algorithms in glacier surface velocity estimation with high-resolution data – the example of Austerdalsbreen, Norway by Harald Zandler, Jakob Abermann, Benjamin A. Robson, Alexander Maschler, Thomas Scheiber, Jonathan L. Carrivick, Jacob C. Yde

“…High-resolution datasets, such as UAV imagery, offer a promising solution to tackle these issues and to study small-scale glacier dynamics, but new workflows are required to handle such data. Therefore, we tested the potential of new deep learning-based image-matching algorithms for deriving glacier surface velocities across the ablation area of a glacier with strong spatial variability in surface velocities (<5 m/yr to >100 m/yr) and substantial changes in surface properties between image acquisitions. …”
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Harnessing optimization with deep learning approach on intelligent transportation system for anomaly detection in pedestrian walkways by Sultan Refa Alotaibi, Fatma S. Alrayes, Mashael Maashi, Mohammed Maray, Mohammed A. Alliheedi, Donia Badawood, Moneerah Alotaibi

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Multi-scale eddy identification and analysis based on deep learning method and ocean color data by Meng Hou, Lixing Fang, Kai Wu, Jie Yang, Ge Chen

“…The algorithm integrates high resolution ocean color data, digital image processing, artificial intelligence, and multi-scale object detection technologies. …”
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