LSTM-ANN-GA A HYBRID DEEP LEARNING MODEL FOR PREDICTIVE MAINTENANCE OF INDUSTRIAL EQUIPEMENT by Farouk Noumich, Abouchabaka Jaafar, Amrani Ayoub

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Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection by Opeyemi Taiwo Adeniran, Blessing Ojeme, Temitope Ezekiel Ajibola, Ojonugwa Oluwafemi Ejiga Peter, Abiola Olayinka Ajala, Md Mahmudur Rahman, Fahmi Khalifa

“…With the advancements in deep learning methods, AI systems now perform at the same or higher level than human intelligence in many complex real-world problems. The data and algorithmic opacity of deep learning models, however, make the task of comprehending the input data information, the model, and model’s decisions quite challenging. …”
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ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning by Danlei Li, Nirmal-Kumar C. Nair, Kevin I-Kai Wang

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A GNN-Based False Data Detection Scheme for Smart Grids by Junhong Qiu, Xinxin Zhang, Tao Wang, Huiying Hou, Siyuan Wang, Tiejun Yang

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Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges by Tomer Raitsis, Yossi Elgazari, Guy E. Toibin, Yotam Lurie, Shlomo Mark, Oded Margalit

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Context-Aware Adaptive Encryption: Integrating Sensitive Data Detection and Network intrusion detection for Dynamic Data Security and Encryption by Leonardo C. Lawrence, Ramin Giovanni, Cynthia Calongne, Abdullah Alshboul

“…This research project develops and evaluates a novel context-aware adaptive encryption system that integrates sensitive data detection, network intrusion detection, and dynamic encryption techniques to enhance data security. …”
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Performance Analysis of Real-Time Detection Transformer and You Only Look Once Models for Weed Detection in Maize Cultivation by Oscar Leonardo García-Navarrete, Jesús Hernán Camacho-Tamayo, Anibal Bregon Bregon, Jorge Martín-García, Luis Manuel Navas-Gracia

“…To reduce the influence of weeds, precision weeding is used, which uses image sensors and computational algorithms to identify plants and classify weeds using digital images. …”
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State-of-the-Art Review on the Application of Unmanned Aerial Vehicles (UAVs) in Power Line Inspections: Current Innovations, Trends, and Future Prospects by Bongumsa Mendu, Nhlanhla Mbuli

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Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale by Harsh Vazirani, Xiaofeng Wu, Anurag Srivastava, Debajyoti Dhar, Divyansh Pathak

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Lightweight blasthole image detection and positioning method by Shan PAN, Ting YU, Zhongwen YUE, Zijian TIAN, Qingyu JIN

“…Compared with the minimum baseline model, the Mv3-SCDn blasthole algorithm has the best blasthole detection effect, the number of blasthole detection model parameters is reduced by 7.17%, and the detection speed is increased by 45.44%. …”
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Rapid video copy detection on compressed domain by ZHANG Yong-dong, ZHANG Dong-ming, GUO Jun-bo, TANG Sheng

“…To reduce the detection time efficiency under large scale data environment, a rapid algorithm was proposed on compressed domain using a two-level hierarchical detection scheme.The ordinal measures of DCT coefficients were adopted as visual features for similarity-matching in order to reduce the computational load in video decoding.Inverted indexing structure was used to accelerate the first level detection process.The experiment results show, compared with the previous algorithm, the algorithm can improve the detection speed obviously with the similar detection precision.…”
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Lightweight Vehicle Detection Based on Mamba_ViT by Ze Song, Yuhai Wang, Shuobo Xu, Peng Wang, Lele Liu

“…Vehicle detection algorithms are essential for intelligent traffic management and autonomous driving systems. …”
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Study on privacy preserving encrypted traffic detection by Xinyu ZHANG, Bingsheng ZHANG, Quanrun MENG, Kui REN

“…Existing encrypted traffic detection technologies lack privacy protection for data and models, which will violate the privacy preserving regulations and increase the security risk of privacy leakage.A privacy-preserving encrypted traffic detection system was proposed.It promoted the privacy of the encrypted traffic detection model by combining the gradient boosting decision tree (GBDT) algorithm with differential privacy.The privacy-protected encrypted traffic detection system was designed and implemented.The performance and the efficiency of proposed system using the CICIDS2017 dataset were evaluated, which contained the malicious traffic of the DDoS attack and the port scan.The results show that when the privacy budget value is set to 1, the system accuracy rates are 91.7% and 92.4% respectively.The training and the prediction of our model is efficient.The training time of proposed model is 5.16 s and 5.59 s, that is only 2-3 times of GBDT algorithm.The prediction time is close to the GBDT algorithm.…”
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ALOGORITHMS FOR AUTOMATIC RUNWAY DETECTION ON VIDEO SEQUENCES by A. I. Logvin, A. V. Volkov

“…The article discusses algorithm for automatic runway detection on video sequences. …”
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IoT intrusion detection method for unbalanced samples by ANTONG P, Wen CHEN, Lifa WU

“…In recent years, network traffic increases exponentially with the iteration of devices, while more and more attacks are launched against various applications.It is significant to identify and classify attacks at the traffic level.At the same time, with the explosion of Internet of Things (IoT) devices in recent years, attacks on IoT devices are also increasing, causing more and more damages.IoT intrusion detection is able to distinguish attack traffic from such a large volume of traffic, secure IoT devices at the traffic level, and stop the attack activity.In view of low detection accuracy of various attacks and sample imbalance at present, a random forest based intrusion detection method (Resample-RF) was proposed, which consisted of three specific methods: optimal sample selection algorithm, feature merging algorithm based on information entropy, and multi-classification greedy transformation algorithm.Aiming at the problem of unbalanced samples in the IoT environment, an optimal sample selection algorithm was proposed to increase the weight of small samples.Aiming at the low efficiency problem of random forest feature splitting, a feature merging method based on information entropy was proposed to improve the running efficiency.Aiming at the low accuracy problem of random forest multi-classification, a multi-classification greedy transformation method was proposed to further improve the accuracy.The method was evaluated on two public datasets.F1 reaches 0.99 on IoT-23 dataset and 1.0 on Kaggle dataset, both of which have good performance.The experimental results show that the proposed model can effectively identify the attack traffic from the massive traffic, better prevent the attack of hackers on the application, protect the IoT devices, and thus protect the related users.…”
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cpop: Detecting Changes in Piecewise-Linear Signals by Paul Fearnhead, Daniel Grose

“…There are many different types of changes that one may wish to detect, and a widerange of algorithms and software for detecting them. …”
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Detecting anomalies in graph networks on digital markets. by Agata Skorupka

“…It compares different graph algorithms to extract feature sets for anomaly detection models. …”
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MODERN METHODS OF AUTOMATIC RECTANGLE OBJECTS DETECTION by E. S. Matusevich, I. E. Kheidorov

“…The algorithm for object detection based on correlation analysis, as well as the algorithm containing the use of Canny edge detector, Hough and Radon transform for lines detection, and then, depending on the properties of the object lines combining in the rectangular area, were explored. …”
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Human skin region detection in Web video by GUO Jun-bo1, ZHANG Dong-ming1, ZHANG Yong-dong1, XU Jie1, WANG Wen-ying1

“…An improved detection algorithm was presented.Firstly,an improved skin color model,which adopted many preprocessing including optical offset,auto white balance,and auto gamma correction,was built to detect candidate skin pixels.Secondly,the image was filtered with erosion and dilation to exclude unconnected candidate pixels.Lastly,gray co-matrix was used to exclude those candidate human skin regions with complex texture.Experimental results show that this algorithm improves the skin region detection precision.…”
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Distributed Abnormal Activity Detection in Smart Environments by Chengliang Wang, Qian Zheng, Yayun Peng, Debraj De, Wen-Zhan Song

“…DetectingAct works as follows. Firstly, DetectingAct finds the normal activity patterns through duration-dependent frequent pattern mining algorithm (DFPMA), which adopts unsupervised learning instead of supervised learning. …”
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