Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers by Raja Azlina Raja Mahmood, AmirHossien Abdi, Masnida Hussin

“…Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  …”
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Cost-Efficient RSSI-Based Indoor Proximity Positioning, for Large/Complex Museum Exhibition Spaces by Panos I. Philippopoulos, Kostas N. Koutrakis, Efstathios D. Tsafaras, Evangelia G. Papadopoulou, Dimitrios Sigalas, Nikolaos D. Tselikas, Stefanos Ougiaroglou, Costas Vassilakis

“…However, it suffers from low accuracy (in NLOS traffic), noise, and multipath fading issues. In large complex spaces, such as museums, where heavy visitor traffic is expected to seriously impact the ability to maintain LOS, RSSI coupled with Bluetooth Low Energy (BLE) seems ideal in terms of market availability, cost-/energy-efficiency and scalability that affect competing technologies, provided it achieves adequate accuracy. …”
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Selective trimerization of ethylene using chromium catalysts complexed with tridentate ligands supported on titanium modified silica by Younes Habibi, Mohamadreza Marefat, Sajjad Gharajedaghi, Masoumeh Mohamadhoseini, Zahra Mohamadnia, Ebrahim Ahmadi

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MFA-net: Object detection for complex X-ray cargo and baggage security imagery. by Thanaporn Viriyasaranon, Seung-Hoon Chae, Jang-Hwan Choi

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Low complexity hybrid iterative algorithm based signal detection in massive MIMO system by Shufeng ZHAO, Bin SHEN, Furong YANG

“…Among the uplink signal detection algorithms for massive MIMO systems,the minimum mean square error (MMSE) algorithm can achieve the near-optimal linear detection performance.However,conventional MMSE usually involves high complexity due to the required matrix inversion of large-size matrix,which makes it hard to implement in realistic applications.Based on joint steepest descent (SD) algorithm and Gauss-Seidel iteration,a low complexity hybrid iterative detection algorithm was proposed.The SD algorithm was employed to obtain an efficient searching direction for the following Gauss-Seidel to speed up convergence.Meanwhile,an approximated method was also proposed to compute the bit log-likelihood ratio (LLR) for soft channel decoding.Simulation results verify that the proposed algorithm can converge rapidly and achieve its performance quite close to that of the MMSE algorithm with only a small number of iterations.Meanwhile,the complexity is reduced by an order of magnitude,which is kept consistently of O(K ²).…”
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On the Design of Near-Optimal Generalized Block-Based Spatial Modulation With Low Detection Complexity by Yen-Ming Chen, Wei-Lun Lin, Heng Lee, Tsung-Lin Chen

“…However, independently designing the constellation cardinality and TACs leads to limited performance gains and an exponential increase in complexity, particularly under maximum-likelihood (ML) detection. …”
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Nearest neighbor permutation entropy detects phase transitions in complex high-pressure systems by Arthur A. B. Pessa, Leonardo G. J. M. Voltarelli, Lúcio Cardozo-Filho, Andres G. M. Tamara, Claudio Dariva, Papa M. Ndiaye, Matjaž Perc, Haroldo V. Ribeiro

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SmartRipen: LSTM-GRU feature selection& XGBoost-CNN for fruit ripeness detection by Archana Ganesh Said, Bharti Joshi

“…A Bacterial Foraging Optimizer (BFO) built around variance maximization retains high-density as well as discriminative features during feature selection. A novel Convolutional XGBoost Network (CXGBN) combines CNN's completely connected layers with XGBoost classifications for enhanced efficiency. …”
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Real-Time Power System Event Detection: A Novel Instance Selection Approach by Gabriel Intriago, Yu Zhang

“…This study presents a novel adaptation of the Hoeffding Adaptive Tree (HAT) classifier with an instance selection algorithm that detects and identifies cyber and non-cyber contingencies in real time to enhance the situational awareness of cyber-physical power systems (CPPS). …”
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A Precise Detection Method for Tomato Fruit Ripeness and Picking Points in Complex Environments by Xinfa Wang, Xuan Wen, Yi Li, Chenfan Du, Duokuo Zhang, Chengxiu Sun, Bihua Chen

“…Aiming at the problems faced in practical applications, such as low accuracy of tomato ripeness and picking points detection in complex greenhouse environments, which leads to wrong picking, missed picking, and fruit damage by robots, this study proposes the YOLO-TMPPD (Tomato Maturity and Picking Point Detection) model. …”
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REDETR-RISTD: Real-Time Long-Range Infrared Small Target Detection Network Based on the Reparameterized Efficient Detection Transformer by Ning Li, Daozhi Wei

“…This happens because the targets are small in size, have a weak signal-to-noise ratio (SNR), and are surrounded by complex backgrounds. A novel real-time long-range infrared small target detection network based on the Reparameterized Efficient Detection Transformer (REDETR-RISTD) is proposed. …”
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ASD-YOLO: a lightweight network for coffee fruit ripening detection in complex scenarios by Baofeng Ye, Baofeng Ye, Renzheng Xue, Renzheng Xue, Haiqiang Xu, Haiqiang Xu

“…In order to improve the detection efficiency of coffee fruit maturity, this paper proposes an improved detection method based on YOLOV7 to efficiently identify the maturity of coffee fruits, called ASD-YOLO. …”
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YOLOv8n-CSD: A Lightweight Detection Method for Nectarines in Complex Environments by Guohai Zhang, Xiaohui Yang, Danyang Lv, Yuqian Zhao, Peng Liu

“…To improve the accuracy of nectarine fruit recognition in complex environments and to increase the efficiency of automatic orchard-picking robots, a lightweight nectarine detection method, YOLOv8n-CSD, is proposed in this study. …”
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SDES-YOLO: A high-precision and lightweight model for fall detection in complex environments by Xiangqian Huang, Xiaoming Li, Limengzi Yuan, Zhao Jiang, Hongwei Jin, Wanghao Wu, Ru Cai, Meilian Zheng, Hongpeng Bai

“…In the field of object detection, while YOLOv8 has recently made notable strides in detection accuracy and speed, it still faces challenges in detecting falls due to variations in lighting, occlusions, and complex human postures. …”
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Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set by Jinna Li, Yuan Li, Haibin Yu, Yanhong Xie, Cheng Zhang

“…A novel fault detection technique is proposed to explicitly account for the nonlinear, dynamic, and multimodal problems existed in the practical and complex dynamic processes. …”
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Specific and Rapid Detection of Mycobacterium tuberculosis Complex in Clinical Samples by Polymerase Chain Reaction by Anamika Singh, Vijendra Kumar Kashyap

“…This method, by well distinguishing between MTB complex and NTM, presented a fast and accurate method to detect and diagnose mycobacterial infections more efficiently and could thereby help in better patient management particularly considering the increase in mycobacterial infections due to emergence of NTM over the past decades.…”
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A Parts Detection Network for Switch Machine Parts in Complex Rail Transit Scenarios by Jiu Yong, Jianwu Dang, Wenxuan Deng

“…This article proposes a complex scene rail transit switch machine parts detection network YOLO-SMPDNet (YOLO-based Switch Machine Parts Detecting Network). …”
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Deep Complex Gated Recurrent Networks-Based IoT Network Intrusion Detection Systems by Engy El-Shafeiy, Walaa M. Elsayed, Haitham Elwahsh, Maazen Alsabaan, Mohamed I. Ibrahem, Gamal Farouk Elhady

“…DCGR_IoT employs advanced techniques to enhance anomaly detection capabilities. Convolutional neural networks (CNN) are used for spatial feature extraction and superfluous data are filtered to improve computing efficiency. …”
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Intrusion detection system based on machine learning using least square support vector machine by Pratik Waghmode, Manideep Kanumuri, Hosam El-Ocla, Tanner Boyle

“…The hyperparameters of our model are tuned by utilizing those selected features to maximize the accuracy. The model developed is verified using three different datasets, which have been widely applied to intrusion detection. …”
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Optimizing Input Selection for Cardiac Model Training and Inference: An Efficient 3D Convolutional Neural Networks-Based Approach to Automate Coronary Angiogram Video Selection by Shih-Sheng Chang, MD, PhD, Behrouz Rostami, PhD, Gerardo LoRusso, MD, Chia-Hao Liu, MD, Mohamad Alkhouli, MD

“…Objective: To develop an efficient and automated method for selecting appropriate coronary angiography videos for training deep learning models, thereby improving the accuracy and efficiency of medical image analysis. …”
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