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Real-time flight path conflict detection method based on space box

Real-time flight path conflict detection method based on spa...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Zhenfeng Li Yuhan Chen Ruqin Wang Jianghua Pan Unit 63969 of PLA Nanjing China Nanjing Les Electronic Equipment Co. Ltd. Nanjing China

Flight conflict detection is an important guarantee for flight safety. The traditional real-time flight conflict detection is based on probability, statistics and geometry. Conflict calculation is performed on air targets in turn. The common shortcomings such as large amount of calculation, high complexity, time delay and poor stability do not apply to the air traffic control system in large-scale airspace. In recent years, with the development and application of multiprocessor and distributed computing technology, a real-time flight path conflict detection method based on space box is presented, which can be used in distributed computing environment to solve the problem of flight conflict detection in large-scale airspace air traffic control system. The algorithm transforms conflict detection that used to be centered on air targets into centered on space boxes. First, the air traffic control airspace is divided into cube-shaped space boxes. Calculate the space box where the target location is located based on the received target spot information. Then, the number of air targets in the space box is counted, and track conflicts are calculated for multiple batches of targets in the same space box to determine the possibility of conflicts. The simulation results show that the calculation time reaches 10 milliseconds, the computational complexity is greatly reduced, and the operation is stable. It can be used in engineering applications directly.

关键词： Target tracking Simulation Transforms Aerospace electronics Real-time systems Stability analysis Partitioning algorithms

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Hardware Trojans: Threats, detection, and Prevention 23

Hardware Trojans: Threats, Detection, and Prevention

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Proceedings of the 2023 3rd international conference on Big Data, Artificial intelligence and Risk Management

作者： Chaoen Xiao Xiangze Chang Jianxin Wang Lei Zhang Hongke Xu Xinwei Wu Yuzheng Zheng Department of Electronics and Communication Engineering China

ISBN: (纸本)9798400716478

With the rapid development of integrated circuit technology, hardware Trojans have attracted widespread attention as a growing threat. Positioned at the hardware level of electronic devices and integrated circuits, hardware Trojans possess concealment and destructiveness, potentially leading to severe security threats. This paper conducts an in-depth exploration of the threats posed by hardware Trojans, including typical and special instances of hardware Trojans. Regarding the detection of hardware Trojans, this paper introduces methods such as destructive and non-destructive detection and explores the application of machine learning in hardware Trojan detection. Additionally, this paper proposes a series of strategies for preventing hardware Trojans, including reinforcement measures for detection and physical security measures, aiming to address the evolving threat of hardware Trojans. Finally, this paper discusses the development trends of hardware Trojans from the perspectives of attackers and defenders. Through comprehensive research and analysis, this paper aims to deepen the understanding of the issue of hardware Trojans and provide robust support for ensuring the security of digital infrastructure.

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A Novel Cloud-Based Framework for Leaf Disease detection and Classification: Enhancing Plant Health Using Android Applications

A Novel Cloud-Based Framework for Leaf Disease Detection and...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Ajmeera Kiran Arpit Namdev Radha Raman Chandan Pravin B. Waghmare Dharmesh Dhabliya Ankur Gupta Dept. of CSE MLR Institute of Technology Dundigal Hyderabad Telangana India Dept. of IT University Institute of Technology RGPV Bhopal MP India Dept. of Computer Science School of Management Sciences Varanasi UP India Dept. of Civil Engineering Acharya Shrimannarayan Polytechnic MSBTE Mumbai India Dept. of IT Vishwakarma Institute of Information Technology Pune Maharashtra India Dept. of CSE Vaish College of Engineering Rohtak Haryana India

The difficult challenge of identifying the leaf disease in agriculture is employed to stop the dangerous epidemic. The majority of plant diseases are brought on by fungus, bacteria, and viruses, with the fungi often attached to the plant's reproductive organs. As a result, bacteria have simpler life cycles and are more dangerous than fungi. Similar to bacteria, viruses are microscopic particles that contain genetic information and protein but no additional protein. Therefore, it is crucial to spot illness in plant leaves. Therefore, the goal of this research is to identify leaf disease in a cloudy environment. The use of smartphones for many applications has increased recently. A brand-new operating system called Android is freely accessible in the Android Market. Thus, this study uses an Android application to develop a cloud-based illness categorization system.

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A Novel Optimization Strategies for Detecting and Preventing Distributed Denial of Service Attacks in Wireless Networks

A Novel Optimization Strategies for Detecting and Preventing...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Saurabh Kumar Jambi Ratna Raja Kumar Dipannita Mondal S. Hemelatha Geetha. B Mandar P. Diwakar Department of Commerce Mangalayatan University Aligarh India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Artificial Intelligence And Data Science Department Dr. D. Y.Patil College Of Engineering And Innovation Talegaon Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India

allotted Denial of carrier (DDoS) assaults have end up a prime difficulty in trendy wireless networks. these assaults aim to disrupt the regular operation of networks by means of overwhelming them with excessive traffic from more than one assets. traditional DDoS detection and prevention strategies have established to be useless in wireless networks due to the dynamic and unpredictable nature of the wireless surroundings. To deal with this problem, novel optimization techniques were proposed to enhance the potential of wireless networks to discover and prevent DDoS *** of the key techniques is the use of wise algorithms which includes machine learning and synthetic intelligence. these algorithms can analyze and study from network site visitors patterns to pick out strange behavior this is indicative of a DDoS attack. by way of continuously tracking the network and adapting to converting traffic patterns, these algorithms are capable of as it should be locate and mitigate DDoS assaults in actual-time. any other method is the deployment of robust and dynamic defense mechanisms. those mechanisms include the usage of anomaly detection, traffic filtering, and traffic shaping. Anomaly detection strategies rent statistical techniques to hit upon patterns that deviate from ordinary network conduct. traffic filtering involves figuring out and blockading malicious site visitors, even as site visitors shaping manages and controls the flow of legitimate visitors to prevent congestion and enhance community overall performance.

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detection of estrus through automated classification approaches using vocalization pattern in Murrah buffaloes

Detection of estrus through automated classification approac...

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international Symposium on Artificial intelligence and Signal Processing (AISP)

作者： Sugandha Sharma Virender Kadyan Speech and Language Research Centre School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand India

The field that deals with the studying of animal sounds to detect or classify various animal conditions in an open or a closed setup. Bioacoustics has its prospective applications in speaker identification, detection of respiratory distress, estrus phases, stress induced by ecological conditions, separation and weaning and so on. In dairy cattle, especially murrah buffalo breed, inaccurate detection of estrus is a significant constraint that affects the reproductive capacity of small as well as large cattle-herds. Detecting estrus in dairy cattle at correct time assists livestock managers and farmers to augment milk production, profitability and maintaining welfare of *** studies on relationship between vocal behavior and reproductive performance is still at a very primary stage. This is due to inadequate amount of research work that has been done in order to study the association between acoustic features of animal vocal sounds and estrus owing to limited publicly available dataset. Outcomes of animal vocal sounds based studies are primarily dataset-dependent, i.e., the findings are ineffective if recording conditions are unidentified. Modern developments in deep-learning and machine-learning based approaches have led to the development of high-performance non-invasive ‘‘animal sound classification system These systems, however, are built on scarce speech-corpora which is a preferred requisite for machine learning model training, which requires hours of good-quality recordings of sound of a single speaker in a noiseless environment. Developing such a good-quality sound corpus manually requires labour and time which makes the process expensive. In the present paper, the authors have executed machine-learning models like Random Forest, Decision Tree and SVM as well as a custom-built CNN architecture on a dataset and have carried out a comparative analysis of the above-mentioned techniques.

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Revolutionizing Video Event detection: An Efficient Approach Utilizing Temporal Convolutional Neural Networks Using Real-time Performance

Revolutionizing Video Event Detection: An Efficient Approach...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Laith H. Alzubaidi Bura Vijay Kumar Zainab Alassedi A. H. A. Hussein Rajesh N The Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Warangal Telangana India National University of Science and Technology Dhi Qar Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Automatic detection, localization and interpretation of an unusual event in a sequence of video is a challenging task due to its ambiguous and complex nature. A key challenge with manual detection and sequential analysis lies in the fact that video analysis often demands an immense amount of time and resources. To address this challenge, we have adopted an approach that involved Temporal Convolutional Neural Networks (TCNNs) and Long Short-Term Memory (LSTM) methods. This methodology offers a more efficient way to detect activities in video data. Furthermore, our approach enhances the capture of contextual information within the video, leading to improved results in video content detection. TCNNs, in particular, stand out as they can process input sequences in parallel, resulting in faster training and inference when compared to Recurrent Neural Networks (RNNs) which process data sequentially. To evaluate the effectiveness of these models, we conducted experiments on a manually annotated dataset known as UCF101, which contains 101 different activities. This dataset comprises 1,200 videos representing various actions. Our experimental results demonstrate that TCNNs achieved an accuracy rate of 93.80%, surpassing other existing models are ResNet and Multiclass Support vector machine in overall performance. TCNN, in particular, exhibited exceptional accuracy, indicating its effectiveness in video event detection.

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Phishing Site detection Using Logistic Regression and Fine Tuning It Using Various Optimization Parameters

Phishing Site Detection Using Logistic Regression and Fine T...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Muskan Singla Kanwarpartap Singh Gill Rahul Chauhan Hemant Singh Pokhariya Deepak Banerjee Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Cybercriminals frequently prefer to deceive people into compromising their own security than using exploits or complex assaults to get access to networks. No matter what technological safeguards are in place, this strategy can be very effective. To conceal their actions and get around conventional security measures, economic resources through phishing attempts might leverage encrypted connections, such as HTTPS. This kind of intrusion has been detected and is being prevented by machine learning technologies. Using two different algorithms (Logistic Regression and Multinomial Naive Bayes) to analyse the URLs and various datasets to compare the results with previous research, we suggested a machine learning-based phishing detection system in this paper. The experimental findings show that the suggested models function superbly and have a high success rate in a social environment.

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Automated detection of Pain Across Varied Intensity Levels Through the Fusion of CNN and Random Forest

Automated Detection of Pain Across Varied Intensity Levels T...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Ankita Suryavanshi Vinay Kukreja Ayush Dogra Abhishek Bhattacherjee Tejinder Pal Singh Brar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Lovely Professional University Phagwara Punjab India Department of Computer Applications CGC Landran Mohali India

An important part of preserving well-being is addressing pain, a complex human experience. In the medical field, accurate pain assessment is still difficult, especially when verbal communication is restricted. In response, by utilizing state-of-the-art technology, this research offers a strong and automated approach to pain identification. Feature extraction is done using Convolutional Neural Networks (CNNs), face expression analysis is done using deep learning techniques, and classification is done using Random Forest. When image preprocessing techniques are incorporated, data consistency and quality are improved, leading to consistent outcomes for a range of pain intensities. The system's ability to discriminate between various pain intensities, ranging from minor discomfort to excruciating anguish, is demonstrated by a carefully selected dataset. The accuracy and generalization abilities of the model are demonstrated by performance metrics such as precision, recall, and F1-Score. This study adds to the growing body of research on applications of computer vision in healthcare as well as the area of pain assessment. Promising developments in patient care, early intervention, and the comprehension of pain manifestations in many clinical contexts can be attributed to the computerized pain detection system.

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Face Mask detection Using CNN Model and Transfer Learning

Face Mask Detection Using CNN Model and Transfer Learning

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Gaurav Gulati Rajat Jain Harsh Anand Jha Moolchand Sharma Divya Gupta Umesh Gupta CSE Department MAIT Delhi India CSE Department Bennett University Greater Noida India

Our society has recently undergone a significant pandemic, presenting us with valuable opportunities for learning and growth. This research study presents a deep learning model designed to automatically recognize masks in public settings, addressing concerns related to the global COVID-19 pandemic and potential influenza outbreaks. The model employs convolutional neural networks (CNN) for the purpose of classifying individuals as either wearing masks or not, utilizing real-time facial image analysis. A diverse compilation of facial image datasets, including individuals of different ages, genders, and ethnic backgrounds, was curated, and appropriately labeled. By employing various techniques such as data augmentation, transfer learning, and fine-tuning, significant enhancements were achieved in the performance of the model. The incorporation of the proposed automated detection system into surveillance systems, public transit networks, and open areas presents significant potential for the effective implementation of mask-wearing regulations. This technology enhances public health by aiding in the management of infectious diseases such as COVID-19 and mitigating their associated risks.

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Realistic Condition-Based Anomaly detection of Multi-Faults in Rotating Machines

Realistic Condition-Based Anomaly Detection of Multi-Faults ...

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Sustainable Energy and Future Electric Transportation (SEFET), international conference on

作者： Rismaya Kumar Mishra Anurag Choudhary Shahab Fatima Amiya Ranjan Mohanty Bijiaya Ketan Panigrahi Centre for Automotive Research and Tribology Indian Institute of Technology Delhi New Delhi India School of Interdisciplinary Research Indian Institute of Technology Delhi New Delhi India Department of Mechanical Engineering Indian Institute of Technology Kharagpur West Bengal India Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India

Faulty machine components often get replaced instead of repaired in industries. The involved cost and downtime increase due to this problem, which can be mitigated with proper maintenance planning. This paper proposes a multi-fault diagnosis of two critical rotating components, motor and bearing at a system level. Motor current signatures of all possible muti-fault conditions were acquired at different speeds. The extracted signals were divided into small signal segments by implementing a sliding window with a fixed sample size. Continuous Wavelet Transform (CWT) was then employed on each extracted segment, and different datasets were formed for doing signal, sequence and image classification using Artificial Neural Network (ANN), 1D Convolutional Neural Network (CNN) and 2D CNN models, respectively. A comparative analysis of model performance was performed and found that 2D CNN outperformed other machine learning models. The Motor Current Signature Analysis (MCSA) could be used for system-level multi-fault anomaly detection using machine learning algorithms.

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