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Animal Intrusion detection system using SIFT Features and Transfer Learning with MobileNetV2

Animal Intrusion Detection System using SIFT Features and Tr...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： E. Chandralekha Muzammil Ali A Ritesh V Muthu Kumar Srinivasan CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India AI&DS Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India

Agriculture’s impact on India’s economy is substantial, offering employment for a significant part of the population and contributing to livelihoods. It plays a crucial role in ensuring food security, providing industrial raw materials, and supporting exports. Yet, agricultural challenges arise from animal-related issues like crop damage, livestock attacks, disease transmission, and farming infrastructure disruptions. These problems hinder productivity, necessitating sustainable farming strategies. Deep learning advancements enhance animal intrusion detection by autonomously analyzing data patterns for accurate identification of wildlife, thus improving farm security. A unique intrusion detection model is introduced, utilizing architectural augmentation and transfer learning. The model, built on MobileNetV2 architecture, successfully detects intruders across diverse image categories, bolstered by a data generator and custom layers. Effective optimization techniques are employed in model construction, and the trained model is stored for future intrusion detection use.

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Deep Learning based Dynamic Object Probability with Long Short-Term Memory for Drowsiness detection

Deep Learning based Dynamic Object Probability with Long Sho...

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

作者： K Sudheer Kumar K. Radha Mohammed Ayad Alkhafaji Hassan M. Al-Jawahry Zainab abed Almoussawi School of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of AI & DS Vivekananda College of Technology for women Namakkal India National University of Science and Technology Dhi Qar Iraq The Islamic University Najaf Iraq College of Islamic Science Ahl Al Bayt University karbala Iraq

Driver Drowsiness detection (DDD) is crucial for preventing road accidents and serves as a pivotal element for shift from semi-autonomous to fully autonomous driving. Various Deep Learning (DL) models leveraging video sequences, offer sophisticated motion sensing capabilities. In this paper, the potential of identifying the regions of the eyes and mouth to capture yawning behavior, serving as a reliable indicator of drowsiness. The proposed Dynamic Object Probability with Long Short-Term Memory (DOP-LSTM) technique is employed to specifically detect the driver within the video sequence. Subsequently, the Dense Scale-Invariant Feature Transform (D-SIFT) technique is utilized to extract the regions of the driver's eyes and mouth. The features extracted from both the eyes and mouth are fused using Multi-Scale Feature Fusion (MSFF) techniques to determine the driver's state. LSTM is implemented for classification and detection and it demonstrates superior performance, achieving Precision of 99.69%, F-Measure of 99.02%, Recall of 98.30%, and Accuracy of 98.81%, when compared to existing models such as Inception V3-lstm, Hybrid-DDD, and Multi-scale Convolutional Neural Network-Flamingo Search Algorithm (MCNN-FSA).

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Research on detection and Protection technology of DC Residual Current

Research on Detection and Protection Technology of DC Residu...

引用

Electrical Engineering and Control Science (IC2ECS), international conference on

作者： Ruixiong Yang Yong Chen Kai Xu Shuping Gao Guangdong Power Grid Co. Ltd. DC Distribution Power Research Center Zhuhai Guangdong China College of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an Shaanxi China Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security Xi'an Shaanxi China

At present, the waveforms generated in the power system are more and more complex, not only a single sinusoidal current, the pulsating DC component and the smooth DC component are often generated when the fault occurs. But the traditional AC residual current protector can only detect the AC residual current, and can not protect this complex waveform signal. In this paper, the pulsating DC residual current is studied, and a residual current detection method based on Fourier transform is proposed. It can effectively identify the residual current waveform, and set threshold according to different waveforms. It can accurately detect the residual current, but its anti-interference ability is poor. In order to solve the problem of anti-interference, a residual current detection method based on Kalman filter is proposed. The predicted value of Kalman filter is obtained after the generated residual current is filtered, and the threshold of the action of the system protection device can be set according to the predicted value. Simulation results show that this method can effectively improve the anti-interference ability of the system.

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A Novel Attitude Feature Extraction Method for Multi-IMU Based Fall detection system

A Novel Attitude Feature Extraction Method for Multi-IMU Bas...

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

作者： Xiaoqing Chai Boon-Giin Lee Matthew Pike Renjie Wu Xian Wu School of Computer Science Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute University of Nottingham Ningbo China Ningbo China Tianjin Fire Science and Technology Research Institute of MEM Tianjin China

Falling has become the leading cause of non-fatal and preventable injuries. The majority of existing fall detection systems (FDSs) rely on motion sensors. However, most of the applied feature extraction methods result in longer processing time and higher computation. This study proposes a novel attitude feature extraction (AFE) method that extract five feature sets from 54 raw measurements obtained from nine inertial measurement units (IMUs) placed on the firefighter’s protective clothing. Our results indicate that the metrics of attitude feature extraction (AFEM) method outperforms the existing metrics of raw feature extraction (RFEM) method in the fall detection. In addition, the proposed method reduces the algorithm processing time and computations significantly. This enables on-device fall detection classification on constrained processing architectures.

关键词： Measurement units Protective clothing Inertial navigation Computer architecture Computer applications Feature extraction Power electronics

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Using Artificial intelligence to Integrate Machine Learning, Fuzzy Logic, and The IOT as A Cybersecurity system

Using Artificial Intelligence to Integrate Machine Learning,...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Vipul Bansal Ankit Bhardwaj Jitendra Singh Devvret Verma Mohit Tiwari Someshwar Siddi Information Technology Kurukshetra University Department of Computer Science and Engineering University of Connecticut Storrs Connecticut Law College Dehradun Uttaranchal University Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi St Martin's Engineering College Dhulapally Secunderabad Telangana India

Concerns about cybersecurity have increased along with the Internet of Things' (IoT) exponential growth in recent years. Artificial intelligence (AI), which is used to create sophisticated algorithms to safeguard networks and systems, including Internet of Things technologies, is at the forefront of cybersecurity. However, hackers have discovered methods to take advantage of his AI, and they have begun leveraging it against their adversaries to launch cyber security assaults. This review study investigates the connections between IoT, AI, and attacks utilizing and against AI, synthesizes data from several previous studies and research publications, and explores these areas. It is intended to be a comprehensive presentation and summary of the relevant literature.

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Role of Artificial intelligence in Input Tax Credit Reconciliation

Role of Artificial Intelligence in Input Tax Credit Reconcil...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Rakesh Kumar Rupa Khanna Malhotra Shweta Pandey Anita Gehlot Indu Gautam Shailesh Chamola Uttaranchal institute of Management Uttaranchal University Dehradun India Graphic era University Dehrdun India Deprtment of Commerce Graphic era Deemed to be University Dehrdun India LCD Division of Research & Innovation Uttaranchal University Dehradun India UIT Division of Research & Innovation Uttaranchal University Dehradun India Gurukul Kangri vishwavidyalaya Uttaranchal University Dehradun India

The Goods and Services Tax has significantly changed the economic structure of India and replaced the previous indirect taxation system. With the intention of reducing tax evasion, this new law simplifies the implementation procedure and makes tax credit technology more readily available. Prior to this time, there were several taxes, which encouraged tax evasion, increased compliance with government regulations, and burdened consumers. New statistical and analytical tools that offer speed and simplicity are now available to tax professionals. AI can help with fraud identification, which will help with governmental oversight and control. Taxes are a significant source of public finance in a growing nation like India. Taxpayers view about using artificial intelligence to power tax assessment. This study emphasizes how AI can simplify input tax credit reconciliation and future scope.

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Intrusions detection system Using Machine Learning Algorithms

Intrusions Detection System Using Machine Learning Algorithm...

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Emerging Smart Technologies and Applications (eSmarTA), international conference on

作者： Hadeel Ahmed Abdullah Abdulwali Maram Hussein Saleh Al-Humaidi Hadeel Zaid Abdullah Al-Asri Abdulkareem Faisal Mansour Al-saidi Ahmed Ali Al-Himiary Computer Networks Engineering University of Modern Sciences Sana'a Yemen

With the development of information technology, networks and the Internet of Things, the number of people using the Internet has increased, as well as the number of internet-connected devices, resulting in important and sensitive data on the Internet, making it vulnerable to penetration. There must be an intrusion detection system, which is one of the safe ways to reduce cyberattacks that the network can be exposed to. The goal of the project is to choose the best algorithm from machine learning algorithms to detect infiltration. In order to achieve the goal, we searched for the latest dataset available on the Internet that meets the need to apply intrusion detection systems and has also not been very largely researched. We got an X-IIOTID data set that applies to the Internet of Industrial Things. It was studied and analyzed using python programming language. It was introduced on several algorithms for machine learning in order to train them to detect and reduce infiltration, and from these algorithms, the best algorithm was (Random Forest). It was the highest accuracy with an accuracy rate of 99.9963% and also the least in terms of false alarms where it reached almost zero compared to other algorithms. It was therefore selected as the best algorithm to be used when designing intrusion detection systems in classifying.

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Application of Deep Learning Based Longitudinal Tear detection in Intelligent Main Transportation

Application of Deep Learning Based Longitudinal Tear Detecti...

引用

Computer Science, Electronic Information Engineering and Intelligent Control technology (CEI), IEEE international conference on

作者： Zhiqiang Yu Tiandi (Changzhou) Automation Co. Ltd. Changzhou China Middling Coal Science and Engineering Group Changzhou Research Institute Co. Ltd. Changzhou China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

The current traditional device for detecting longitudinal tearing of belts has a low level of intelligence, and the triggering mechanism is inherently prone to missed and false detections. This paper designs a longitudinal tear detection system based on deep learning, which takes YOLOv5s model as the deep learning model and low power consumption edge computing module as the carrier. The industrial intrinsic safety camera is used to obtain the image, which is transferred to the edge computing module for tear feature detection. The longitudinal tear length is calculated by fusing the belt speed sensor data, and only the external detection results are finally output. When the detected longitudinal tear length exceeds a certain threshold, the belt conveyor collaborative controller will control the sound and light alarm to give an alarm, and at the same time issue a shutdown command to the specific single controller. In addition, the edge computing module uploads the video stream and alarm data after image processing to the control platform for reference. This system deepens the intelligence level of longitudinal tear detection for the main conveyor belt in coal mines, ensures the safety of the coal production process, improves production efficiency, and reduces economic losses.

关键词： Deep learning YOLO Computational modeling Collaboration Production Streaming media Belts

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A Comparative Study in the Design of an ANN Based Controller

A Comparative Study in the Design of an ANN Based Controller

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Moazzam Haidari D. Sengeni Mamata Choudhury Hilda Jerlin C M Kallakunta Ravi Kumar Vijay Singh Department of Electrical Engineering Saharsa College of Engineering Saharsa Bihar India Department of Electronics and Communication Engineering CK College of Engineering and Technology Cuddalore Tamil Nadu India Department of Computer Science and Engineering PSCMR College of Engineering and Technology Vijayawada Andhra Pradesh India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The power system stabilizer is based on artificial neural networks and is designed to cope with changing operational circumstances in real-world applications. The parameters of a conventional or supplementary power system stabilizer are generated with changing operating circumstances using a Multi-layer feed-forward with back propagation (MLP) Artificial neural network. As a result, the power system stabilizer's utility becomes more stable. Many researchers included this artificial neural network in their designs; however, information on network creation functions, hidden layer size, non-linear (active) functions employed in output and hidden layers, and Back Propagation (BP) training functions were not studied in detail. In this paper a study is carried out on these aspects to explore the effect of these aspects on the functionality of power system stabilizers.

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Machine Learning-based Prediction of Parkinson’s Disease: A Comparative Analysis of Algorithms

Machine Learning-based Prediction of Parkinson’s Disease: A...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： P. G. Om Prakash Balineni Nehan Sai Kumar Reddy Seedella Sai Mohankrishna Lohith Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

Diagnosing Parkinson’s disease (PD) is an important medical diagnosis that calls for the application of cutting-edge methods such as machine learning algorithms. This study explores the application of cutting-edge machine learning techniques for the early detection of Parkinson’s disease using a diverse dataset that includes patient and clinical record data. The study calls for rigorous feature selection, model training, data preprocessing, and evaluation because it uses a complicated and nuanced dataset. Using rigorous cross-validation and extensive experimentation, this study assesses the sensitivity, specificity, and accuracy of these machine learning algorithms in identifying subtle patterns suggestive of symptoms of Parkinson’s disease. Additionally, traditional diagnostic methods are thoroughly scrutinized for comparative analysis, augmenting the scope of the evaluation *** research findings shed important light on how well machine learning algorithms identify Parkinson’s disease, which will significantly improve medical diagnostic methods. This work highlights the critical role that a range of sophisticated machine learning models and meticulous dataset curation play in enhancing the precision and robustness of Parkinson’s disease detection systems.

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