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IoT-Enabled CNN for Real-Time Forest Fire detection: Fusing technology to Safeguard Nature 3

IoT-Enabled CNN for Real-Time Forest Fire Detection: Fusing ...

引用

3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Sharma, Ishu Neema, Shantanu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Syntelli Solutions Inc. Charlotte United States

ISBN: (纸本)9798350343632

Effective fire management and prevention strategies are urgently needed because forest fires pose a serious hazard to both ecosystems and human settlements. This research offers a novel framework for detecting forest fires that makes use of Internet of Things (IoT) devices and a Convolutional Neural Network (CNN) model. The framework uses IoT sensors to continuously track environmental variables and collect crucial information for accurate fire detection. The CNN model then carefully processes this data, allowing the system to send out real-time fire detection alarms. The model's strong learning and generalization skills are confirmed by the system's performance, which has been thoroughly evaluated via extended training and validation. This has led to noticeably high AUC values for both training and validation datasets. Future directions that include real-world implementation, scalability, and community participation are attentively considered, as are ethical and environmental issues related to the system's deployment. This framework for detecting forest fires offers a potential route for early fire detection and makes a substantial contribution to the improvement of forest fire management and prevention tactics. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Data Privacy Environmental Monitoring Forest Fire Prediction Internet of Things (IoT)

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Analysis of Machine Learning for Recognizing Hand Gestures 3

Analysis of Machine Learning for Recognizing Hand Gestures

引用

3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Kumar Sharma, Anand Kumar, A Suresh Nagpal, Amandeep Singh, Rajesh Reddy, DasariVeera Chadha, Anupama Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Department of Information Technology Hyderabad India Graphic Era Deemed to Be University Department of Cse India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India ManavRachna International Institute of Research and Studies Department of Computer Applications Haryana Faridabad India

ISBN: (纸本)9798350399264

The medical field and other research areas fields heavily rely on artificial intelligence (AI) and machine learning (ML). Hand gesture recognition (HGR), which is a straightforward approach for interacting with machines, has drawn the attention of numerous researchers in the context of AI. Utilizing ML technique, HGR is carried out. Both the image technique and the design approach are parts of it. In the image technique, the hand picture is rebuilt utilizing the attributes of the images. However, when using the model-based approach, the image is recreated using many models, including volumetric, geometric, and others. The key difficulty in gesture control continues to be the ML individual's complexity and preparation time. Its accuracy is used to assess the HGR accuracy. Its precision is used to assess the HGR overall system performance. The many techniques and algorithms utilized in gesture control are covered in detail inside this work. © 2023 IEEE.

关键词： Machine learning

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Covert Channel detection and Generation Techniques: A Survey 3

Covert Channel Detection and Generation Techniques: A Survey

引用

3rd international conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Al-Khulaidi, Nesmah A. Zahary, Ammar T. Hazaa, Muneer A.S Nasser, Adel A. Yemen Academy for Graduate Studies Department of Computer Networks Sana'a Yemen University of Science and Technology Faculty of Computing and It Department of Computer Science Sana'a Yemen Thamar University Faculty of Computer and Information System Department of Computer Science Thamar Yemen Sa'adah University Faculty of Sciences Department of Information Systems and Computer Science Sa'adah Yemen

ISBN: (纸本)9798350305333

The use of covert communications has become more widespread recently as a solution to the information security issue. Information security can be partially solved by the discovery and creation of covert routes. Covert channel use is still in its early phases, even though information security concerns are growing as a result of quick technology advancements. As a result, several problems and concerns need to be resolved, and the future plan is foggy. This paper seeks to give a thorough comparison of earlier and more recent attempts in this field. It offers a survey of the literature on the drawbacks and gaps in conventional detection techniques, emphasizing the most recent techniques for discovering and creating these covert channels while carefully considering machine learning and deep learning-based detection techniques. Additionally, it broadens the topic to cover the crucial function that these channels play in technology. Timing channels, storage channels, and hybrid channels are just a few of the ways covert channels may be utilized to transfer data. They can be used to get around security measures like intrusion detection systems and firewalls. Additionally, they may be used to steal confidential data from a system, including passwords, credit card numbers, and intellectual property. system security is seriously threatened by covert channels, and more study on covert channel production and detection is required to stay up with the changing threat environment. © 2023 IEEE.

关键词： Intrusion detection

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Hybrid Optimization Algorithm to Mitigate Phishing URL Attacks In Smart Cities 3

Hybrid Optimization Algorithm to Mitigate Phishing URL Attac...

引用

3rd international conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Sujatha, G. Ayyannan, Manimuthu Priya, S. Gayathri Arun, V. Arularasan, A.N. Kumar, M Jogendra Sri Venkateswara College of Engineering Department of Electronics and Communication Andhra Pradesh Tirupati India AI/ML and Data Expert San FranciscoCA United States R. M. D. Engineering College Department of Electronics and Communication Tamil Nadu Kavaraipettai India Madanapalle Institute of Technology Department of Computer Science &engineering Andhra Pradesh Madanapalle India Panimalar Engineering College Department of Artificial Intelligence &data Science Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350336238

To improve the detection of phishing sites, this proposal introduces feature weights for intelligent phishing site detection based on hybrid bio-inspired algorithms. The proposed approach uses Gray Wolf Optimization (GWO) and Firefly Algorithm (FF), which examines a wide range of website attributes, to more precisely identify phishing sites. Then employs Artificial Neural Network (ANN) to classify different website elements according to the importance of each component in differentiating between legitimate and phishing websites using bioinspired-based recommended site feature weights. According to experimental findings, the suggested hybrid bioinspired-based feature weighting greatly improved classification accuracy, true positive (TPR), and negative rates (TNR), as well as precision and F1 score. Phishing is an online crime that entails the gathering of private information like passwords, account numbers, and credit card numbers. Attackers use alluring URLs to entice phony website visitors. Recently, Artificial intelligence-based phishing detection has seen some success, and in this study, ANN was used to detect phishing. This ANN classifier may make phishing websites simpler to spot. However, it has been shown that the effectiveness of detection can be increased by applying a genetic algorithm to improve feature selection. © 2023 IEEE.

关键词： Websites

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Vision-based Real Time Bangla Sign Language Recognition system Using MediaPipe Holistic and LSTM 3

Vision-based Real Time Bangla Sign Language Recognition Syst...

引用

3rd IEEE international conference on Computational intelligence and Knowledge Economy, ICCIKE 2023

作者： Foysol, Md. Walid Sajal, Sk. Estiaque Ahmed Alam, Mohammad Jahangir Daffodil International University Dept. of CSE Dhaka Bangladesh

ISBN: (纸本)9798350338263

The Bangla Sign Language detection system converts the Bangla sign language into text so that deaf-mute people can communicate with ordinary people. Deaf-mute people are quite detached from society because normal individuals and deaf-mute individuals have a communication gap. On the strength of technological welfare, it is now possible to capture any deaf-mute person's gesture, and with the help of machine learning, it can be converted into text. In this research, we adopt a development model for recognizing gestures that accommodates MediaPipe for extracting hands and posing landmarks and long short-Term memory (LSTM) to train and recognize the gesture. This will convert Bangla sign language gestures into readable text. The requirements analysis served as the foundation for our proposed model, which will be carried out in four stages: collecting data and processing it, training and testing the chosen neural network, and finally, real-Time testing. A gesture model is taught to recognize gestures with the help of a self-created dataset of Bangla sign language. The trained model successfully identifies the gesture, and the text equivalent of the gesture is displayed on screen. The purpose of this model is to help create a medium to communicate between normal people and the hearing impaired. © 2023 IEEE.

关键词： Long short-term memory

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Research on Static Reverse Analysis technology for Security detection of Power Industrial Control Software 3

Research on Static Reverse Analysis Technology for Security ...

引用

3rd international conference on Intelligent Power and systems, ICIPS 2023

作者： Chen, Jinshan Yu, Sihang Xie, Xuanxuan Cheng, Yu Tang, Zhijun Deng, Chaoping Li, June State Grid Fujian Electric Power Research Institute Fujian Fuzhou350000 China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Hubei Wuhan430072 China

ISBN: (纸本)9798350328691

In the power industrial control software, defects of software and possible pre-implanted backdoors and malicious codes are important factors that threaten the security of the power industrial control system. The complete and accurate extraction of function call relationship is the basis of software security detection based on function call graph. Therefore, based on analyzing function call sequence of abnormal behavior of electric power industrial control software, a method of software static reverse analysis is proposed. The method identified the function call relationship optimized by the compiler to be a jmp instruction by searching the call/jmp instructions. In order to extract a simplified key function call relationship, a key function list was obtained by performing keyword fuzzy matching. Experiments show that this method can accurately and comprehensively obtain the function call relation, the simplified key function call relationship can improve the efficiency of reverse analysis and provide a better support for software defects, backdoors and malicious codes detection. ©2023 IEEE.

关键词： Industrial research

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Crowd detection system & Prediction Analysis 3

Crowd Detection System & Prediction Analysis

引用

3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Mane, Abhiraj Maurya, Vivek Mendonca, Crezel Tripathy, Amiya Kumar Don Bosco Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

The problem of population growth is an ongoing issue, particularly in countries such as India where it is rapidly increasing. This rise in population has led to various issues such as high unemployment, limited resources, and overcrowding, particularly in urban areas. One specific case of overcrowding can be seen in Mumbai's Suburban Railways, which carry a large number of passengers each day. Most of the city's population relies on these trains to travel to work, but peak travel hours can be particularly difficult due to overcrowding. This has resulted in several accidents in recent years, and the frequency of such incidents is on the rise. Therefore, the focus of this research is to develop a system that can address this problem. The proposed approach involves collecting GPS data from individuals entering the railway platform, which is then used to monitor and predict crowd density, as well as provide warnings to commuters. © 2023 IEEE.

关键词： Population statistics

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Blind Navigation system Based on NS-YOLOV5 3

Blind Navigation System Based on NS-YOLOV5

引用

3rd international conference on Intelligent Communications and Computing, ICC 2023

作者： Cong, Geng Yang, Liu Liu, Hongyan Chao, Han Niu, Chenzhu Zaozhuang University Artificial Intelligence Institute ZaoZhuang China

ISBN: (纸本)9798350308327

In response to the challenges faced by visually impai red individuals when navigating their surroundings, this paper pr esents an enhanced machine vision object detection algorithm kno wn as NS-YOLO, which builds upon YOLOV5s. NS-YOLO aims to achieve efficient and precise detection of complex scenarios in p edestrian walkways. Given the real-time demands of this applicati on, several enhancements have been made to YOLOV5s. Firstly, t he backbone feature extraction network of YOLOV5s has been re placed with MobileNetV3, leading to a significant reduction in the number of parameters and computational load through the use o f depthwise separable convolutions. Secondly, the NAM attention mechanism has been introduced to improve the model's computat ional efficiency. Lastly, a weighted attention mechanism (SimAM) has been incorporated before the prediction stage to dynamically adjust the model's focus, further improving feature representatio n capabilities in complex scenarios. Experimental results demonst rate that the NS-YOLO model enhances mAP50 by 4.5 compared to the original YOLOV5s model, all while achieving a detection sp eed of 90.88 FPS. NS-YOLO elevates detection accuracy while ma intaining detection speed, making it significantly superior to com parative algorithm models and meeting practical application requ irements, thus possessing substantial practical value. © 2023 IEEE.

关键词： Object detection

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Intrusions detection system Using Machine Learning Algorithms 3

Intrusions Detection System Using Machine Learning Algorithm...

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3rd international conference on Emerging Smart Technologies and Applications, eSmarTA 2023

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

ISBN: (纸本)9798350305333

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. © 2023 IEEE.

关键词： Internet of things

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An Optimized Approach for Monkey-Pox Prediction with Neural Networks 3

An Optimized Approach for Monkey-Pox Prediction with Neural ...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Kiran, Kalvacherla Gudimilla, Pallavi Ravi, Silumula Mahender, Nagurla Mohmmad, Sallauddin SR University School of Computer Science and Artificaial Intelligence Telangana Warangal India Vaagdevi College of Engineering Telangana Warangal India Kakathiya Institute of Technology &science Telangana Warangal India

ISBN: (纸本)9798350343632

In the quest to combat the rare viral illness of monkeypox, particularly prevalent in Central and West Africa, our research has delved into refining detection methods using Artificial Neural Networks (ANN). Given the severity of the disease and its potential for community spread, early detection is imperative. Previous efforts to develop AI-based detection systems have aimed at increasing Accuracy, yet consistency has yet to be achieved due to the challenge of imbalanced models. In response, our study introduces an optimized ANN model for monkeypox detection. Through meticulous fine-tuning of hyperparameters, we identified optimal settings and evaluated the model's performance using Precision, Recall, F1 score, and Accuracy. Addressing the imbalance issue, we harnessed ensemble techniques, combining the most effective deep learning models through a majority voting approach based on their probabilistic outputs. Conducted on a publicly accessible dataset, our experiments yielded promising results, with average scores of 85.74% for Precision, 85.47% for Recall, 85.40% for F1-score, and 87.13% for Accuracy, showcasing the potential of our proposed ensemble methodology to enhance the detection of the monkeypox virus significantly. © 2023 IEEE.

关键词： Artificial neural network Deep Learning Evaluation Metrics Monkeypox Neural Networks

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