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Recent Advances in Analysis and Detection of Tuberculosis System in Chest X-Ray Using Artificial Intelligence (AI) Techniques: A Review

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Current Materials science 2023年第1期16卷 43-51页

作者： Suchitra, Shanmugam Ibrahim, S. Jafar Ali Sathya, Mariappan Sahini, Varsha Chakravarthy, N. Surya Kalyan Kumar, Vaneet Saruchi Department of Computer Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT SCOPE Vellore Institute of Technology Tamilnadu Vellore India AMC Engineering College Karnataka Bengaluru India Department of Computer Science Engineering CT Institute of Engineering Management and Technology Punjab Jalandhar India Data Science QIS College of Engineering and Technology Andhra Pradhesh Ongole India Department of Applied Sciences CT Institute of Engineering Management and Technology Shahpur Campus Punjab Jalandhar India Department of Biotechnology CT Institute of Pharmaceutical Sciences Shahpur Campus Punjab Jalandhar India

Mycobacterium tuberculosis causes tuberculosis (TB), a bacterial illness. Although germs are most typically found in the lungs, they can affect other sections of the body as well. Tuberculosis is one of the primary causes of mortality in both developed and developing nations, necessitating worldwide attention. Even though TB may be prevented in the majority of instances if discovered and treated early, the number of deaths caused by the disease is quite high. There has been a significant increase in interest and research activity in TB detection in recent years. The new advancement in the field of AI Technology may be able to assist them in overcoming these development gaps. computer-Aided Detection and Diagnosis (CADD) aids in the diagnosis of diseases by analysing symptoms and X-ray images of patients. Many solutions are currently be-ing developed to improve the effectiveness of TB diagnosis classification using AI and DL ap-proaches. Although a variety of TB detection techniques have been developed, there is no common-ly acknowledged method. The purpose of this study is to give a survey on Tuberculosis Detection. It also emphasises the difficulty and complexity of the Tuberculosis Detection System's design. © 2023 Bentham science Publishers.

关键词： computer vision

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Assessing the Effects of Blackhole Attacks on MANET Reliability and Security

Assessing the Effects of Blackhole Attacks on MANET Reliabil...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Fadhil, Saif Aamer Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad10011 Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Mobile Ad Hoc Networks (MANETs) are characterized by some important attributes, including infrastructure, mobile, and dynamic nature, which makes them have vast applications in different areas of computer networks. The black hole attack falls among the strongly criticized security threats within the framework of the MANETs. This deceptive behavior captures data packets from a source destined for the malicious node and then declines them when they are received. This paper comprehensively reviews prior studies done to analyze and mitigate black hole attacks in MANETs. Besides this, we will conduct a comparative analysis of the research above projects. The present paper extends prior work by actually performing the black hole assault simulations in a MANETs environment. This is done with the aid of the NS2 simulator and the Ad Hoc On-Demands Distance Vector (AODV) routing protocol. In this study, the black hole attack is analyzed and the consequences of the attack are assessed in terms of the system performance and security. We analyze crucial parameters like end-to-end latency, PDR, packet drop, and throughput to evaluate the consequences of this assault. From our results derived from the analysis of our simulations, we aim to present substantial points to do with the impacts of blackhole attacks on MANETs. In this way, our goal is to participate in the perpetually evolving efforts aimed at enhancing the security and reliability of such dynamic networks. © 2024 IEEE.

关键词： Mobile ad hoc networks

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An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks

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computers, Materials & Continua 2022年第2期70卷 2923-2936页

作者： Abdul Kadir Hamid Fahd N.Al-Wesabi Nadhem Nemri Ammar Zahary Imran Khan Department of Electrical&Computer Engineering University of SharjahSharjahUnited Arab Emirates Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversitySanaaYemen Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Electrical Engineering University of Engineering and TechnologyPeshawarPaki

With the emergence of 5G mobile multimedia services,end users’demand for high-speed,low-latency mobile communication network access is *** them,the device-to-device(D2D)communication is one of the considerable *** D2D communication,the data does not need to be relayed and forwarded by the base station,but under the control of the base station,a direct local link is allowed between two adjacent mobile *** flexible communicationmode reduces the processing bottlenecks and coverage blind spots of the base station,and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless *** of the important factors which affects the quality-of-service(QoS)of D2D communications is co-channel *** order to solve this problem of co-channel interference,this paper proposes a graph coloring based *** main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user *** proposed algorithm also provides simpler power *** heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is *** results show that the proposed algorithm effectively reduced the co-channel interference,power consumption and improved the system throughput as compared with existing algorithms.

关键词： 5G device-to-device communication wireless networks spectrum

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Smart-City-based Data Fusion Algorithm for Internet of Things

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computers, Materials & Continua 2022年第11期73卷 2407-2421页

作者： Jawad Khan Muhammad Amir Khan N.Z.Jhanjhi Mamoona Humayun Abdullah Alourani Department of Computer Science IIC University of TechnologyPhnom PenhCambodia Department of Computer Science COMSATS University IslamabadAbbottabad Campus22060Pakistan School of Computer Science and Engineering SCETaylor’s UniversitySubang JayaSelangorMalaysia Department of Information Systems College of Computer and Information SciencesJouf Universityal-JoufKSA Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Increasingly,Wireless Sensor Networks(WSNs)are contributing enormous amounts of *** the recent deployments of wireless sensor networks in Smart City infrastructures,significant volumes of data have been produced every day in several domains ranging from the environment to the healthcare system to *** wireless sensor nodes,a Smart City environment may now be shown for the benefit of *** Smart City delivers intelligent infrastructure and a stimulating environment to citizens of the Smart Society,including the elderly and ***,Quality of Service(QoS)and poor data performance are common problems in WSNs,caused by the data fusion method,where a small amount of bad data can significantly impact the total fusion *** our proposed research,a WSN multisensor data fusion technique employing fuzzy logic for event *** the new proposed Algorithm,sensor nodes will collect less repeated data,and redundant data will be used to increase the data’s overall *** network’s fusion delay problem is investigated,and a minimum fusion delay approach is provided based on the nodes’fusion waiting *** proposed algorithm performs well in fusion,according to the results of the *** a result of these discoveries,It is concluded that the algorithm describe here is effective and dependable instrument with a wide range of applications.

关键词： Internet of things data fusion wireless sensor network fuzzy inference system

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Enhancing Highway Safety with LSTM-based Vehicle Intention Prediction (HVIP)

Enhancing Highway Safety with LSTM-based Vehicle Intention P...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Amer, Lujina Elias, Catherine M. C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems Cairo Egypt German University in Cairo Faculty of Media Engineering and Technology Computer Science and Engineering Department Egypt

ISBN: (纸本)9798350367560

Predicting vehicles' motion on highways has become crucial for enhancing road safety and traffic flow. Deep learning, which reached exceptional results in various applications, is now the leading approach for vehicle motion prediction. This work presents a deep learning-based model using Long Short-Term Memory (LSTM) networks to classify vehicle intentions into lane-keeping, left lane-changing, and right lane-changing. Utilizing the PREVENTION dataset, which provides naturalistic driving data, a sequence of centre points, longitudinal distances, lateral distances and yaw angles of a vehicle has been extracted to train the model to predict lane changes effectively. The model consists of three LSTM layers, a dense output layer and a drop out added between layers to prevent overfitting. Experiments focusing on optimizing the model parameters, learning rate, sequence length, and batch size, were conducted to determine their impact on prediction effectiveness. The best-performing model showed significant results in lane changing prediction, achieving an accuracy of 84%, precision of 89%, and recall of 82%. Future work will aim to train the model on more complex features, to account for vehicle inter-dependencies and interactions, and test model on various highway scenarios for a more reliable system. © 2024 IEEE.

关键词： Vehicle safety

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Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving

Enhance Planning with Physics-informed Safety Controller for...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhou, Hang Liu, Haichao Lu, Hongliang Ma, Jun Ji, Yiding Robotics and Autonomous Systems Thrust Systems Hub Guangzhou China School of Engineering The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering SAR Hong Kong Hong Kong Intelligent Transportation Thrust Systems Hub Guangzhou China

ISBN: (纸本)9781665481090

Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction a few years ago and it is gradually being applied to motion planning tasks. Despite the performance of networks improve over time, DNN planners inherit the natural drawbacks of Deep Learning. Learning-based planners have limitations in achieving perfect accuracy on the training dataset and network performance can be affected by out-of-distribution problem. In this paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving fusion framework which combines physics-informed control for safety assurance. By incorporating Potential Field into Model Predictive Control, FusionAssurance is capable of navigating through scenarios that are not included in the training dataset and scenarios where neural network fail to generalize. The effectiveness of the approach is demonstrated by extensive experiments under various scenarios on the CARLA benchmark. © 2024 IEEE.

关键词： Deep neural networks

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AI-Powered Precision: Revolutionizing Skin Cancer Detection with Advanced Machine Learning 6

AI-Powered Precision: Revolutionizing Skin Cancer Detection ...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Abd-Elaziz, Mariam M. Elshafie, Basma M. Abdullah, Hagar Gerges, Karen Y. Ghanem, Ingy A. Fouad, Khaled M. El-Ghamry, Amir Moawad, Ibrahim F. El-Metwally, Sara New Mansoura University Faculty of Computer Science and Engineering Dakahlia Egypt Mansoura University New Mansoura University Faculty of Engineering Computer and Communication Program Faculty of Computer Science and Engineering Egypt Benha University Information Systems Faculty of Computers and Artificial Intelligence Egypt Mansoura University New Mansoura University Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Egypt School of Engineering and Computer Science University of Hertfordshire Gaf Egypt Ain Shams University Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning © 2024 IEEE.

关键词： Adversarial machine learning

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Advanced Spatio-Temporal Froth Analysis Using Smart Soft Sensors in Mineral Processing

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SN computer science 2024年第4期5卷 363页

作者： Bendaouia, Ahmed Abdelwahed, El Hassan Qassimi, Sara Boussetta, Abdelmalek Benzakour, Intissar Amar, Oumkeltoum Chekroun, Mohamed Hasidi, Oussama Lachihab, Oussama Computer Systems Engineering Laboratory (LISI) Cadi Ayyad University Marrakech Morocco SEIA Department Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco R & amp D and Engineering Center Reminex Managem Group Marrakech Morocco Computer and Systems Engineering Laboratory (L2IS) Cadi Ayyad University Marrakech Morocco

In the transformative field of mineral processing, the need for innovative technologies to overcome inherent difficulties and a critical shortage of high-quality data is an acute challenge. This study addresses these pressing issues by leveraging advanced spatio-temporal deep learning techniques, specifically Convolutional Long Short-Term Memory (ConvLSTM). Focused on the Zinc flotation circuit at CMG Managem Group in Morocco, our comprehensive approach encompasses meticulous data collection from a real-world industrial setting, rigorous spatial and temporal analyses, practical and accurate data augmentation, and the development of a ConvLSTM model for precise prediction of mineral grades. By capturing the temporal intricacies of froth behavior through video data, we gain insights into mineral concentration fluctuations, a critical aspect of traditional analysis. By offering a comprehensive solution that combines cutting-edge technology, meticulous data analysis, and practical augmentation methods, we aim to elevate not only mineral processing efficiency but also a broader spectrum of resource utilization and sustainable practices. This study offers a promising solution to elevate mineral processing efficiency and accuracy in the face of data limitations. It underscores the potential for transformative impacts on mineral processing operations, paving the way for more effective resource utilization and sustainable practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Data augmentation Industrial data analysis Machine learning Mineral processing

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Mageswaran, J. Uma Indumathi, G. Kalpana, A.V. Elangovan, G. Lalitha, S.D. Nalini, S. Sohanda, Mayank Kumar Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur DistTamil Nadu India Srm Institute Of Science And Technology Ramapuram India Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India R.M.K Engineering College Department Of Computer Science And Engineering India Srm Institute Of Science And Technolog Department Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350352689

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus. © 2024 IEEE.

关键词： Buses

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The EGY-DRiVeS Dataset: Illuminating the Dynamics of Egyptian Road Traffic Behaviors

The EGY-DRiVeS Dataset: Illuminating the Dynamics of Egyptia...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Saadawy, Zeyad H. Elfar, Mohamed A. Aly, Menna M. Adel, Momen Bahy, Mohamed A. Elias, Catherine M. C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems Cairo Egypt German University in Cairo Faculty of Media Engineering and Technology Computer Science and Engineering Department Egypt

ISBN: (纸本)9798350367560

This paper presents a comprehensive dataset of Egyptian roads integrated with chaotic scenarios captured by EGY-DRiVeS' lab golf car to aid in developing the autonomous driving experience. Using Velodyne 3D laser scanners, C922 Pro HD webcams, and iPhone 11 Pro cameras, a cumulative recording time of 3 hours and 48 minutes was documented. This included 1 hour and 15 minutes of LiDAR recordings, 1 hour and 45 minutes of camera recordings, and 48 minutes and 13 seconds of synced recordings. The dataset encompasses diverse scenarios and objects, ranging from urban environments with dynamic and static objects to rural highways. In-campus roads and intersections are also included, providing real-world scenarios such as vehicular and pedestrian behavior within campus premises. Each part of the dataset is annotated with 2D bounding boxes offering detailed information about vehicles, pedestrians and road objects. The provided data are calibrated, synchronized, and timestamped. Alongside the dataset, data format specifications, annotated data samples, and all supplementary recordings are provided upon request. © 2024 IEEE.

关键词： Network security

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