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A Novel Convolutional Neural Network Model for Malaria Cell Images Classification

computers, Materials & Continua 2022年第9期72卷 5889-5907页

作者： Esraa Hassan Mahmoud Y.Shams Noha A.Hikal Samir Elmougy Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

Infectious diseases are an imminent danger that faces human beings around the *** is considered a highly contagious *** diagnosis of various diseases,including malaria,was performed manually,but it required a lot of time and had some human ***,there is a need to investigate an efficient and fast automatic diagnosis *** deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image *** study proposed a Convolutional Neural Network(CNN)model to detect malaria automatically.A Malaria Convolutional Neural Network(MCNN)model is proposed in this work to classify the infected *** focuses on detecting infected cells,which aids in the computation of parasitemia,or infection *** proposed model achieved 0.9929,0.9848,0.9859,0.9924,0.0152,0.0141,0.0071,0.9890,0.9894,and 0.9780 in terms of specificity,sensitivity,precision,accuracy,F1-score,and Matthews Correlation Coefficient,respectively.A comparison was carried out between the proposed model and some recent works in the *** comparison demonstrates that the proposed model outperforms the compared works in terms of evaluation metrics.

关键词： Deep learning malaria dataset diagnostic medical approach

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Intelligent Street Light Management system using Solar Panels 3

Intelligent Street Light Management System using Solar Panel...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Venkatraman, Akila Ruchitha, Arutla Sellamuthu, Suseela Shalini, Dappu Reddy, Pulsani Dinesh Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Vellore Institute of Technology Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331518592

The conventional street lighting system is used during night time. It consumes more amount of power and there is major energy demand arises. The energy saving methods and lighting control arises to overcome the problem. The smart street light project aims to improve the energy efficiency and urban safety by using sensor- equipped street lights. It can adjust their brightness automatically based on levels of ambient light and the presence of vehicles and pedestrians. Additionally, the project incorporates the central management system implemented by using the ESP 32 module and BLYNK cloud, that allows for remote monitoring and control of street lights. The implementation of this technology can lead to significant cost saving, improves security for the cities and rise the overall quality of life for residents. Vehicle detection system is implemented to control the street lights when vehicle is detected. LED's (Light Emitting Diode) doesn't consume large amount of electricity. By the combination of both LED's and LDR's (Light Dependent Resistor) the intensity of light can be controlled and save more electricity. To make a step towards green energy, solar panels are used to power the street lights. The increased standard of living and growth of cities lead to the complex lighting system with automatic and intelligent control schemes. © 2024 IEEE.

关键词： Energy efficiency

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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Integrating Blockchain technology into Healthcare Through an Intelligent Computing Technique

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computers, Materials & Continua 2022年第2期70卷 2835-2860页

作者： Asif Irshad Khan Abdullah Saad Al-Malaise ALGhamdi Fawaz Jaber Alsolami Yoosef B.Abushark Abdulmohsen Almalawi Abdullah Marish Ali Alka Agrawal Rajeev Kumar Raees Ahmad Khan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia Department of Computer Applications Shri Ramswaroop Memorial UniversityBarabanki225003Uttar PradeshIndia

The blockchain technology plays a significant role in the present era of information *** the last few years,this technology has been used effectively in several *** has already made significant differences in human life,as well as is intended to have noticeable impact in many other domains in the forthcoming *** rapid growth in blockchain technology has created numerous new possibilities for use,especially for healthcare *** digital healthcare services require highly effective security methodologies that can integrate data security with the availablemanagement *** test and understand this goal of security management in Saudi Arabian perspective,the authors performed a numerical analysis and simulation through a multi criteria decision making approach in this *** authors adopted the fuzzy Analytical Hierarchy Process(AHP)for evaluating the effectiveness and then applied the fuzzy Technique forOrder of Preference by Similarity to Ideal Solution(TOPSIS)technique to simulate the validation of *** eliciting highly corroborative and conclusive results,the study referred to a real time project of diabetes patients’management application of Kingdom of Saudi Arabia(KSA).The results discussed in this paper are scientifically proven and validated through various analysis *** the present study can be a credible basis for other similar endeavours being undertaken in the domain of blockchain research.

关键词： Blockchain technology data management fuzzy logic AHP TOPSIS

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On the design of an AI-driven secure communication scheme for internet of medical things environment

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Digital Communications and Networks 2023年第5期9卷 1080-1089页

作者： Neha Garg Rajat Petwal Mohammad Wazid D.P.Singh Ashok Kumar Das Joel J.P.C.Rodrigues Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Center for Security Theory and Algorithmic ResearchInternational Institute of Information TechnologyHyderabad500032India College of Computer Science and Technology China University of Petroleum(East China)Qingdao266555China Instituto de Telecomunicaçoes 6201-001CovilhaPortugal

The Internet of Medical Things(IoMT)is a collection of smart healthcare devices,hardware infrastructure,and related software applications,that facilitate the connection of healthcare information technology system via the *** is also called IoT in healthcare,facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare *** other words,IoMT is an amalgam of medical devices and applications,which improves overall healthcare ***,this system is prone to securityand privacy-related attacks on healthcare ***,providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is *** mitigate this,we proposed a new Artificial-Intelligence envisioned secure communication scheme for *** discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to ***,we provide the security analysis of the proposed scheme to show its security against different possible ***,a comparative study of the proposed scheme with other similar schemes is *** results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs,and security and functionality ***,we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.

关键词： Internet of Medical Things(IoMT) Security Authentication and key agreement Artificial Intelligence(AI) Big data analytics

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IoT-Enabled Remote-Controlled Raft for Enhanced Water Quality Assessment 20

IoT-Enabled Remote-Controlled Raft for Enhanced Water Qualit...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Surasak, Thattapon Kitchat, Kotcharat Jiteurtragool, Nattagit King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Dept. Of Computer And Information Science Bangkok Thailand National Central University Dept. Of Computer Science And Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350382310

Water is vital for all life, serving as the foundation for plants and aquatic creatures and promoting abundance among all living beings. This study introduces an innovative approach by combining the Internet of Things (IoT) with advanced sensing techniques to assess water quality. The IoT-powered water quality testing raft has been developed to significantly enhance the monitoring capabilities. Equipped with sensors measuring key parameters such as turbidity, pH, and temperature, the raft provides a comprehensive understanding of water quality. Its GPS module enables real-time tracking and maintains location history, ensuring precise sampling. The collected data undergoes robust processing by a Raspberry Pi, ensuring integrity before being showcased on the Thingsboard. The system's uniqueness lies in its capacity to promptly issue alerts via LINE Notify upon detecting water quality abnormalities, ensuring timely intervention. This IoT-powered raft represents a paradigm shift in water quality assessment, characterised by its precision, remote capabilities, and user-focused approach. Practical applications have been demonstrated through real-world testing in a private lake in Cha-choengsao province, Thailand. The seamless integration of IoT into environmental monitoring marks a significant advancement in ensuring water quality and ecosystem health. © 2024 IEEE.

关键词： Internet of things

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Development of Deepfake Detection Techniques for Protecting Multimedia information using Deep Learning 3

Development of Deepfake Detection Techniques for Protecting ...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Siva Rama Lingham, N. Devakanth, J Jude Moses Anto Raj, Gowtham Gayathri, K. Janani, R. Dhanapal, R. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Acharya Institute of Technology Department of Computer Science and Engineering Bangalore India Bannari Amman Institute of Technolgy Department of Information Technology Erode India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

The development of proposed deepfake detection techniques plays a prominent role in the safeguarding the multimedia information. The various challenges are addressed using the aid of deep learning techniques. This involves hybrid optimization techniques such as particle swarm optimization and genetic algorithms. This helps to improve the accuracy and efficiency in the detection of deepfake. PSO helps in optimizing the weights and parameters of the neural network to obtain faster convergence. GA helps in obtaining potential solutions to obtain the robust deepfake detection model. The integration of deep learning with hybrid optimization involves the collection and preprocessing of the multimedia information and proceeds to an optimization algorithm. The manipulated content in the online platform are detected during the training process. The hybrid optimization tcehniques helps in obtaining model generability and achieving resilience in various attacks occurring in the deepfake generation. The integration of PSO and GA helps in the accurate detection of deepfake content through continuous training and evaluation. Thus the proposed system forms a safeguarding tool for multimedia content and helps to eliminate various risks. © 2024 IEEE.

关键词： Genetic algorithms

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High-Performance Video Retrieval by Combining Contrastive Language Image Pre-Training and Cross-Modal Attention

High-Performance Video Retrieval by Combining Contrastive La...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Ou, Jun-Jie Yang, Shih-Hsuan National Taipei University of Technology Taipei Taiwan National Taipei University of Technology Computer Science and Information Engineering Department Taipei Taiwan

ISBN: (纸本)9781665410205

Video retrieval is a task that locates the video segments that best match the query text. Although joint language-image models have recently achieved great success, training a video retrieval model typically requires a large data set and leads to long training time. This paper aims for a high-performance video retrieval method by combining pre-trained encoders and cross-modal attention. We first leverage the pre-trained CLIP (Contrastive Language-Image Pre-Training) ViT-B/16 encoders to extract the feature vectors for videos and texts. Next, the X-Pool, a recent cross-modal language-video attention model is incorporated into the system, where three layers of X-Pool are used to enhance retrieval accuracy. We also suggest using sparse sampling on video frames to further reduce training time. The effectiveness of the proposed method is evaluated using the MSRVTT and MSVD benchmark datasets. The proposed method achieves the best retrieval performance (recall@k, median rank, and mean rank) among the state-of-the-art methods with comparable computational complexity. © 2024 IEEE.

关键词： Query languages

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Generative AI-Powered Aerial Access Networks: Recent Studies and Future Outlook 15

Generative AI-Powered Aerial Access Networks: Recent Studies...

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15th International Conference on information and Communication technology Convergence, ICTC 2024

作者： Nguyen, Tri-Hai Nguyen, Truong Khang Bao, Vo Nguyen Quoc Park, Heejae Park, Laihyuk School of Technology Van Lang University Faculty of Information Technology Ho Chi Minh City70000 Viet Nam Seoul National University of Science and Technology Department of Computer Science and Engineering Seoul01811 Korea Republic of

ISBN: (纸本)9798350364637

Aerial access networks (AANs), comprising un-manned aerial vehicles (UAV), high-altitude platforms (HAPs), and low-earth orbit (LEO) satellites, are rapidly emerging as a critical component of next-generation communication systems. Their dynamic nature, heterogeneous composition, and vast coverage area pose significant challenges for network management and optimization. Generative artificial intelligence (GenAI), renowned for its ability to generate new content, offers a promising solution to these challenges. In this paper, we explore the applications of GenAI in AANs in recent studies. We also identify key challenges and outline promising research directions. © 2024 IEEE.

关键词： Generative adversarial networks

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Detection of Low Rate DDoS with Adversarial Attacks by Enhanced Generative Adversarial Networks

Detection of Low Rate DDoS with Adversarial Attacks by Enhan...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Hsiao, Tzu-Yi Yang, Shih-Hsuan National Taipei University of Technology Taipei Taiwan National Taipei University of Technology Computer Science and Information Engineering Department Taipei Taiwan

ISBN: (纸本)9781665410205

The Internet of Things is vulnerable to cybersecurity attacks because it is typically designed with minimal security considerations. The Low-Rate Distributed Denial-of-Service (LR-DDoS) attacks evade detection by injecting a small portion of malicious packets that persistently lurk within the IoT network. Recently, adversarial LR-DDoS attacks further complicate machine learning based security protection. In this study, an improved detection algorithm based on a generative adversarial network is proposed against LR-DDoS. To learn the temporal characteristics, LR-DDoS time-series features are taken as a part of the generator's input and Long Short-Term Memory modules are incorporated. Two discriminators with adaptive weighting are employed, one for conventional LR-DDoS attacks and the other for the adversarial attacks, to generate plausible LR-DDoS samples. The generated packets are used to train the detection algorithm of an Intrusion Detection system. Simulation results show that the proposed method achieves an accuracy of 94% in detecting adversarial LR-DDoS packets, which outperforms the state-of-the-art methods by at least 10%. © 2024 IEEE.

关键词： Generative adversarial networks

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