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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Yang, Bowen Yu, Yao Li, Jianqi Xu, Tongze Deng, Der-Jiunn Chin, Hui-Hsin Li, Yonghui Northeastern University School of Computer Science and Engineering Shenyang China National Changhua University of Education Department of Computer Science and Information Engineering China Overseas Chinese University Department of Information Technology China University of Sydney School of Electrical and Information Engineering Sydney Australia Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (纸本)9798350345384

The recently proposed concept of Age of information (AoI) measures the freshness of the sensor data sampled by remote Internet of Things (IoT) devices, which is an important indicator for the smart city. Unmanned Aerial Vehicles (UAV) communication can significantly reduce the sensor data's AoI due to its high maneuverability. However, the UAV's high energy consumption remains a challenging issue. In this paper, we propose a Reconfigurable Intelligent Surface (RIS) assisted UAV data collection framework to minimize UAV energy consumption. By adjusting the phase of the RIS, the UAV can collect more sensor data simultaneously. A RIS phase alignment method is introduced to reduce the complexity of RIS phase solving. The IoT devices are clustered to discretize UAV trajectory. The optimal clustering scheme to maximize the AoI suppression efficiency is found by adjusting the RIS phase alignment points continuously. Therefore, the AoI requirement can be achieved with a shorter UAV flight distance. Finally, the UAV trajectory is solved by the Deep Q network (DQN) algorithm to guarantee the long-term AoI requirement. Simulation results validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Maneuverability

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Cervical Cancer Classification using Multi-Scale, Multiple-Attention-based Deep Learning Approach

Cervical Cancer Classification using Multi-Scale, Multiple-A...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Maurya, Ritesh Deshwal, Suvidha Rajput, Lucky Gopalakrishnan, T. Krishna, Raguru Jaya Amity Centre for Artificial Intelligence Amity University Noida India Amity School of Engineering & Technology Amity University Noida India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9798350374933

Cervical cancer is a widely acknowledged health concern, especially in the areas lacking appropriate medical treatment. The early diagnosis and the explicit categorization of cervical lesions may bring about effective therapy and restrict the expansion of the disease. The novel approach proposed in this work employs a multi-scale, multi-attention based MobileNetV2 network for the categorisation of cervical cancer. The proposed model improves the efficiency of MobileNetV2, a lightweight convolutional neural network (CNN), by incorporating multi-scale and multi-attention techniques to improve feature extraction capabilities and categorisation accuracy. By incorporating multi-scale component, the suggested model can effectively detect features at different resolutions. Moreover, the integration of multi-attention mechanism allows the model to dynamically highlight salient features over multiple scales. The proposed method has been evaluated using single cell conventional Pap Smear images, exhibiting improved performance compared to existing approaches in terms of accuracy. The results suggest that the Multi-Scale, Multiple-Attention MobileNetV2-based network proves to be a useful tool for cervical cancer categorisation, offering potential interests for clinical diagnosis with an accuracy of 92.4. © 2024 IEEE.

关键词： cervical cancer categorisation deep learning MobileNetV2 multi-attention multi-scale transfer learning

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Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

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Multimedia Tools and Applications 2025年第16期84卷 15735-15761页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

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Classification of Pavements into Bleeding and Non-Bleeding using Deep Architectures: A Comparative Study 3

Classification of Pavements into Bleeding and Non-Bleeding u...

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3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Sowmiyalakshmi, G. Jegadish, Subiksha Kothai, R. Srinivasa Murthy, Y.V. Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Dept of Information Technology Manipal India

ISBN: (纸本)9798350370867

Bleeding or flushing refers to a reflective, dark layer of asphalt on the road caused by the upward movement of asphalt within the pavement. This issue is often caused by excess asphalt in the asphalt concrete, high temperatures, low air void content, and poor asphalt quality. The presence of bleeding poses safety risks as it creates an excessively smooth surface, lacking the necessary texture to prevent hydroplaning. This study presents a comparative analysis of various image classification models for the detection of bleeding and non-bleeding roads. The objective is to develop an automated system that accurately identifies road conditions related to bleeding, which is crucial for effective road maintenance and safety measures. In this regard, four pre-trained deep learning models such as VGG16, InceptionV3, EfficientNet, and ResNet50, to classify road images. The advantages of this work lie in the exploration of multiple state-of-the-art models, allowing for a comprehensive evaluation of their performance. The features and approach of the proposed work involve training the models on a labeled dataset of bleeding and non-bleeding road images. Each model is fine-tuned using transfer learning techniques, leveraging the pre-trained weights from large-scale image datasets. This study highlights several notable contributions about pre-trained deep learning models. In addition, the study identifies the strengths and weaknesses of each model, helping to guide future research and development efforts. The findings of this comparative study contribute to the field of road condition analysis and maintenance by providing valuable insights into the performance and limitations of different image classification models. The better performance is identified with InceptionV3 with an accuracy of 70.00% and least performance is given by ResNet50 with 31.66%. © 2023 IEEE.

关键词： Deep learning

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Transmission of ECG in Android Application by Cloud Server 1

Transmission of ECG in Android Application by Cloud Server

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Elavarasan, N. Bindu, Garikapati Buvaneswari, B. Kanaga Priya, P. Srinivasan College of Art's and Science Department of Computer Applications Perambalur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur India Panimalar Engineering College Department of Information Technology Chennai India Computer Science and Engineering KPR Institute of Engineering and Technology Arasur India

ISBN: (纸本)9798350323795

This paper proposes an approach for transmitting electrocardiographic (ECG) signals through a cloud server from an application named App1 to a remote application called App2. The objective is to enable real-time monitoring and analysis of ECG signals, facilitating remote healthcare services and enhancing the accessibility of medical data. The proposed approach leverages the capabilities of cloud computing and network connectivity to establish a seamless and secure communication channel between the two applications. The proposed approach has the potential to revolutionize remote healthcare services by enabling real-time transmission of ECG signals through a cloud server. It facilitates continuous monitoring of patients' cardiac activity, enhances collaboration between healthcare professionals, and expands the reach of healthcare services to remote areas. However, further research and development are required to address potential challenges such as network latency, data privacy concerns, and system scalability to accommodate a large number of users and data streams. © 2023 IEEE.

关键词： Cloud computing

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Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

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computer Systems Science & engineering 2023年第3期44卷 2243-2257页

作者： B.Kalpana S.Dhanasekaran T.Abirami Ashit Kumar Dutta Marwa Obayya Jaber S.Alzahrani Manar Ahmed Hamza Department of Information Technology RMD Engineering CollegeChennai601206India Department of Information Technology Kalasalingam Academy of Research and EducationSrivilliputtur626126India Department of Information Technology Kongu Engineering CollegeErode638060India Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Biomedical data classification has become a hot research topic in recent years,thanks to the latest technological advancements made in ***-dical data is usually examined by physicians for decision making process in patient *** manual diagnosis is a tedious and time consuming task,numerous automated models,using Artificial Intelligence(AI)techniques,have been presented so *** this motivation,the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI(BDC-CMBOAI)*** aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical ***,the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection(CMBO-FS)technique to derive a useful subset of *** addition,Ridge Regression(RR)model is also utilized as a classifier to identify the existence of *** novelty of the current work is its designing of CMBO-FS model for data ***,CMBO-FS technique is used to get rid of unwanted features and boosts the classification *** results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.

关键词： Artificial intelligence biomedical data feature selection cat and mouse optimizer ridge regression

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A Knowledge-and-Data-Driven Modeling Approach for Mobile Payment Mode Recognition

A Knowledge-and-Data-Driven Modeling Approach for Mobile Pay...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Li-Na Fan, Xing-Rong Zhang, Yu-Bu Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Asset Management Department Chongqing Technology and Business University Chongqing China Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Technology and Business University Chongqing China School of Artificial Intelligence School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China

ISBN: (纸本)9798350368604

Identifying different mobile payment modes based on cell phone signaling data in user movement is crucial for mobile payment scenario planning, payment risk assessment, and analyzing payment behavior patterns across multiple users. This paper proposes a novel knowledge-and-data-driven modeling approach to optimize the use of signaling data for mobile payment mode recognition. The approach integrates both unparsed signaling data and domain-specific URL-Key knowledge extracted from parsed signaling data that contains payment-related information. First, we develop a data-driven model for mobile payment event recognition, leveraging cell phone signaling data. Next, we establish a knowledge-driven model that utilizes domain URL-Key knowledge to identify mobile payment modes during user movement. These two models are then integrated using a composite coupling operator, and real data from a mobile telecommunication company located in Chongqing are used to calibrate and test our approach. Experimental results show that our approach significantly enhances both performance and efficiency. © 2024 IEEE.

关键词： Risk assessment

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Exploring Sensor-Based Smart Farming Technologies in the Internet of Things (IoT) 1

Exploring Sensor-Based Smart Farming Technologies in the Int...

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1st IEEE International Conference on Computing and Data Science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： Internet of things

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Grocery Purchase through Blockchain System 7

Grocery Purchase through Blockchain System

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Kumar, Boddu Manoj Rajeswari, G. Thanush, B.V. Krishna, Vamsi Sree, E. Divija Balakrishnan, Arunkumar Sathyabama Institute of Science and Technology School of Computer Science Engineering with Blockchain Technology Tamil Nadu Chennai600119 India Sathyabama Institute of Science and Technology School of Computer Science Engineering Tamil Nadu Chennai600119 India VIT-AP University School of Computer Science Engineering India Crm Institute of Technology School of Computer Science Engineering Karnataka Bengaluru India Sree Vidyanikethan Engineering College Rangampeta School of Computer Science Engineering with Artificial Intelligence Tirupati517102 India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India

ISBN: (纸本)9798331527822

The integration of blockchain technology into the grocery purchasing process offers a transformative approach to enhancing transparency, security, and efficiency. This paper presents a comprehensive framework for a blockchain-based grocery system, detailing each step from user registration and identity verification to product listing, smart contract creation, and real-time inventory management. Consumers can place orders with confidence, knowing that payments are securely processed and supply chain logistics are fully transparent. The use of IoT devices ensures quality control during transportation, while smart contracts automate delivery and receipt confirmation. Additionally, the system supports consumer feedback and dispute resolution through immutable records and predefined arbitration mechanisms. This innovative application of blockchain technology aims to revolutionize the grocery sector, fostering trust and accountability among all stakeholders involved. Future research will explore the potential for AI integration and address scalability and interoperability challenges. © 2024 IEEE.

关键词： Smart contract

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Enhanced Resource Allocation Strategy using Cox Regression for Wormhole Attack Mitigation in Routing Protocols 5

Enhanced Resource Allocation Strategy using Cox Regression f...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Praveenkumar, G.D. Ilango, M. Mohanasathiya, K.S. Naveen, N. Rakkimuthu, P. Kongu Engineering College-Erode Department of Computer Technology -UG India Government Arts and Science College Gudalur Department of Computer Science Tamilnadu The Nilgiris India VET Institute of Arts and Science Co-Education College- Erode Department of Computer Science India Excel Engineering college Department of Information Technology Tamilnadu India

ISBN: (纸本)9798350367171

MANETs, as self-configuring networks lacking a fixed infrastructure, are exceptionally vulnerable to a multitude of security threats. One such severe threat is the Wormhole Attack, where malicious nodes create a virtual link between two distant points in the network, creating a shortcut for communication. This deceptive behavior disrupts the routing protocols and leads to severe consequences, including data manipulation and network partitioning. The Wormhole Attack has had a significant impact on MANETs. This study stimulates COX Regression techniques that are used to calculate the regression coefficient for the association between mobile nodes to perform routing in the MANETs. Based on the calculation of residual energy, the delay and bandwidth are identified as optimal. Additionally, various mitigation techniques are proposed and evaluated to improve the network's security in the face of this sophisticated attack. According to the computation of residual energy, delay and bandwidth is detected as optimal one. Additionally, this study proposes and evaluates different mitigation techniques to enhance the network's security against this sophisticated attack. The proposed methods findings highlight the importance of proactive measures and vigilance in safeguarding MANETs from Wormhole Attacks. This helps to improve the rate of packet delivery with reduced latency and the lifetime of data transmission within the network. © 2024 IEEE.

关键词： Bandwidth

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