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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Moorthy, Deepthi K Nagaraj, P. Subhashini, S.J. Mar Baselios College of Engineering and Technology Department of Computer Science and Engineering Nalanchira Trivandrum India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Srm Madurai College of Engineering and Technology Department of Information Technology Sivagangai India

ISBN: (纸本)9798350325799

Alzheimer's disease is a progressive and irreversible brain disorder affecting millions worldwide. The timely detection of Alzheimer's Disease (AD) is essential for the effective management and treatment of the disease. Machine learning (ML) approaches have shown promising results in detecting AD at an early stage. This review article provides an overview of the recent developments in the application of ML techniques for AD detection. Also discusses the application of certain image processing, feature extraction, and ML techniques for detecting AD. The article covers various ML algorithms and their performance in detecting AD with different data sources, such as MRI, PET, etc. with their advantages and limitations. Overall, this research study demonstrates the potential of ML approaches in improving the accuracy and efficiency of Alzheimer's disease detection, which could lead to better patient outcomes and disease management. © 2023 IEEE.

关键词： Neurodegenerative diseases

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ASHA: Machine Learning-Based Polycystic Ovary Syndrome (PCOS) Detection and Prediction System 1

ASHA: Machine Learning-Based Polycystic Ovary Syndrome (PCOS...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Dixit, Jayati Rai, Anshuman Tandon, Pulak Pandey, Dhiraj JSS Academy of Technical Education Noida Information Technology Department Noida India JSS University Noida Computer Science and Engineering Department Noida India

ISBN: (纸本)9798350355611

About 17.5% of adults worldwide are infertile, which emphasizes the urgent need for cutting-edge reproductive healthcare solutions. Based on statistics from June 2023. According to the World Health Organization (WHO), 8-13% of females on earth who are in their reproductive age have PCOS, and about 70% of cases go undiagnosed. With the integration of PCOS detection, IVF success rate prediction, and a period tracking system, this work offers a comprehensive women's health application. In addition, a large percentage of women suffer from menstruation abnormalities, such as dysmenorrhea (38.1%) and irregular frequency (80.7%). This approach improves early illness detection and fertility planning by using data analysis and machine learning. The techniques used are Random Forest, K-Nearest Neighbor, Logistic Regression, Naive Bayes, Decision Tree and SVM;which provides us better accuracy than previous works. © 2025 IEEE.

关键词： Nearest neighbor search

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Refining Pancreatic Cancer Detection and Classification through a Hybrid Approach: Stacked Deep Learning Integrated with the Sparrow Search Algorithm for Medical Image Analysis 2

Refining Pancreatic Cancer Detection and Classification thro...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Arunachalam, P. Devi, M. Kalpana Jose, Naduvathezhath Nessariose Vivekanandan, G. Sharma, Rinku Mahesh Yadav, Ganesh Kumar Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology MCA Department Sreenivasa Institute of Technology and Management Studies Chittoor Architect Denken Solutions Department of CSE Sri Sairam Institute of Technology Chennai Department of Computer Science and Engineering SVKM Institute of technology Dhule Department of Computer Science and Engineering JSS Academy of Technical Education

ISBN: (纸本)9798350383867

In the endeavour to improve the precision and efficiency of diagnosing and classifying pancreatic cancer, there is a new idea that fuses deep learning and the Sparrow Search Algorithm with a stacked structure. That is why we consider this approach as revolutionary in terms of utilizing medical image analysis for the diagnosis in the field of pancreatic oncology with more accurate results. Transforming using stacked deep learning allows the systemto recursively learn its own complex feature in medical images, which is a basis for the identification of cancer. SBA or Sparrow Search Algorithm has been developed based on this social sparrow model and is a separate optimization technique which helps in optimizing the method of classification in this case. The foremost aim of this particular paper is to incorporate these two strategies and use the endproduct to improve the diagnostics of pancreatic cancer and also theclassification of the same. The cyclic process is applied together with the stacked deep learning process as well as the Sparrow Search Algorithm to make the systems' continuous optimization for sensitivity and specificity in detecting pancreatic malignancies. It is well to remember that this is not just a pure technological innovationthat holds promise of changing the landscape of pancreatic diagnosisand therapy. This approach of diagnosis and treatment also suggests early diagnosis, develop individual management plan and likely to yield higher survival rates for such patients. In addition, it is also worth considering at the potential that has been provided in enhancement of all sorts of branches of medicine linked to image analysis, along with the potential that has been developed when the best of technologies is paired with the best of ideas in computation. In conclusion, this paper captures the notion of intentionality and exigency in medical procedures. This union of stacked deep learningwith the Sparrow Search Algorithm unveils a new epoch in the medic

关键词： Deep learning

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Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

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computers, Materials & Continua 2023年第11期77卷 2009-2030页

作者： Kalpesh Jadav Vishal Sorathiya Walid El-Shafai Torki Altameem Moustafa HAly Vipul Vekariya Kawsar Ahmed Francis MBui Department of Electronics&Communication Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Computer Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonSK S7N5A9Canada

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking *** shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and *** of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is *** real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above *** paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle *** method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground *** to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

关键词： Change illuminations ImageJ software intelligent traffic systems mixture of Gaussian model National Institute of Health vehicle tracking

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Real-time programmable metasurface for terahertz multifunctional wave front engineering

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Light(science & Applications) 2023年第9期12卷 1795-1806页

作者： Feng Lan Luyang Wang Hongxin Zeng Shixiong Liang Tianyang Song Wenxin Liu Pinaki Mazumder Ziqiang Yang Yaxin Zhang Daniel M.Mittleman Sichuan THz Communication Technology Engineering Research Center School of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313000China Zhangjiang Laboratory Shanghai 201204China National Key Laboratory of Solid-State Microwave Devices and Circuits Hebei Semiconductor Research InstituteShijiazhuang 050051China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China University of Chinese Academy of Sciences School of ElectronicElectrical and Communication EngineeringBeijing 101408China Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA School of Engineering Brown UniversityProvidenceRI 02912USA

Terahertz(THz)technologies have become a focus of research in recent years due to their prominent role in envisioned future communication and sensing *** of the key challenges facing the field is the need for tools to enable agile engineering of THz wave ***,we describe a reconfigurable metasurface based on GaN technology with an array-of-subarrays *** subwavelength-spaced array,under the control of a 1-bit digital coding sequence,can switch between an enormous range of possible configurations,providing facile access to nearly arbitrary wave front control for signals near 0.34 *** demonstrate wide-angle beam scanning with 1°of angular precision over 70 GHz of bandwidth,as well as the generation of multi-beam and diffuse wave fronts,with a switching speed up to 100 *** device,offering the ability to rapidly reconfigure a propagating wave front for beam-forming or diffusively scattered wide-angle coverage of a scene,will open new realms of possibilities in sensing,imaging,and networking.

关键词： wave scattered arbitrary

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Enabling Robust Security in MQTT-Based IoT Networks with Dynamic Resource-Aware Key Sharing

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Procedia computer science 2025年 252卷 633-642页

作者： Sharadadevi Kaganurmath Nagaraj Cholli Department of Computer Science Engineering Global Academy of Technology Bengaluru India Department of Information Science Engineering RV College of Engineering Bengaluru

This paper presents a novel approach aimed at developing a secure secret key-sharing system optimized for resource-constrained Internet of Things (IoT) devices. Focusing on the MQTT protocol, the research endeavors to establish secure communication channels between IoT devices and brokers, thereby enhancing the overall security of MQTT-based IoT deployments. This research introduces a novel Dynamic Lightweight Authentication for MQTT (DLA-MQTT) mechanism designed to the unique needs of IoT devices operating under the MQTT protocol. The DLA-MQTT mechanism leverages an innovative lightweight Generalized Feedback Shift Register (GFSR)-based Pseudo-Random Number Generator (PRNG) to generate ephemeral keys, ensuring secure communication while addressing the limitations of computational power and energy resources. Through a detailed comparative analysis with existing cryptographic solutions, the DLA-MQTT mechanism demonstrates superior performance in terms of computational overhead, energy consumption, and execution time, while maintaining robust security against common attacks such as Man-in-the-Middle (MitM) and Denial of Service (DoS). he proposed algorithm’s adaptability and scalability are validated using the Cooja simulator, where simulated IoT networks are subjected to various threat scenarios. The results confirm the DLA-MQTT mechanism’s efficacy, showcasing a significant reduction in resource utilization without compromising the strength of the security provided.

关键词： Internet of Things(IoT) Message Queuing Telemetry Transport (MQTT) Lightweight Cryptography Resource-Constrained Devices Security Pseudo-Random Number Generator Generalised feedback Shift Register (GFSR) Ephemeral Key Generation

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Deep-Based Hybrid Model For Enhancing Land Region Prediction Outcomes 2

Deep-Based Hybrid Model For Enhancing Land Region Prediction...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Selvapandian, D. Raja, Laxmi Tharageswari, K. Vm, Senthilkumar Jc, Nikitha Hemalatha, M. Karpagam Academy of Higher Education Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Faculty of Engineering Department of Cyber Security Coimbatore India Mahendra Institute of Technology Department of Information Technology Namakkal India Nandha Arts and Science College Department of Information Technology Erode India

ISBN: (纸本)9798331534950

Although satellite data is not a novel approach to mapping wetlands, its accuracy depends on data quality and categorization algorithms, making it a continuous challenge. The increasing accessibility of satellite data and the development of data modelling technologies, such as big data modelling using machine learning (ML) algorithms, have improved the accuracy of this type of mapping. Our work demonstrated a simple, scalable, and reliable wetland classification using linear Support Vector Machine (l-SVM) and unsupervised k-Nearest Neighbor (k-NN) machine learning approaches. Normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), the two primary inputs were modelled using Landsat optical data. Subsequently, l-SVM was used to supervise k-NN data using training data collected from outside observations. The accuracy test results indicate that there is a considerable reduction in error for both optical indices, and all prediction models have an accuracy of about 99%. © 2024 IEEE.

关键词： Data accuracy

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Plant Leaf Disease Identification based on Machine and Deep Learning Methods 3

Plant Leaf Disease Identification based on Machine and Deep ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kalaiarasi, P. Ramalakshmi, R. Kannan, V. Bhuvanesh, A. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Agriculture Kalasalingam Academy of Research and Education Krishnankoil India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

The substantial effects of plant diseases on agriculture, which result in losses in production of crops, financial losses, and food insecurity, highlight the necessity for plant leaf disease detection and categorization. In order to effectively control illnesses, stop them from spreading to other plants, and develop tailored treatment plans, early and precise disease identification is essential. Various strategies are used for plant leaf disease diagnosis, such as manual symptom assessment, computer image processing, and visual examination by skilled professionals. Traditional methods rely on human expertise to visually identify disease symptoms based on characteristic patterns, discolorations, or lesions on plant leaves. These techniques, however, are labor-intensive, time-consuming, and sensitive to bias and mistakes. The development of automated and computer-based techniques for the identification of plant leaf disease through the use of ML (machine learning) algorithms and digital imaging technology has garnered increasing attention in recent years. There are still a number of issues with the DL (deep learning) and ML algorithms. In order to identify research gaps and address them in the future while developing a unique framework, this study presents an examination of ML and DL models. © 2025 IEEE.

关键词： Deep learning

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SITSL: Strategic Image Tag Recommendation Framework Encompassing Semantics Oriented Learning and Reasoning 2

SITSL: Strategic Image Tag Recommendation Framework Encompas...

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2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Kumari, Rupa Deepak, Gerard National Institute of Technology Department of Electronics and Communication Engineering Meghalaya India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350383287

This paper introduces the concept of a strategic structure for Web Image Tagging that is oriented toward semantics and knowledge centric. It encompasses strategies like LDA (Latent Dirichlet Allocation) based topic modelling and ontology generation through the dataset terms and further help in the metadata generation. The generated metadata is classified using autoencoders classifier to yield the classified instances. In the proposed model, the classifier for logistics is applied in the dataset of web images that integrates features through Lin Similarity from the ontologies generated in the perfect global dataset. The LDA also enhances the topics and contextualizes that for classified instances of the dataset. The normalized compression distance with lance William index helps in semantics similarity computation for semantics measure-based reasoning and learning. Shuffle frog algorithm helps in Optimization helps in refining the initial solutions into a much more optimal solution state to produce in the top-tier images. So, to produce in the top-tier tags for the images and overall precision of dash with a measure of dash and an FDR of dash has been achieved by the proposed framework which is the best-in-class model for web image tagging. © 2024 IEEE.

关键词： Semantics

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Enhancing Lung Disease Identification with Multimodal Data Fusion and Deep Learning CNN Approach 5

Enhancing Lung Disease Identification with Multimodal Data F...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Grace Shalini, T. Shiny, G. Susan Saranya, R. Suresh Babu, P. Kavitha, R. Atheeswaran, Athiraja Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chengalpattu Kattankulathur India Sethu Institute of Technology Department of information technology Kariapatti India Velammal college of engineering and technology Department of Eee Madurai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil India Bannari Amman Institute Of Technology Department Of Computer Science And Engineering Erode Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331504403

Lung diseases, encompassing a wide spectrum from chronic conditions to acute infections, present a significant burden on global healthcare systems, necessitating precise and timely diagnostic methodologies. In this study, we propose a pioneering approach leveraging multimodal data fusion and deep learning Convolutional Neural Network (CNN) algorithms for enhanced identification of lung diseases. Our methodology integrates diverse data sources including medical images, clinical reports, and patient demographics to capture a comprehensive representation of lung pathology. We employ a state-of-the-art CNN architecture for medical image analysis, trained on a large-scale dataset encompassing various lung diseases and healthy lung tissue. Through extensive experimentation and validation, the approach achieved remarkable performance, with an accuracy of 94%, sensitivity of 92%, specificity of 93%, and an F1-score of 93% in accurately identifying and classifying lung abnormalities. Furthermore, we demonstrate the clinical utility of our method in providing valuable insights to healthcare professionals, aiding in timely diagnosis, prognosis, and treatment planning. Our findings highlight the potential of multimodal data fusion coupled with deep learning CNN algorithms in advancing the field of lung disease identification, offering a promising avenue for improving healthcare practices and patient outcomes in respiratory medicine. © 2024 IEEE.

关键词： Lung cancer

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