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Cybernetics, Cognition and machine learning Applications (ICCCMLA), IEEE International Conference on

作者： Riya Sharma Jayesh Gangrade Shweta Gangrade Ashish Mishra Gautam Kumar Vinit Kumar Gunjan Dept of Computer Science & Engineering Manipal University Jaipur Jaipur India Dept. of Artificial Intelligence & Machine Learning Manipal University Jaipur Jaipur India Dept of Information Technology Manipal University Jaipur Jaipur India Dept of Computer Science & Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering CMR Institute of Technology Hyderabad India

The only way to prevent blindness from eye problems is by early detection and prompt treatment. Although colour fundus photography (CFP) is useful for fundus inspection, there is a need for computer-assisted automated diagnosis tools due to the similarities between the early symptoms of many eye disorders. The suggested approach uses cutting-edge deep learning model to categorize images into several disease categories by learning distinguishing features from the input images. The high-resolution fundus photos from individuals with diabetic retinopathy (DR), glaucoma, cataract, and healthy eyes make up most of the dataset used for this research. The experimental findings show that the suggested system achieve 97% accuracy with modified efficientNetB3 model and surpasses current approaches for categorizing eye diseases. This approach may help doctors diagnose and treat eye conditions earlier, leading to better patient outcomes.

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Provably Secure and Efficient One-to-Many Authentication and Key Agreement Protocol for Resource-Asymmetric Smart Environments

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IEEE Internet of Things Journal 2025年

作者： Li, Long Hsu, Chingfang Cui, Jianqun Au, Man Ho Harn, Lein Li, Quanrun Central China Normal University Computer School Wuhan430079 China Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Wuhan430079 China Department of Computing Hong Kong Polytechnic University 25809 Hong Kong University of Missouri-Kansas City Department of Computer Science Electrical Engineering MO64110 United States Zhejiang Sci-Tech University School of Cyber Science and Engineering Hangzhou310020 China

The smart environment is a crucial application of the Internet of Things(IoT). Due to its growing security and efficiency needs, recent years have seen the proposal of numerous authentication and key agreement (AKA) protocols. Unfortunately, most of existing AKA protocols only support one-to-one AKA and rely on the elliptic curve cryptosystem, resulting in huge overhead. In addition, these protocols fail to consider the resource-asymmetric characteristics of this scenario. That is, the resources on the gateway side are abundant, while the resources on user sides and device sides are limited. In order to achieve efficient and secure one-to-many AKA establishment in this scenario, where one-to-many means that users can realize key agreements with multiple smart devices at the same time. For the first time, this paper uses the one-to-many computing structure of the Chinese Remainder Theorem (CRT) to design an efficient one-to-many AKA establishment, which is perfectly adapted to resource-asymmetric allocation in smart environments. Compared with existing solutions, this solution has the following advantages. Firstly, our protocol is suitable for resource-asymmetric environments, where the gateway acts as an intermediate node and uses rich resources to integrate multiple AKA requests. Secondly, the solution supports users to negotiate session keys with multiple smart devices at the same time. Thirdly, we prove the protocol’s security under the Real-or-Random (ROR) model. In addition, we perform formal security verification of the protocol using the Automated Validation of Internet Security Protocols and Applications(AVISPA) tool. Finally, the security and efficiency of this solution are superior to similar solutions. Specifically, our solution can meet 18 security and functionality requirements. Compared with the latest similar scheme, assuming that the number of smart devices is 10, our scheme reduces the computational cost by 75.75%. At the same time, in terms of comm

关键词： Authentication Protocol

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Energy Efficient Dynamic Improved Optimization Method for Resource Management in Cellular Networks 3

Energy Efficient Dynamic Improved Optimization Method for Re...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Rana Veer Samara Sihman Bharattej, R. Alabdeli, Haideer Pareek, Piyush Kumar Meenakshi, S. Jeyanthi, S. National Louis University ChicagoIL United States College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Dhanalakshmi Srinivasan College of Engineering Technology Department of Electronics and Communication Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Applications Chennai India

ISBN: (纸本)9798331508456

The dense deployment of small cell networks is primary feature of next generation mobile networks assigned to offer essential ability. The small cells are deployed in regions covered through macro-Base Stations (eNBs) for supplying required local capacity depended on hierarchical networks. Additionally, small cell networks utilize high capacity backhaul links on millimeter wave bands for developing multi-hop topologies to overcome data broadcasting costs. In this manuscript, developed the dynamic improved optimization method for reducing energy consumption of 5G networks and offers significant capacity and coverage. By optimizing the carrier allocation and power usage, developed algorithm determined when to turn ON or OFF small cells to meet constraints of Quality of Service (QoS) of users with greatest level of energy efficiency. Moreover, developed the multi-hop backhauling strategy for effective utilization of existing architecture of small-cell networks for continuous dual-hop transmissions. In accordance with simulation outcomes, the energy efficiency and data rates of system effectively enhanced. © 2025 IEEE.

关键词： Resource allocation

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The Role of Non-Invasive Neural Devices in Modern Neurorehabilitation and Mental Health Treatment

The Role of Non-Invasive Neural Devices in Modern Neurorehab...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Adesh Pawar Palash Gourshettiwar Pradnyawant M. Gote Vishal Kumar Choudhary Prajyot Yesankar Shrikant Khadse Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U) Wardha Maharashtra India Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U.) Wardha Maharashtra India Department of Artificial Intelligence &Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U.) Wardha Maharashtra India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Modern neurorehabilitation and mental health treatment have received transformative tools through non-invasive neural devices that offer effective and patient-friendly alternatives to traditional interventions. Technologies in this regard include transcranial magnetic stimulation, transcranial direct current stimulation, EEG-based neurofeedback, and functional near-infrared spectroscopy, where advances in neuroscience and bioengineering are utilised in modulating brain activity without risks associated with surgical procedures. Their applications in neurorehabilitation range from post-stroke motor recovery to cognitive rehabilitation and treatment of mental health disorders, including depression, anxiety, and PTSD. Focusing on personalised medicine, machine learning, and big data could be used in non-invasive neural devices to fine-tune outcomes tailored to an individual's needs. These are seminal challenges, such as accessibility, variability in patient responses, and regulatory hurdles, in the face of these devices offering much promise regarding enhancing the quality of life and improving cognitive functions, besides providing sustainable, long-term mental health. This paper underlines their potential and discusses future directions in integration.

关键词： Neuroplasticity Transcranial magnetic stimulation Surgery Mental health machine learning Motors Market research Neurorehabilitation Transcranial direct current stimulation Neurofeedback

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Classification for Jaundice Symptoms Using Improved Dragonfly with XGBoost Model

Classification for Jaundice Symptoms Using Improved Dragonfl...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Piyush Kumar Pareek Vidyalakshmi K Anoop N Prasad Padmavathi Diggi Deepak H A Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Information Science and Engineering Sri Siddhartha Institute of Technology Tumakuru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Navkis College of Engineering Hassan India

The yellowing of the skin is a sign of neonatal hyperbilirubinemia, often known as jaundice. The most common method for screening for Hyperbilirubinemia in newborns is a visual examination; however, collecting blood specimens is the gold standard for identifying the condition and its harshness. Albumin-bound bilirubin was dissolved, producing the yellow hue. Too much haemoglobin breakdown results in bilirubin that the liver can't process. There are two ways for detecting bilirubin, an invasive one and a non-invasive one. Both methods have their advantages and disadvantages, but the invasive one needed more blood samples and was less pleasant to the patient. Neonatal jaundice is a physiological condition that affects about 80% of newborns. Because of this, hyperbilirubinemia occurs. Hence, it is important to check bilirubin levels on a frequent basis to determine whether patients require treatment and to identify those who may be at risk. The present continuous bilirubin measuring equipment is not only less precise than its predecessors but also more costly. In this research, we offer a diagnostic system for illness prediction using an enhanced version of the Extreme Gradient Boosting (XGBoost) classifier. A classifier's effectiveness relies heavily on the accuracy with which its hyperparameters are tuned. We employed the very effective hyper-parameter optimisation approach known as enhanced dragonfly optimisation (IDFO) on the XGBoost hyper-parameters. To liken the presentation of the suggested model to that of other replicas, tests are carried out.

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An Involuntary Liver Cancer Recognition in Abdominal Liver Imageries with Soft Optimization Methods in Biomedical Application

An Involuntary Liver Cancer Recognition in Abdominal Liver I...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： M. Vinodh Kumar Raj Kumar Patra Veena K A Siva Kumar Reddy V Sunil Kumar Inventuriz Labs Pvt Ltd Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Telangana India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

A medical image recovery system can be used for a variety of useful purposes, one of which is the classification of medical images. When numerous different types of medical imaging evidence are accessible, it is essential to have accurate categorization models. Because of the CT paradigm in clinical diagnosis, radiologists are able to properly detect and monitor changes in the body's physiological state. The ability to differentiate between different types of tissues in a CT image based on the various grey rates along each line contributes significantly to the accuracy of a medical diagnosis. The standard practice for diagnosing cancer relies on scientific and histological data, both of which have the potential to lead to incorrect or erroneous findings. The liver, which may be found in the upper abdominal region of the external human form, is responsible for filtering the blood and getting rid of any toxins it discovers in the process. The distinction between the two types of cancer is taught through an artificial neural network in this method. A number of different parameters, including accuracy, area, correlation, entropy, homogeneity, contrast, and the similarity index, are employed to conduct an analysis of the impact. After that, researchers tried to enhance the early identification and treatment of liver cancer by segmenting and classifying liver tumors from CT images. This was done in order to gain a better understanding of the disease. The accuracy of the identification was raised to 99.63 percent thanks to the classifier and optimizer that was proposed.

关键词： Liver cancer Image segmentation Computed tomography Software algorithms Liver Software Biomedical monitoring

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Advancing Alzheimer's Nodule Detection Through a Comprehensive Multi-Scene Deep learning Framework

Advancing Alzheimer's Nodule Detection Through a Comprehensi...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： C. Premkumar R. Rajeshwari R. Remya M. S. Cholaathiraj A. Reethika S. Bharathidasan Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D institute of science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Electronics and Communication Engineering Erode Sengunthar Engineering College Erode India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAD modules, including MRI, PET, and CT, struggle with feature extraction and segmentation due to time limits and complexity. This study proposes an efficient brain nodule detection technique based on the Multi-Scene Deep learning Framework (MSDLF) that makes use of the vesselness filter. A four-channel CNN model is developed using information from two image sequences to enhance radiologists' ability to recognise four-stage nodules. This adaptable model may be used in two different classes. The outcomes demonstrate how well the MSDLF performs while processing substantial volumes of image data for brain nodule recognition, improving with accuracy 98.86% and 98.45% of sensitivity by using decreasing false positives.

关键词： Deep learning Training Solid modeling Accuracy Sensitivity Image recognition Magnetic resonance imaging Brain modeling Classification algorithms Alzheimer's disease

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learning the expressibility of quantum circuit ansatz using transformer

arXiv

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arXiv 2024年

作者： Zhang, Fei Li, Jie He, Zhimin Situ, Haozhen College of Computer and Information Engineering Henan Normal University Xinxiang China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province School of Electronic and Information Engineering Foshan University Foshan528000 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

With the exponentially faster computation for certain problems, quantum computing has garnered significant attention in recent years. Variational quantum algorithms are crucial methods to implement quantum computing, and an appropriate task-specific quantum circuit ansatz can effectively enhance the quantum advantage of VQAs. However, the vast search space makes it challenging to find the optimal task-specific ansatz. Expressibility, quantifying the diversity of quantum circuit ansatz states to explore the Hilbert space effectively, can be used to evaluate whether one ansatz is superior to another. In this work, we propose using a transformer model to predict the expressibility of quantum circuit ansatze. We construct a dataset containing random PQCs generated by the gatewise pipeline, with varying numbers of qubits and gates. The expressibility of the circuits is calculated using three measures: KL divergence, relative KL divergence, and maximum mean discrepancy. A transformer model is trained on the dataset to capture the intricate relationships between circuit characteristics and expressibility. Four evaluation metrics are employed to assess the performance of the transformer. Numerical results demonstrate that the trained model achieves high performance and robustness across various expressibility measures. This research can enhance the understanding of the expressibility of quantum circuit ansatze and advance quantum architecture search algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Expression of Concern for: Predicting Consumer Intention using Logistic regression by analyzing Social media data

Expression of Concern for: Predicting Consumer Intention usi...

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： T. Saravanan D. Venkatesan Department of CSE GITAM School of Technology GITAM (Deemed to be University) Bengaluru India Department of Artificial Intelligence and Machine Learning St.Martin's Engineering College Secunderabad India

ISBN: (数字)9781665474368

ISBN: (纸本)9781665474375

With the headway of web innovation and its development, there is a colossal of data out there and numerous information created for web clients. The Internet has become a stage for web based getting the hang of trading thoughts and imparting insights. Twitter, the mainstream microblogging webpage, has gotten expanding consideration as a special specialized instrument that encourages electronic verbal (e-verbal). This expansive reach of e-verbal gives purchasers colossal clout to impact brand picture and discernments regarding brand the board. We present computational models to foresee Twitter clients' mentality towards a particular brand through their own and social attributes. We likewise foresee their probability of making various moves dependent on their perspectives. This work considers the popular long range interpersonal communication site, Twitter, where individuals express contemplations and thoughts and either energetically or reluctantly uncover data about themselves, including their own advantages, different preferences. This work considers the popular long range interpersonal communication site, Twitter, where individuals express musings and thoughts and either enthusiastically or reluctantly uncover data about themselves, including their own advantages, different preferences. This work, consequently, focuses on a novel directed AI based characterization method. To accomplish high order exactness, the proposed framework presents preprocessing and the k-means grouping approach. The test results show that the proposed framework performs in a way that is better than existing methodologies regarding exactness, accuracy, review, and F-measure.

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Predicting Consumer Intention using Logistic regression by analyzing Social media data

Predicting Consumer Intention using Logistic regression by a...

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

关键词： Training Knowledge engineering Technological innovation Social networking (online) Instruments Computational modeling Blogs

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