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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Saeed, Ali Swetha, K. Singh, Navdeep Yadav, Mohit Anandhi, R.J. Venkatraman, Akila College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Griet Department Of Information Technology Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

The main point of this study is to find out how blockchain technology might be able to help make business financial deals safer and more open. Blockchain technology is changing the financial world because it creates a global record system that can't be changed. These tools are making this change happen. Three parts are used in the method given to create a safe system: the Elliptic Curve Digital Signature Algorithm, Proof of Work agreement, and Merkle Trees. In this method, Merkle Trees are also used. These techniques are used by this system to investigate not only the data, but also the security of the cryptography and the proof that comes from outside the system. To find out how well firewalls, audit trails, SSL encryption, multi-signature wallets, and role-based access control work, the components must be compared to them. Using this method could have several benefits, such as decentralization, immutability, openness, correctness, foreign access, automation of smart contracts, and increased efficiency. According to the study, blockchain technology can make information easier to find, speed up financial transfers, and reduce concerns about security. © 2025 IEEE.

关键词： Authentication

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SVM-based Predictive Modeling of Drowsiness in Hospital Staff for Occupational Safety Solution via IoT Infrastructure 2

SVM-based Predictive Modeling of Drowsiness in Hospital Staf...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Ganesh, B. Jai Vijayan, P. Vaidehi, V. Murugan, S. Meenakshi, R. Rajmohan, M. R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Vinayaka Mission's Kirupananda Variyar Arts and Science College Department of Computer Science Tamil Nadu Salem India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

This research proposes an efficient approach to improve hospital worker safety on the job by detecting and preventing drowsiness. The system incorporates data from hospital-deployed Internet of Things (IoT) infrastructure and using Support Vector Machines (SVM) as its primary predictive modeling tool. Data pertaining to ambient factors, work habits, and physiological indications of employees are collected in real-time by the IoT infrastructure. To identify patterns linked to sleepiness, the SVM model is trained on an extensive dataset. The next step is to implement the model in a healthcare setting so that it can detect signs of sleepiness and act accordingly in a timely manner. With the help of the IoT the predictive model can be dynamically adjusted and monitored in real-time, making it useful in a wide range of situations. The recommended approach is to enhance workplace safety by reducing the likelihood of accidents caused by sleepiness among healthcare workers. The SVM-based prediction model can correctly detect and forecast cases of sleepiness, the research subjected it to extensive testing and validation. This system's adoption helps with the creation of data-driven, proactive methods to improve hospital occupational safety. © 2024 IEEE.

关键词： Internet of things

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Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Model Security

Unbridled Icarus: A Survey of the Potential Perils of Image ...

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

作者： Fan, Yihe Cao, Yuxin Zhao, Ziyu Liu, Ziyao Li, Shaofeng School of Electronics and Information Engineering TongJi University China School of Computing National University of Singapore Singapore Fan Gongxiu Honors College Beijing University of Technology China Nanyang Technological University Singapore School of Computer Science and Engineering Southeast University China

ISBN: (纸本)9781665410205

Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities that increasingly influence various aspects of our daily lives, constantly defining the new boundary of Artificial General Intelligence (AGI). Image modalities, enriched with profound semantic information and a more continuous mathematical nature compared to other modalities, greatly enhance the functionalities of MLLMs when integrated. However, this integration serves as a double-edged sword, providing attackers with expansive vulnerabilities to exploit for highly covert and harmful attacks. The pursuit of reliable AI systems like powerful MLLMs has emerged as a pivotal area of contemporary research. In this paper, we endeavor to demostrate the multifaceted risks associated with the incorporation of image modalities into MLLMs. Initially, we delineate the foundational components and training processes of MLLMs. Subsequently, we construct a threat model, outlining the security vulnerabilities intrinsic to MLLMs. Moreover, we analyze and summarize existing scholarly discourses on MLLMs' attack and defense mechanisms, culminating in suggestions for the future research on MLLM security. Through this comprehensive analysis, we aim to deepen the academic understanding of MLLM security challenges and propel forward the development of trustworthy MLLM systems. © 2024 IEEE.

关键词： Semantics

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Enhancing Fintech Fraud Detection through Graph Convolutional Neural Networks 1

Enhancing Fintech Fraud Detection through Graph Convolutiona...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Pavan, V. Omprakash, Omprakash Bugge, Bhagya Prasad Thangamani, S. Perada, Anvesh Suneetha, Sampathirao Manipal Academy of Higher Education Manipal Law School Manipal India Jain University School of Law Bengaluru India S.R.K.R Engineering College Department of Electronics and Communication Engineering Bhimavaram India Nandha Engineering College Department of Information Technology Erode India Drexel University Department of Electrical and Computer Engineering PhiladelphiaPA United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798331529246

The swift advancement of fintech has transformed financial services, offering users seamless digital experiences. Nonetheless, this expansion has also presented intricate cybersecurity difficulties, especially in fraud detection. This research investigates novel methods for addressing fintech fraud through the utilization of GCNN and the ResNet101-C model. GCNN adeptly addresses dynamic fraud trends via graph-based feature aggregation, whereas ResNet101-C, augmented with dropout and fully connected layers, attains enhanced classification performance. Preprocessing procedures, such as imputation, noise reduction, and feature standardization, enhance data quality, whereas feature extraction emphasizes essential transaction variables like frequency and value. Assessment of synthetic datasets reveals that ResNet101-C achieves an accuracy of 93.46%, surpassing leading models. These results underscore the transformational capability of GCNN and ResNet101-C in tackling the dynamic nature of fraud, providing scalable, precise, and privacy-preserving solutions for the financial sector. © 2025 IEEE.

关键词： Noise abatement

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Indian Sign Language to Speech Conversion Using Convolutional Neural Network 2

Indian Sign Language to Speech Conversion Using Convolutiona...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Shashidhar, R. Hegde, Surendra R Chinmaya, K. Priyesh, Ankit Manjunath, A.S. Arunakumari, B.N. Sri Jayachamarajendra College of Engineering Jss Science and Technology University Department of Electronics and Communication Engineering Mysuru India Jss Science and Technology University Sri Jayachamarajendra College of Engineering Department of Computer Applications Mysuru India Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

True incapacity is the inability to speak. A person who has a speech impediment is unable to communicate with others through speech and hearing. Individuals use sign language as a form of communication to overcome this disability. Even though signing has become commonplace in recent years, it can still be difficult for non-signers to communicate with signers. The flow of information and emotions in a person's life has become increasingly dependent on communication over time. The only way for that person with special needs to communicate with the rest of the world is through sign language, which uses entirely distinct hand motions. With the most recent developments in computer vision and deep learning techniques, there has been significant improvement in the disciplines of motion and gesture identification. For American Sign Language (ASL), sign language recognition has been a well-researched subject. Nevertheless, there aren't much published analysis works on Indian Sign Language (ISL). The intended method will recognise 4972 static hand signs for the twenty-four different English alphabets (A, B, C, D, E, F, G, H, I, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y). The main goal of our effort is to create a deep learning-based application that uses the "Google text to speech"API to translate sign language into text, facilitating communication between signers and non-signers. We made use of the Kaggle-available dataset. Proposed method using custom Convolutional Neural Network and got the accuracy of 99%. © 2022 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Image Processing Framework for Early Diagnosis of Diabetic Retinopathy

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computers, Materials & Continua 2024年第11期81卷 2665-2683页

作者： Kusum Yadav Yasser Alharbi Eissa Jaber Alreshidi Abdulrahman Alreshidi Anuj Kumar Jain Anurag Jain Kamal Kumar Sachin Sharma Brij BGupta College of Computer Science and Engineering University of Ha’ilHa’il81481Saudi Arabia Department of CSE Chandigarh UniversityMohaliPunjab140413India Department of Electronics and Communication Engineering Amity UniversityNoida201301India Department of Information Technology Indira Gandhi Delhi Technical University for WomenNew Delhi110006India Chief Manager State Bank of IndiaPanchkulaHaryana134109India Department of Computer Science and Information Engineering Asia UniversityTaichung City413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

In today’s world,image processing techniques play a crucial role in the prognosis and diagnosis of various diseases due to the development of several precise and accurate methods for medical *** analysis of medical images is essential for doctors,as manual investigation often leads to inter-observer *** research aims to enhance healthcare by enabling the early detection of diabetic retinopathy through an efficient image processing *** proposed hybridized method combines Modified Inertia Weight Particle Swarm Optimization(MIWPSO)and Fuzzy C-Means clustering(FCM)*** FCM does not incorporate spatial neighborhood features,making it highly sensitive to noise,which significantly affects segmentation *** method incorporates a modified FCM that includes spatial functions in the fuzzy membership matrix to eliminate *** results demonstrate that the proposed FCM-MIWPSO method achieves highly precise and accurate medical image ***,segmented images are classified as benign or malignant using the Decision Tree-Based Temporal Association Rule(DT-TAR)*** analysis with existing state-of-the-art models indicates that the proposed FCM-MIWPSO segmentation technique achieves a remarkable accuracy of 98.42%on the dataset,highlighting its significant impact on improving diagnostic capabilities in medical imaging.

关键词： Image processing biological data PSO Fuzzy C-Means(FCM)

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A Novel Hybrid GAACO Algorithm for Cloud Computing using Energy Aware Load Balance Scheduling 12

A Novel Hybrid GAACO Algorithm for Cloud Computing using Ene...

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12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Senthil, G.A. Somasundaram, K. Arun, M. Saranya, N. Naga Prabha, R. Babu, D. Vijendra Dept of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Dept of Information Technology Chennai India Panimalar Institute of Technology Dept of Electronics and Communication Engineering Chennai India Meenakshi College of Engineering Dept of Master of Computer Application Chennai India Sri Sairam Institute of Technology Dept of Electronics and Communication Engineering Chennai India Aarupadai Veedu Institute of Technology Vinayaka Mission's Research Foundation Dept of Electronics and Communication Engineering Paiyanoor Chennai India

ISBN: (纸本)9781665480352

The process of spreading workloads and computing resources through one or more servers is cloud load balancing. This method of delivery ensures maximum response time at low throughput. The workload is divided between two or more servers, storage devices, network connectivity or other data centers, thus allowing efficient use of resources and response time for the device. To ensure device cloud performance, an energy-aware operating model is implemented which is used for load balancing. The method describes an energy-optimal operating regime and seeks to optimize the number of servers that run under this regime. To save resources, idle and lightly charged servers are moved to one of the sleeping states. A Genetic Algorithm and Ant Colony Optimization (GAACO) framework for energy-efficient cloud computing is specified based on meta-heuristics inspired by nature, namely novel QoS based on hybrid genetic ant colony, and load scheduling algorithm. The proposed energy-aware QoS load balance scheduling was implemented in an ns3 simulator and the performance was evaluated in terms of packet loss ratio and throughput. © 2022 IEEE.

关键词： Energy utilization

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Benign and Malignant Brain Tumor Segmentation Using a Melody-Search Optimization Algorithm with an Extreme Softplus Learning

Benign and Malignant Brain Tumor Segmentation Using a Melody...

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2022 IEEE Silchar Subsection Conference, SILCON 2022

作者： Berlin Shaheema, S. Muppalaneni, Naresh Babu Jasper, J. National Institute of Technology Department of Computer Science and Engineering Assam Silchar India St. Thomas Institute for Science and Technology Department of Electrical and Electronics Engineering Kerala India

ISBN: (纸本)9781665471008

Brain tumor is a terrible disease that affects people worldwide. The main reason behind the growth of brain tumors is the uncontrolled and abnormal development of cells in the brain. Early detection of such growth improves survival. Therefore, developing automated systems that detect abnormal growth will help radiologists make accurate diagnoses. This paper presents a new metaheuristic-based methodology for early detection of brain tumors using the Melody Search Optimization Algorithm with Extreme Softplus learning. In the pretreatment step, image quality is improved by intensity normalization in combination with an adaptive bilateral filter. Histogram Oriented Gradient (HOG) extracts slope features. Automatic brain tumor segmentation splits tumor into sub-regions using Melody Search Optimization Algorithms combined with Extreme Softplus Learning. The performance of the proposed tumor segmentation technique is assessed based on the images acquired from the database. Our experiments have demonstrated that improved segmentation can help avoid the next level of danger. Segmentation metrics were calculated and compared to manual depictions, such as dice similarity, Jaccard index, and percentage of relative error (RE %). © 2022 IEEE.

关键词： Tumors

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Enhanced Condition Monitoring of Low Voltage Motors in Harsh Environments with Ladybug Beetle Optimization and Context-Aware Graph Convolutional Networks 4

Enhanced Condition Monitoring of Low Voltage Motors in Harsh...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Rajasekar, P. Tiwari, Praveen Prakash Sharmila, L. Garg, Akriti Srinivas, Rayudu Tallapragada, V. V. Satyanarayana Srm Institute of Science and Technology Department of Data Science and Business System Tamil Nadu Kattankulathur603203 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India Mahatma Jyotiba Phule Rohilkhand University Department of Electrical Engineering Uttar Pradesh Bareilly243006 India Aditya College of Engineering & Technology Department of Electrical and Electronics Engineering Andhra Pradesh Surampalem533437 India School of Engineering Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India

ISBN: (纸本)9798331540364

Low voltage motors are essential in many applications in contemporary industrial settings, as they power machines and guarantee efficient operations. However, these motors often operate in harsh environments where they are exposed to factors such as extreme temperatures, moisture, dust, and vibrations, which can lead to premature wear and failure. This paper proposes a hybrid method for low voltage motor condition monitoring in challenging industrial environments. The proposed method is the combined execution of Ladybug Beetle Optimization algorithm (LBOA) and Heterogeneous Context-Aware Graph Convolutional Network (HCAGCN). Hence it is named as LBOA-HCAGCN technique. Initially, the input data is collected from Triboelectric vibration sensors. Then, the collected data is fed into preprocessing utilizing Generalized Multi-kernel Maximum Correntropy Kalman Filter (GMKMCKF). This filter normalizes and cleans the data by effectively removing noise. Recursive Hilbert Transform (RHT) is used to extract time domain features such as mean, Root Mean Square (RMS), Peak-to-Peak Value, Skewness, and Kurtosis. Then the extracted features are given to HCAGCN for classify the motor health conditions such as normal, broken-bar, bowed-bar, bowed-rotor, faulty bearing, and voltage imbalance. In general, HCAGCN does not express adapting optimization strategies to determine optimal parameters to classify motor conditions. Hence, the LBOA is used to optimize the weight parameter of the HCAGCN which accurately classify motor conditions. The proposed LBOA-HCAGCN is implemented in MATLAB. Performance indicators such as Mean Absolute Error (MAE), Precision, and Accuracy were used to analyze the effectiveness of the proposed approach. Comparing the proposed LBOA-HCAGCN methodology to other existing techniques like Deep Residual Neural Network (DRNN), Convolutional Neural Network (CNN), and Recursive Neural Network (RNN), it achieves 25.8%, 26.4%, and 24.7% greater accuracy, 26.7%, 29.4%, and

关键词： Mean square error

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Smart Personal Protective Equipment in Ambulance Services with IoT Integration for Safety 2

Smart Personal Protective Equipment in Ambulance Services wi...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Senkamalavalli, R. Merlin Livingston, L.M. Guruva Reddy, Elangovan Basavaraddi, Chethan Chandra S. Latha, N. Department of Computer Science and Engineering Kammavari Sangham Institute of Technology Karnataka Bangalore India Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur Karnataka Tumkur India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Internet of Things (IoT) technology has improved ambulance safety and efficiency. In ambulance services, Smart PPE (Personal Protective Equipment) improves patient and healthcare worker safety via IoT connectivity. In crises, ambulances are vital. However, healthcare professionals risk contagious infections and physical damage. Though vital, traditional PPE lacks real-time monitoring and communication, leaving healthcare workers exposed. These issues are addressed with Smart PPE using IoT. This novel invention adds sensors and communication devices to paramedic and EMT PPE. These sensors measure air quality, temperature, and the wearer's heart and body temperature. A centralized system can remotely monitor healthcare personnel's safety and well-being using real-time data. Smart PPE has two-way communicators and emergency alert systems. Emergency coordination is improved by ambulance crews and dispatch centers communicating seamlessly. The Smart PPE can automatically inform the team and dispatch in urgent situations, increasing response times and patient outcomes. IoT enables predictive maintenance and data analytics. Trends and dangers may be identified from gathered data, allowing proactive risk mitigation. Smart PPE can measure use and remind users to maintain and replace equipment, assuring dependability when required most. By continually monitoring conditions, allowing real-time communication, and enabling data-driven decision-making, this unique technology improves ambulance services and reduces dangers to devoted personnel. © 2023 IEEE.

关键词： Ambulance Services Efficiency Improvement Emergency Healthcare Real-time Alerts Safety Enhancement

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