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International Conference on Advanced Computing and Communication systems (ICACCS)

作者： N. Ashokkumar C. Kanmani Pappa Siva Kumar A Dhamodharan Srinivasan M. M. Vijay Department of ECE Mohan Babu University Tirupati India Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India Department of Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu Department of ECE SCAD College of Engineering and Technology Tirunelveli India

Biosensors will monitor a patient's physiological signals (ECG, EEG, etc.) and send an alarm as soon as irregularities are discovered. Downsized biomedical sensors are the focus of this article, which explores innovative circuit approaches for miniaturised sensors. In order to demonstrate the proposed biomedical compression engine, ECG compression was applied and assessed. A MIP (mixed-integer programming model) is used to represent and solve the clustering issue based on the power consumption analysis. It follows that an architectural model that may be used to design ECG monitoring systems has been provided, as well as an in-depth study of the ECG monitoring system's value chain and a complete evaluation of relevant literature based on an expert classification. Time synchronisation and signal synthesis are the two stages of the distributed cooperative sensing algorithm's execution of ECG signal gathering. This method uses the mutation probability of each solution to prevent local convergence.

关键词： Wireless sensor networks Electrocardiography Sensor systems Biosensors Synchronization Biomedical monitoring Integrated circuit modeling

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Fashionable Four Wheeler Parking to Reduce the Human Intervention and to Enhance the Flexibility in Parking system Using IoT

Fashionable Four Wheeler Parking to Reduce the Human Interve...

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Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： Manoj P Varun C M Keerthi Kumar N Pradip Padhye K. Aruna Kumari Puja Das Swagata Sarkar Department of Civil Engineering Vidyavardhaka College of Engineering Mysore India Department of Computer Science and business system R.M.K. Engineering College Kavaraipettai India Department of Mechanical Engineering BMS Institute of Technology and Management Bengaluru India School of Management Dr. Vishwanath Karad MIT World Peace University Pune India Department of Mathematics Gitam University Visakhpatanam India Department of Computer science Hiralal Mazumder Memorial college for Women Kolkata India Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India

Due to the lack of a mechanism in place to check online for parking availability, it is now quite difficult to find a parking spot in congested regions. Imagine being able to access information about parking slot availability on your phone and not having to move around to do so. The cutting-edge parking system powered by IoT can overcome this issue. The IoT-based parking system makes it simple to check the availability of parking spaces online. With the aid of this device, the parking system might be fully automated. You might have automatic admission, payment, and exit processes. A NodeMCU-based IOT-based car parking system is being created using five IR sensors, two servo motors, and NodeMCU. Three IR sensors are utilised to determine whether parking spaces are available, and two IR sensors are used at the entry and exit gates to identify vehicles. Based on the sensor value, servo motors control the gates' opening and closing. In this part, we'll show you how to upload data to the cloud that is reachable from anywhere using the Adafruit IO platform.

关键词： Space vehicles Technological innovation Motor drives Cloud computing Costs Logic gates Sensor systems

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Dynamic Searchable Scheme with Forward Privacy for Encrypted Document Similarity

Dynamic Searchable Scheme with Forward Privacy for Encrypted...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Mustafa A. Al Sibahee Chengwen Luo Jin Zhang Yijing Huang Zaid Ameen Abduljabbar National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen PR China College of Computer Science and Software Engineering Shenzhen University Shenzhen PR China Computer Technology Engineering Department Iraq University College Basrah Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen PR China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections.

关键词： Privacy data privacy Cloud computing Memory Search problems Cryptography Servers

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Design Of Financing Strategy Model of Financial Management Based on data Mining technology

Design Of Financing Strategy Model of Financial Management B...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： G. Chandramowleeswaran Laith H. Alzubaidi Anita Sofia Liz Neha Khare Azizbek Khurramov Swathi Baswaraju Department of Commerce and Business Administration VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai College of Technical Engineering The Islamic University Najaf Iraq Computer Science And Engineering New Prince Shri Bhavani College Of Engineering and Technology Chennai India Department of Computer Science & Engineering IES University Bhopal MP India Tashkent State University of Economics Tashkent Uzbekistan Tashkent Department of Computer Science & Engineering (Data Science) New Horizon College of Engineering Bengaluru

With the continued expansion of India's market economy, competition among firms is heating up. To effectively navigate this severe market rivalry, corporate managers are increasingly prioritizing internal management. In comparison to traditional financial analysis approaches, data mining technology provides for a more in-depth comprehension of the underlying information inside large amounts of financial data. It assists financial analysts, investors, and decision-makers in acquiring full insights into the company's financial status and making appropriate judgments on pertinent topics. Both financial and non-financial data produced by traditional accounting information systems frequently confront issues of excess data and insufficient information. When presented with an excess of structured or semi-structured data, adding data mining technologies to the system allows for a detailed and effective projection of the enterprise's future development *** succeed in this competitive market setting and achieve sustainable development, businesses must realize the critical role of financial management, finance, and data mining technologies. Fundraising management is an important component of financial management for the majority of businesses. On the one hand, finance management has a direct impact on the funding process for small and medium-sized businesses, ultimately defining the extent and pace of their financing initiatives. On the other hand, by prudently allocating and managing finances, businesses may assure the smooth functioning of diverse operations, providing a firm basis for debt payback financing. This article will go into the research and study of financial management and financing for businesses, exploiting the possibilities of data mining technologies.

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Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction

arXiv

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arXiv 2025年

作者： Xia, Xiaobo Liu, Xiaofeng Liu, Jiale Fang, Kuai Lu, Lu Oymak, Samet Currie, William S. Liu, Tongliang School of Computer Science University of Sydney Australia Department of Statistics and Data Science Yale University United States Michigan Institute for Data and AI in Society University of Michigan United States School for Environment and Sustainability University of Michigan United States College of Information Science and Technology Penn State University United States Department of Earth System Science Stanford University United States Department of Electrical Engineering and Computer Science University of Michigan United States

Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, offer trans-formative potential for large-scale water quality prediction and scientific insights generation. However, their widespread adoption in high-stakes decision-making, such as pollution mitigation and equitable resource allocation, is prevented by unresolved trustworthiness challenges including fairness, uncertainty, interpretability, robustness, generalizability, and reproducibility. In this work, we present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model predicting 20 water quality variables (encompassing physical/chemical processes, geochemical weathering, and nutrient cycling) across 482 U.S. basins. Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics, the inherent complexity of biogeochemical processes, and variable predictability, emphasizing critical performance fairness concerns. We further propose methodological frameworks for quantitatively evaluating critical aspects of trustworthiness, including uncertainty, interpretability, and robustness, identifying key limitations that could challenge reliable real-world deployment. This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management and provides a pathway to offering critical insights for researchers, decision-makers, and practitioners seeking to leverage artificial intelligence (AI) responsibly in environmental management. Copyright © 2025, The Authors. All rights reserved.

关键词： Resource allocation

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Adaptive Computer Vision Solutions for Individuals with Special Needs

Adaptive Computer Vision Solutions for Individuals with Spec...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Issam Jebreen Shadi Nashwan Ahmad Al-Qerem Ali Mohd Ali Mohammad A Al Khaldy Mohammed Rajab Faculty of Information Technology Middle East University Jordan Computer Science Department Zarka University Jordan College of Engineering Technology Al-Balqa Applied University Jordan Business Intelligence Data Analytics University of Petra Amman Jordan University Headquarter University Of Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

With the rapid evolution of artificial intelligence (AI) and computer vision, assistive technologies have become increasingly adept at supporting individuals with special needs. This paper provides a comprehensive examination of two critical areas: AI-powered visual assistance systems for the visually impaired and sign language recognition and generation frameworks for the Deaf and hard-of-hearing communities. We review the latest research trends, algorithms, and tools, highlighting emerging deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers in both domains. We then propose a unified framework that leverages edge computing, multimodal learning, and advanced human-computer interaction (HCI) techniques to address challenges such as real-time object recognition, complex gesture capture, data scarcity, and cultural variations in sign languages. We further detail an experimental design and set of results that demonstrate the feasibility and benefits of these approaches, followed by a discussion of ethical considerations, limitations, and future directions. Our goal is to illustrate a path toward more inclusive AI systems that offer robust solutions for accessibility, ultimately bridging communication gaps and fostering independence for individuals with special needs [1]–[11].

关键词： Sign language Computer vision Visualization Ethics Visual impairment Assistive technologies Transformers Real-time systems Object recognition Artificial intelligence

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Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network

Mobility and Behavior based Trustable Routing in Mobile Wire...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： B. Praveen Kumar J. Umamageswaran A.V. Kalpana R. Dhanalakshmi Department of Computer Science and Engineering KL University Vijayawada Andhra Pradesh India Department of Information Technology RMK Engineering College Chennai Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more complicated as the sensor nodes are low-power, cost-effective mobile devices with minimum resources. MWSNs raises deployed in security-critical applications due to their resource-constrained features; they are prone to several security attacks that critically affect data transmission. The most significant innovation of trust is that it evades untrustable nodes by activating several detection routes to detect and obtain node trust, enhancing security quickly. Mobility and Behavior based Trustable Routing (MBTR) is proposed to solve these issues. This approach measures sensor node mobility based on the Received Signal Strength (RSS) between the two sensor nodes. The trust manager computes the node trust ability by the node behavior. Here, the node behavior is checked through the node energy, supportive account, and received packet account. The trust manager selects the security algorithm based on distance during data transmission. The simulation examination demonstrates lesser delay and lesser packet loss in the network. Furthermore, it improves the network throughput in the MWSN.

关键词： Wireless communication Wireless sensor networks Computational modeling Routing Throughput Behavioral sciences Delays

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Computational access point selection based on resource allocation optimization to reduce the edge computing latency

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Measurement: Sensors 2022年 24卷

作者： Kumaran, K. Sasikala, E. Research Scholar SRM Institute of Science and Technology Chennai600089 India Department of Information Technology Easwari Engineering College Chennai600089 India Department of Data Science & Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603203 India

Mobile Devices (MD) and the Internet of Things (IoT) transfer compute tasks to the client through Computational Access Point (CAP) through the extensive implementation of Local Area Networks (LAN). If all IoT or mobile devices choose the same ports of entry to offload their work, then the calculation of offloading leads to an increase in operating costs. In a network of different CAP to Edge Computing (EC), this research proposes a resource allocation optimization technique and a computation offloading strategy that aims to reduce network costs by recommending the best transmit power distribution, bandwidth assignment, and computation task planning. The optimization problem of NP-hard solved by the proposed technique separates from sub-problems of resource allocation and offloading technique. An algorithm called Dynamic Weighed Quantum Arithmetic Optimization Algorithm (DWQAOA) was proposed for this offloading problem NP-hard. To achieve the ideal solution, a transition probability introduced the optimal Pareto connection during the optimization process. Numerous simulation results show that the edge task latency could be reduced from 3 to 25% relative to competing solutions. © 2022 The Authors

关键词： Resource allocation

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Transforming business with Generative AI Models Applications Trends

Transforming Business with Generative AI Models Applications...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Franciskus Antonius Alijoyo L. Chandra Sekhar Reddy V. Selvi Rekha Murugan Shital Kakad A. Balakumar Center for Risk Management and Sustainability Indonesia School of Business and Information Technology STMIK LIKMI Bandung Indonesia Department of CSE (Data Science) CMR College of Engineering & Technology Hyderabad India Department of Computer Science Mother Teresa Women's University Kodaikanal India Department of Commerce and Management Jain Deemed to be University Chris Canadian Degree college Bangalore India Department of AI & DS Vishwakarma Institute of Technology Pune Maharashtra India Department of ECE K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Generative artificial intelligence (AI) has unexpectedly emerged as a transformative generation, with the functionality to revolutionize numerous business sectors by way of developing new and realistic content material from present records. Unlike traditional AI fashions that cognizance in the main on class and reputation obligations, generative AI excels at producing pics, text, tune, and films, which drives innovation and operational performance across a couple of industries. This examine explores the impact of generative AI, evaluating its packages, overall performance metrics, and emerging traits using a complete dataset from Kaggle that includes numerous statistics kinds such as pics, textual content, track, and video. The research technique includes a radical manner of information collection, rigorous preprocessing, and distinct function extraction and engineering. data preprocessing is important, related to the removal of replica records, addressing missing values through imputation techniques, and standardizing records codecs to make certain consistency and reliability. Feature extraction focuses on figuring out key attributes associated with generative AI applications and their effect on commercial enterprise performance. Feature engineering extends this through growing and selecting new metrics to seize the nuanced elements of AI improvements. Classification is completed the usage of both supervised and unsupervised learning techniques to successfully categorize and examine the effect of generative AI on exclusive commercial enterprise procedures. The proposed method achieves an outstanding accuracy of 98%, demonstrating its effectiveness and reliability. Implemented in Python, this approach indicates tremendous ability for boosting innovation and operational efficiency.

关键词： Measurement Industries Technological innovation Quantum computing Generative AI Feature extraction Market research Reliability Unsupervised learning business

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Like an Ophthalmologist: Dynamic Selection Driven Multi-View Learning for Diabetic Retinopathy Grading 39

Like an Ophthalmologist: Dynamic Selection Driven Multi-View...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Luo, Xiaoling Xu, Qihao Wu, Huisi Liu, Chengliang Lai, Zhihui Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Diabetic retinopathy (DR), with its large patient population, has become a formidable threat to human visual health. In the clinical diagnosis of DR, multi-view fundus images are considered to be more suitable for DR diagnosis because of the wide coverage of the field of view. Therefore, different from the previous single-view DR grading methods, we design a dynamic selection-driven multi-view DR grading method to fit clinical scenarios better. Since lesion information plays a key role in DR diagnosis, previous methods usually boost the model performance by enhancing the lesion feature. However, during the actual diagnosis, ophthalmologists not only focus on the crucial parts, but also exclude irrelevant features to ensure the accuracy of judgment. To this end, we introduce the idea of dynamic selection and design a series of selection mechanisms from fine granularity to coarse granularity. In this work, we first introduce an Ophthalmic Image Reader (OIR) agent to provide the model with pixel-level prompts of suspected lesion areas. Moreover, we design a Multi-View Token Selection Module (MVTSM) that prunes redundant feature tokens and dynamically selects key information. In the final decision stage, we dynamically fuse multi-view features through the novel Multi-View Mixture of Experts Module (MVMoEM), to enhance key views and reduce the impact of conflicting views. Extensive experiments on a large multi-view fundus image dataset with 34,452 images prove that our method performs favorably against state-of-the-art models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Ophthalmology

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