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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Naveen Kumaran, R. Sanjay Abhishek, J. Bhuvana, S. Karthick, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering SRM Institute of Science and Technology Uttar Pradesh Delhi–NCR Campus Ghaziabad India

ISBN: (纸本)9789819788354

Recent advancements in Smart Assistants (SAs) as well as home automation have captured the attention of both researchers and consumers. Virtual Assistants (VAs) that are speech-enabled are commonly referred to as smart speakers. They offer a wide range of network-based services. In some cases, they seamlessly link to smart environments, introducing innovative and efficient user interfaces. Nevertheless, these devices present their own set of challenges and limitations. The distinctive features of this technology, particularly its hand-free operation handled by voice and implementation of voice-user interface create a unique landscape. Existing technology adoption models, however, fall short in offering a comprehensive explanation for the adoption of this innovative technology. To gain deeper insights into the motivation behind approving and using in-home voice assistants, this work shifts its focus towards examining voice interactions. Utilization of speech recognition for converting speech into text enables constructing an attractive personalized assistant. This development facilitates seamless tasks such as sending emails without manual typing, conducting Google searches without opening the browser, and accomplishing various daily activities including playing music or launching a preferred IDE, all with a single voice command. To assess its performance, the system is seamlessly integrated with home automation (open-source) environment and operated continuously for some days. During this period, users are encouraged to interact with it, and the results demonstrate increased accuracy, reliability and appeal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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Score-Based Generative Model With Conditional Null-Space Learning for Limited-Angle Tomographic Reconstruction in Medical Imaging

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 556-569页

作者： Zhang, Genyuan Wang, Zihao Yu, Haijun Ni, Song Xie, Haixia Liu, Qiegen Liu, Fenglin Wang, Shaoyu Chongqing University Key Lab of Optoelectronic Technology and Systems Engineering Research Center of Industrial Computed Tomography Nondestructive Testing Ministry of Education Chongqing400044 China Nanchang University Department of Information Engineering Nanchang330031 China

Limited-angle computed tomography (LA-CT) reconstruction represents a typically ill-posed inverse problem, frequently resulting in reconstructions with noticeable edge divergence and missing features. Score-based generative models (SGMs) based reconstruction methods have shown strong ability to reconstruct high-fidelity images for LA-CT. Data consistency is crucial for generating reliable and high-quality results in SGMs-based reconstruction methods. However, existing deep reconstruction methods have not fully explored data consistency, resulting in suboptimal performance. Based on this, we proposed a Conditional Score-based Null-space (CSN) generative model for LA-CT reconstruction. First, CSN integrates prior physical information of limited-angle scanning as conditional constraint, which can enable SGMs to obtain more accurate generation. Second, in order to balance the consistency and realness of the reconstruction results, the range-null space decomposition strategy is introduced in the sampling process. This strategy ensures that the estimation of the information occurs only in the null-space. Finally, we employ the sparse least square (LSQR) instead of commonly used consistency terms such as simultaneous iterative reconstruction technique (SIRT), thereby achieving superior reconstruction results. In addition, a mathematical convergence analysis of our CSN method is provided. Experimental evaluations on both numerical simulations and real-world datasets demonstrate that the proposed method offers notable advantages in reconstruction quality. © 2015 IEEE.

关键词： Iterative methods

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Enhancing Skin Disease Diagnosis: Light GBM-DMS Algorithm for Accurate Image Classification

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Cognitive Computation 2025年第3期17卷 1-18页

作者： Kalpana, B. Senthilselvi, A. Muruganandam, S. Kumar, S. Vinodh Department of Information Technology RMD Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Department of Computer Science and Business Systems Panimalar Engineering College Poonamallee Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Thandalam Chennai India

Globally, skin diseases are emerging as the most common health problem. It initiates depressive disorder, and it also causes physical health distress. It rarely led to skin cancer in extreme cases. Diagnosing skin disease is subjective and time-consuming when it is performed by health experts manually. Hence, automatic skin disease prediction is required by both dermatologists and patients to enhance and accelerate treatment plans. Even though many machine-learning techniques are available to detect skin diseases, the timely prediction and accurate classification of skin lesions are still in demand. Hence, the Discrete Mycorrhizal Search Algorithm is used to optimize the novel LightGBM to meet this objective. Firstly, intrusions like noise elimination and hair removal are performed and then the image resizing takes place by utilizing the preprocessing pipelines. Subsequent to data pre-processing, the prime features are extracted using the convolutional self-attention based L2 regularization, thus minimizing the data complexity. At last, the extracted features are shifted to the final categorization phase which is done by the novel approach for effective classification of skin diseases. Two skin disease classification images, including datasets, the Skin Lesions Dermatoscopic Image dataset and Skin Cancer MNIST HAM10000, are selected to validate the classification performance. Comprehensive analyses are performed with significant measures that showed the proposed model attained better performances of 98.6%, 0.95, and 10.43 s from accuracy, intersection of union, and execution time, respectively. The stability and robustness under different environmental conditions for clinical applications in skin disease detection and classification tasks are analyzed for making decisions in real-time scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Dermatitis

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Efficient Parameter Determination for BPDN in Underwater Acoustic MIMO-OSDM Communications Using Stein's Unbiased Risk Estimate

Efficient Parameter Determination for BPDN in Underwater Aco...

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2025 IEEE Underwater technology, UT 2025

作者： Ishijima, Ryoichi Ebihara, Tadashi Wakatsuki, Naoto Maeda, Yuka Mizutani, Koichi Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (纸本)9798331534189

Underwater acoustic (UWA) communication schemes including multiple-input-multiple-output orthogonal signal division multiplexing (MIMO-OSDM) are crucial for the wireless operation of autonomous unmanned vehicles and remotely operated vehicles. This paper presents an efficient regularization parameter determination for MIMO-OSDM to design a reliable communication system. Recent studies have revealed that utilizing sparsity, a physical nature of the UWA channel can improve communication quality. While the combination of MIMO-OSDM and basis pursuit denoising (BPDN) enables the UWA communication that accounts for channel sparsity, this approach requires the prior simulation to obtain a database to fine-tune linear coefficients which decide the regularization parameter of BPDN. To reduce computational cost without sacrificing communication quality, we propose the statistical solution: an application of Stein's unbiased risk estimate (SURE) for MIMO-OSDM using BPDN aiming for parameter determination through a minimization of the channel estimation error. Our simulation results demonstrated that the proposed parameter determinator serves as an estimator of channel estimation error. Our experimental results demonstrated that the proposed channel estimator using SURE achieved almost the same communication quality and demodulation performance as the existing method without prior database creation. © 2025 IEEE.

关键词： Risk perception

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A Novel Framework for Gauging information Extracted from Smartphones Using Neutrosophic Logic

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Neutrosophic Sets and systems 2025年 76卷 154-171页

作者： Abou Alzahab, R.M. Ismail, Amr Abd Elkhalik, S.H. Shams, M.Y. El-Bakry, H.M. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Department. of Communication and Computer Engineering Air Defense University Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Information Systems. Faculty of Computer and Information Science Mansoura University Egypt

Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and use the most relevant information. Neutrosophic logic is a generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool for dealing with the ambiguity of real-world data. The proposed framework aims to identify and utilize the most relevant information for forensic investigators, making it easier to solve criminal cases using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity, or Indeterminacy of this information. Additionally, this study analyzes conversations between individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in each conversation and which can be used to determine its admissibility as evidence and which not. © 2025, University of New Mexico. All rights reserved.

关键词： Smartphones

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Cost-Optimized Task Offloading for Dependent Applications in Collaborative Edge and Cloud Computing

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IEEE Internet of Things Journal 2025年第9期12卷 12975-12988页

作者： Yuan, Haitao Hu, Qinglong Wang, Shen Bi, Jing Buyya, Rajkumar Lu, Jinhu Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Laboratory School of Computing and Information Systems MelbourneVIC3010 Australia CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Lyle School of Engineering Department of Computer Science DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm called Genetic Simulated-annealing-based Particle swarm optimization with Auto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs. © 2014 IEEE.

关键词： Mobile edge computing

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Robust Underwater Positioning System Using LED Markers and Perspective-n-Point Solver

Robust Underwater Positioning System Using LED Markers and P...

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2025 IEEE Underwater technology, UT 2025

作者： Kitano, Emi Ebihara, Tadashi Wakatsuki, Naoto Maeda, Yuka Graduate School of Science and Technology University of Tsukuba Ibaraki Japan Institute of Systems and Information Engineering University of Tsukuba Ibaraki Japan

ISBN: (纸本)9798331534189

Swarm operations of underwater robots are gaining attention as a means of performing more complex and efficient tasks underwater. For successful operation, each underwater robot must know its position and pose. Traditional underwater acoustic positioning using ultrasonic waves has the advantage of providing wide-area coverage. However, the large size of those systems makes them difficult to mount on smaller robots such as those used in underwater robot swarms. This paper proposes a new simultaneous position and pose measurement system suitable for underwater robot swarm operations. In this system, a camera is mounted on each robot, and the relative position and pose of the light-emitting diodes (LEDs) are based on the coordinates of several known LEDs and the images captured by the camera. The effectiveness of the proposed positioning system using LEDs and cameras is verified through experiments in a test tank. © 2025 IEEE.

关键词： Cameras

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RGB Pixel Combination Method for Underwater Camouflaged Image Categorization using Graph Neural Networks

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IEEE Access 2025年 13卷 84341-84356页

作者： Dhamodharan, K. Pandi, Thanapal Vellore Institute of Technology School of Advanced Sciences Department of Mathematics Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore India

Underwater imaging applications require sophisticated image processing techniques aided by Computer Vision (CV). The intensity and light variations under the water demand camouflaged object, plant, and living organism detection in deep seas. This article introduces the Edge-Recognition Method using Graph Neural Networks (ERM-GNN) to address visual differentiations of such particles with precise categorization. This method is reliable in detecting camouflaged object edges and classifying their foregrounds and backgrounds. In the edge detection and object classification process, color features are utilized, relying on RGB variants observed near the edges of the foreground and background images. The edge depth intensity feature is trained repeatedly using the GNN, forming layers between the initial and final pixels of the regions. This approach imposes an xC_y pixel combination to analyze depth intensity until the foreground and backgrounds are categorized. The neural network trains the hidden layers by differentiating categorized and non-categorized RGB variants for in-depth detection. Consequently, this combination increases the chances of camouflaged underwater object categorization with better precision. This method outperforms state-of-the-art approaches such as G+L-CET, FEGM, and MFEM by a wide margin, improving recognition accuracy by 14.48%, precision by 12.81%, and object classification by 9.82%. These noteworthy improvements highlight the method's efficacy and potential use in marine biology and environmental surveillance, necessitating accurate underwater object detection. © 2013 IEEE.

关键词： Camouflaged Image CV Depth Intensity GNN Underwater Objects

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Specification of Complex Analytics Workflows: A Formal Language Model of Decision Options 20th

Specification of Complex Analytics Workflows: A Formal Lang...

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20th International Conference on the Economics of Grids, Clouds, systems, and Services, GECON 2024

作者： Miri, Pouriya Kochovski, Petar Tuler de Oliveira, Marcela Stankovski, Vlado Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Department of Engineering Systems Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031812255

The specification of experiments expressed as Complex Analytics Workflows is a complex task that involves many decision-making steps with various degrees of complexity. The use of the context, the expert knowledge, and the potential for its sharing and reuse in the context of experiment specification have not been addressed sufficiently until now. Moreover, to make such knowledge instrumental, it should be coupled with specific probabilistic measures, such as particular assurances, ranking, and verification of various options. The paper aims to present a novel semantic model for probabilistic reasoning in any experimentation context coupled with a functional system for knowledge generation, reuse, and sharing. The result of our work can be used within existing experimentation engines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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Empirical techniques for effort estimation in designing effective ML models

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International Journal of Computers and Applications 2025年第2期47卷 226-234页

作者： Padmanabha Reddy, Y.C.A. Kosuru, Sai Sathwik Sirisalla, Nageswara Rao Vivekananda, G.N. Perala, Venkata Akanksha Department of Artificial Intelligence and Machine Learning Chaitanya Bharati Institute of Technology Telangana Hyderabad India Department of CSE B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering K.S.R.M. College of Engineering AP Kadapa India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Machine Learning Research often involves the use of diverse libraries, modules, and pseudocodes for data processing, cleaning, filtering, pattern recognition, and computer intelligence. Quantization of Effort Required for the above cumulative processes is rarely discussed in the existing works and The time to reach the desired level of the model's functionality is essential to gauge the environment training for the model training and pre-deployment testing. In this study, we empirically defined the manual-cum-computational effort required for the model development in terms of 2-time factors: time-to-live (time until 1st code modification) and time-for-modification (time between 2 codebase changes) taken together in a mathematical model to compute Effort Factor(quantitative measure of determining effort needed to design ML algorithms). The study is novel in terms of how the effort required to create ML models is calculated with a particular focus on the manual effort direction. The results and the findings obtained can be used for determining the total time needed to synthesize ML models and frameworks in terms of code change cycles and implementation strategies marking a 25% performance increase in the current method above the Standard Pipeline. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pipeline codes

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