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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Tiwari, Shivam Bhushan, Aditya Singh, Ashutosh Kumar Yadav, Rakesh Kumar United College of Engineering & Research Department of Mechanical Engineering Prayagraj India United College of Engineering & Research Department of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9798350309409

This research paper explores the transformative potential of integrating Internet of Things (IoT) devices into smart home technologies, ushering in a new era of energy-efficient living. Focusing on energy optimization, the paper examines the intricate network of interconnected devices shaping efficient residential ecosystems. Beginning with an investigates specific devices such as smart thermostats and intelligent lighting systems. Through meticulous analysis of user behavior, it unveils how these devices influence and mold household energy consumption. Spotlighting data analytics, the paper reveals the power of information processing for personalized, efficient energy management. Addressing challenges like privacy and interoperability, it proposes innovative solutions for seamless IoT integration. The research concludes with an environmental assessment, emphasizing the symbiotic relationship between IoT and smart homes in fostering a sustainable and eco-friendly future. © 2024 IEEE.

关键词： Internet of things

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Comparative analysis of twelve transfer learning models for the prediction and crack detection in concrete dams,based on borehole images

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Frontiers of Structural and Civil engineering 2024年第10期18卷 1507-1523页

作者： Umer Sadiq KHAN Muhammad ISHFAQUE Saif Ur Rehman KHAN Fang Xu Lerui CHEN Yi LEI School of Computer and Information Science Hubei Engineering UniversityXiaogan 432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan 432000China College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China School of Computer Science and Engineering Central South UniversityChangsha 410083China College of Aviation Zhongyuan University of TechnologyZhengzhou 451191China School of Civil Engineering Central South UniversityChangsha 410083China

Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and *** research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack *** deep-learning models are trained on 192 crack *** research aims to provide up-to-date detecting techniques to solve dam crack *** finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%*** study’s pre-trained designs help to identify and to determine the specific locations of cracks.

关键词： concrete dam borehole closed-circuit television deep learning models crack detection water resources management management

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Comprehensive Analysis on Optimal Routing in Wireless Mesh Network Using Optimization and Machine/Deep Learning Algorithms 3

Comprehensive Analysis on Optimal Routing in Wireless Mesh N...

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

作者： Kumar, Kaata Ranjith Bala, K. Srinivas, Kalyanapu Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Vaagdevi Engineering College Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798331508456

Wireless Mesh Networks (WMNs) is an important component of wireless communications by enhancing the network coverage. The mesh network topology is interconnecting all nodes in the cluster. However, the existing approaches utilized for optimal routing in WMN face challenges such as increased delays, packet loss, network congestion that lead to inefficient data transmission. WMN has an ability to deliver efficient performance even with a large number of devices. Additionally, WMNs support a wide range of applications, by using its effectiveness in various scenarios. The fundamental architecture of a mesh network integrates multiple wireless nodes interconnected through base stations for efficient routing communication. Mesh networks able to adopt different client-based structures, including infrastructure and hybrid configurations. Optimization, learning techniques and Software defined network methods are utilized for efficient optimal routing to enhance the efficiency of data transmission, while reducing delay. In this study, routing-based optimization and Machine (ML), Deep Learning (DL) algorithms are reviewed along with its advantages and limitations. The performance ML or DL model is utilized for optimal routing in WMN is evaluated based on performance metrics such as Delay, Throughput, Packet Delivery Ratio (PDR) and Connected Client Ration (CCR). © 2025 IEEE.

关键词： Wireless mesh networks (WMN)

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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computer Modeling in engineering & sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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Hybrid RMDL-CNN for speech recognition from unclear speech signal

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International Journal of Speech Technology 2025年第1期28卷 195-217页

作者： Bhargava, Raja Arivazhagan, N. Babu, Kunchala Suresh Research Scholar Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Potti Sriramulu Chaluvadi Mallikarjuna Rao college of Engineering and Technology Andhra Pradesh Vijayawada India

ASR is an effectual approach, which converts human speech into computer actions or text format. It involves extracting and determining the noise feature, the audio model, and the language model. The extraction and determination of the noise feature is a crucial aspect of speech recognition, serving as both a process of information compression and signal deconvolution. ASR schemes are mostly employed in smart homes, smart appliances, and biometric schemes. Yet, traditional approaches offer very low performance because of a noisy environment. Moreover, local differences and accents negatively influence the ASR scheme execution during the conversion of the speech signals. This paper introduces a hybrid RMDL-CNN method to address these challenges. At first, the input of unclear speech is carried out by the dataset. Then, signal pre-processing is done by employing a Gaussian filter. After that, voice enhancement is accomplished by employing nonlinear spectral subtraction. Later, the speech word is segmented from the enhanced output based on the Attentional Encoder-Decoder approach and finally, the speech is recognized using the proposed RMDL-CNN. The RMDL-CNN method is devised by the combination of RMDL and CNN. Furthermore, the established RMDL-CNN is accessed for its efficiency based on several values of k-group value, as well as learning data. In addition, the introduced RMDL-CNN approach for speech recognition achieved better accuracy, PPV, as well as NPV of 0.909, 0.947, and 0.917 for dataset 1. Moreover, the RMDL-CNN has achieved the highest accuracy of 0.909, PPV of 0.926 and NPV of 0.888 for dataset 2. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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An Extensive Examination of Different Phases Available in Enhancing the Power of WSN 4

An Extensive Examination of Different Phases Available in En...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Gill, Rupali Kumar, Rakesh Pant, Shivani Deepika, A. Al-Fatlawy, Ramy Riad Angadi, Sanjeevkumar Chitkara University Centre of Research Impact and Outreach Punjab Rajpura140401 India Gla University Department of Computer Engineering & Applications Mathura India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science Engineering Dehradun248007 India India The Islamic University Najaf Iraq Computer Science and Engineering Nutan College of Engineering and Research Pune Maharashtra Talegaon Dabhade India

ISBN: (纸本)9798350360165

In this whole test, we delve into the evolution and transformative ability of Wireless Sensor Networks (WSNs), emphasizing the pivotal position of graphical statistics in elucidating the technological improvements and stressful conditions interior this region. By harnessing the energy of the IEEE 802.15.4 general, this research provides an in-intensity analysis of WSNs' operational efficiencies, energy consumption styles, and network resilience through meticulously designed graphs and information visualizations. These graphical representations now not only offer a easy and concise overview of WSN universal performance metrics but additionally spotlight the giant enhancements completed thru the implementation of a singular WSN framework. The examiner's findings underscore the vital importance of empirical statistics visualization in advancing our knowledge of WSN technologies, offering valuable insights into the optimization strategies that may result in extra power-inexperienced, reliable, and scalable sensor networks. This research contributes to the continued communicate in the subject, paving the manner for future enhancements and programs of WSNs across several sectors. © 2024 IEEE.

关键词： Energy utilization

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A deep learning framework for enhanced mass spectrometry data analysis and biomarker screening

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computer Methods in Biomechanics and Biomedical engineering 2025年 1-13页

作者： Zhang, Shuyu Li, Zhiyu Peng, Weili Chen, Yuanyuan Wu, Yao Machine Intelligence Lab College of Computer Science Sichuan University Chengdu China National Engineering Research Center for Biomaterials School of Biomedical Engineering Sichuan University Chengdu China

Mass spectrometry (MS) serves as a powerful analytical technique in metabolomics. Traditional MS analysis workflows are heavily reliant on operator experience and are prone to be influenced by complex, high-dimensional MS data. This study introduces a deep learning framework designed to enhance the classification of complex MS data and facilitate biomarker screening. The proposed framework integrates preprocessing, classification, and biomarker selection, addressing challenges in high-dimensional MS analysis. Experimental results demonstrate significant improvements in classification tasks compared to other machine learning approaches. Additionally, the proposed peak-preprocessing module is validated for its potential in biomarker screening, identifying potential biomarkers from high-dimensional data. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Liver disease deep learning mass spectrometry pre-processing

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Detection of Manipulated Multimedia In Digital Forensics Using Machine Learning 2

Detection of Manipulated Multimedia In Digital Forensics Usi...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Anvekar, Preetam Gudnavar, Anand Naregal, Keerti Nagarmunoli, Sreedevi Jain College of Engineering and Research Department of Computer Science and Engineering Belagavi India Graphic Era University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350372847

The surge in digital media usage has spurred an uptick in multimedia manipulation., spanning images., videos., and audio. This manipulation., with its potential to spread misinformation and manipulate public opinion., poses serious threats. Detecting genuine from fake media is challenging due to the diverse tools employed. Consequently., cybercrime involving manipulated media is on the rise. researchers are countering this issue with machine learning techniques., particularly Convolutional Neural Networks (CNNs). This paper presents an application leveraging CNN s to identify genuine and fake media., bolstered by results from experiments on real and manipulated datasets., yielding high accuracy and robustness. Deep learning models excel in detecting various manipulation types., positioning them as potent weapons against manipulated content proliferation. To ascertain the models' effectiveness., the study includes comprehensive validation., testing procedures., and robustness analyses against sophisticated manipulations., including adversarial attacks and deepfake variations. This research advances multimedia forensics., offering a holistic approach to detect manipulated media with deep learning models., underscoring CNN s' effectiveness in curbing manipulated content dissemination., and emphasizing the necessity of ongoing advancements to tackle the evolving multimedia manipulation landscape. © 2024 IEEE.

关键词： Digital forensics

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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An Autoencoder-BiLSTM framework for classifying multiple types of lung diseases from CXR images

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Multimedia Tools and Applications 2024年第38期83卷 86091-86120页

作者： Ankayarkanni, B. Sangeetha, P. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Chennai600 119 India Rajiv Gandhi College of Engineering Research and Technology Maharashtra Chandrapur442403 India

Millions of people die from lung illness each year as a result of its rise in recent years. CXR imaging is one of the most widely used and reasonably priced diagnostic techniques for the diagnosis of many illnesses. Unfortunately, even for seasoned radiologists, accurately diagnosing sickness from Chest X-Rays (CXR) samples is challenging. To combat the pandemic, a reliable, affordable, and efficient way to diagnose lung disease has become essential. Consequently, a unique optimized Auto Encod-BI Long-Short Term Memory (Bi-LSTM) model is proposed in this research work. Pre-processing, segmentation, feature extraction, and multiple types of lung illness diagnosis are the four main stages of the suggested model. First, Laplacian filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to pre-process the gathered CXR pictures. Next, the Region of Interest (ROI) from the previously processed images are recognized by means of the newly enhanced MobileNetV2. The new Self-Improved Slime Mould Algorithm (SI-SMA) is used to fine-tune the hyper-parameters of MobileNetV2 in order to precisely identify the afflicted locations. Based on the phenomenon of slime mould oscillation, the conventional Slime Mould Algorithm (SMA) model has been modified with the creation of the SI-SMA model. Next, characteristics like the Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) are taken out. Finally, a unique AutoEncod-BiLSTM Framework—which is divided into three categories—is shown to automate the process of identifying illnesses in CXR pictures: pneumonia, COVID-19, and normal. The autoencoder and Bi-LSTM are combined to create the suggested AutoEncod-BiLSTM model. The retrieved features are used to train the AutoEncod-BiLSTM Framework. Moreover, the proposed model enhanced the disease detection efficiency than the existing models and the disease detection accuracy of the proposed model is about 99.1%. Furthermore, the suggested model attains better

关键词： Equalizers

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