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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lenin, J. Rohit, V.Robin Nisha, F. Vasudevan, B. Sivakumar, S. Murugan, S. Alliance College of Engineering and Design Alliance University Department of Computer Science & Engineering Bangalore India Presidency University Computer Science & Engineering School of Computer Science & Engineering and Information Science Bengaluru India Cmr University School of Engineering and Technology Computer Science & Engineering Bangalore India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur SRM Nagar Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

This research introduces a new approach to radon detection in homes utilizing Decision Trees (DTs) enabled by the cloud in real-time. High radon levels, a natural radioactive gas, are dangerous to human health. Quick evaluation and mitigation efforts are hindered by the long processes required by traditional detection systems. Using DTs, a machine learning methodology, the proposed method sorts of radon levels according to variables, including building materials, ventilation systems, and geographic location. These trees may handle both numerical and categorical data and provide interpretability. Fast processing of massive data sets and real-time analysis across varied residential areas are made possible by integrating cloud computing, increasing efficiency and scalability. Stakeholders may remotely access crucial information due to the solution's accessibility and flexibility. Results from controlled experiments demonstrate that this method can accurately identify radon levels in the air in real time, allowing for preventative actions to lessen potential health hazards. People living in areas prone to radon may benefit from this practice in terms of their health. © 2024 IEEE.

关键词： Indoor air pollution

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Optimizing Content-Based Image Retrieval System Using Convolutional Neural Network Models 8th

Optimizing Content-Based Image Retrieval System Using Convol...

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8th International Conference on information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Kashid, Shraddha S. Takale, Dattatray G. Gawali, Piyush P. Deshmukh, Gopal B. Mahalle, Parikshit N. Sule, Bipin Deshpande, Arati V. Salve, Bhausaheb S. Department of Computer Engineering MIT ADT University Pune India Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune India and Humanities Vishwakarma Institute of Technology Pune India Computer Engineering Department Sandip University Mahiravani Trimbak Road Maharashtra Nashik India

ISBN: (纸本)9789819766741

This study focuses on improving "Content-Based Image Retrieval" (CBIR) systems through the utilization of optimized convolutional neural network (CNN) models. Traditionally, CBIR relied on text-based approaches, which proved inefficient in the digital era. In this research, we propose a novel visual content-based retrieval method, leveraging key image features like color, texture, and shape. To expedite the process, we employ pre-trained CNN models, such as the ResNet architecture, trained on extensive datasets like Image-Net. Our experiments, conducted on the "Paris 6K and Oxford 5K datasets, demonstrate the superior performance" of the ResNet model compared to the state-of-the-art AlexNet. This is attributed to ResNet’s ability to reduce training time without sacrificing accuracy. The evaluation, based on average mean precision, yielded promising results of 92.12% for Paris 6K and 84.81% for Oxford 5K, showcasing the efficacy of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural network models

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Machine Learning-Based Framework for Cybersecurity of Robotic System 6th

Machine Learning-Based Framework for Cybersecurity of Roboti...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Verma, Narinder Kumar, Neerendra Salotra, Harish Department of Computer Science and Information Technology Central University of Jammu Jammu181143 India

ISBN: (纸本)9789819778614

Robotic systems are increasingly integrated into sectors ranging from manufacturing to health care, presenting significant cybersecurity challenges. Ensuring the security and integrity of these systems is critical to prevent malicious attacks that could compromise functionality and safety. This study introduces a machine learning-based framework designed to enhance the cybersecurity of industrial robotic systems. Leveraging the CICIDS 2017 dataset, which provides a comprehensive collection of network traffic data representing modern cyber threats, the framework demonstrates high efficacy in detecting and mitigating such threats. Experimental results show that the K-Nearest Neighbors and Decision Tree algorithms achieve accuracy rates of 99.01% and 99.83%, respectively, underscoring the potential of these techniques for real-time intrusion detection. These findings highlight the framework's effectiveness in protecting robotic systems against cyber-attacks, thereby fortifying their security in various application domains. The findings highlight the framework effectiveness in protecting robotic systems against cyber-attacks, thereby fortifying their security in various application domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cyber attacks

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Utilization of Processing of Images in Bio-Acoustics

Utilization of Processing of Images in Bio-Acoustics

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Raja Praveen, K.N. Shrimal, Gajendra Shukla, Meenu Computer Science Department Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

The present research examines how the Felsenstein approach might be improved in light of huge data and evolutionary inference. The Felsenstein approach, phylogeny starting points, huge data, statistical techniques, and testing hypotheses are among the important ideas and terms covered in the overview. It emphasizes the importance of repeatability in evolutionary inferences as well as the difficulties brought on by rogue genera and bootstrapping proportions. The article suggests a brand-new strategy to increase the accuracy of phylogenetic investigations that makes use of transfer separation and bipartition distance. The abstract's conclusion emphasizes the need of better methods for managing huge datasets and for recognizing instability taxonomic. This study dives into the refining of the Felsenstein technique within the setting of vast data and evolution prediction. Essential ideas comprise the Felsenstein method, evolutionary points of departure, problems associated with huge datasets, methods in statistics, and testing hypotheses. Noteworthy focus is made on the relevance of reproducibility in phylogenetic conclusions, along with the complexity provided by rogue families and ramping percentages. The article provides a unique technique targeted at boosting the accuracy of phylogenetic studies, utilising transfer isolation and bipartition distances. The summary continues by highlighting the essential need for sophisticated approaches capable of successfully handling big datasets and spotting taxonomic fragility. © 2023 IEEE.

关键词： ABC Big data Bipartition distance Bootstrap Confidence intervals Felsenstein method Homogenization Hypothesis testing Phylogenetic estimation Phylogenetic starting Reboot method Rogue taxa Statistical approach Taxonomic units Transfer distance

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Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

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computers, Materials & Continua 2022年第11期73卷 3351-3365页

作者： R.Rajender C.S.S.Anupama G.Jose Moses E.Laxmi Lydia Seifedine Kadry Sangsoon Lim Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Engineering Sungkyul UniversityAnyang14097Korea

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile *** encompasses several heterogeneous resource and communication standard in ensuring incessant availability of *** the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,*** UAV networks,energy efficiency and data collection are considered the major process for high quality network *** these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted *** issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G *** this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G *** proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets *** presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct *** QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of *** performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

关键词： 6G unmanned aerial vehicles resource allocation energy efficiency artificial intelligence clustering data collection

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Emotional Synchronization for Audio-Driven Talking-Head Generation 24

Emotional Synchronization for Audio-Driven Talking-Head Gene...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Zhao Luo, Yan Zuo, Zhichao Hong, Richang Yang, Yi Wang, Meng School of Computer Science and Information Engineering University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China School of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798331506681

Audio-driven talking-head synthesis has become a significant focus in the field of virtual human applications. However, existing methodologies face challenges in effectively synchronizing audio and video, especially in maintaining emotional consistency. Additionally, there is a notable inefficiency in leveraging emotional prompts to guide expression generation. To address these limitations, this paper introduces an Emotion Synchronized audio-driven Talking-head synthesis (EST) approach. The EST approach aims to enhance the emotion-agnostic talking-head models by enabling emotion control, and it incorporates a diffusion module to learn diverse latent rep-resentations. Furthermore, EST utilizes null-text embedding to align the latent code with emotional prompts. Additionally, a novel Sync Attention Block (SAB) is developed to broaden the spatial perceptual field, thus preventing the loss of critical information. Extensive experiments demonstrate the effectiveness of the EST method, showcasing state-of-the-art performance across widely-adopted datasets. Moreover, the EST approach exhibits exceptional generalization capabilities, even in scenarios where emotional training videos are unavailable. © 2024 IEEE.

关键词： Synchronization

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Towards Dynamic University Course Timetabling Problem: An Automated Approach Augmented via Reinforcement Learning 24

Towards Dynamic University Course Timetabling Problem: An Au...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Xiao, Yanan Li, XiangLin Jiang, Lu Wang, Pengfei Wang, Kaidi Luo, Na School of Computer Science and Information Technology Northeast Normal University China Dalian Maritime University Department of Information Science and Technology China Computer Network Information Center Chinese Academy of Sciences China School of Business Macau University of Science and Technology China

ISBN: (纸本)9798331506681

University Course Timetabling Problem (UCTP) is a significant resource allocation challenge with NP-hard characteristics. As problem sizes increase, finding an optimal solution becomes increasingly complex. To address this, we propose an automated planning method using Reinforcement Learning (RL), which treats UCTP as a series of dynamic decision-making tasks. The RL agent acts as an automated planner, operating in a simulated environment that reflects the complexity and constraints of a university course. It adapts to changes in the timetable by evaluating the outcomes of its actions, continuously refining its strategy to identify and implement the most effective actions. During the explore phase, the agent tests new strategies using a priority-driven reward system and an experience replay mechanism. This approach replays high-value actions, enhancing solution diversity and reducing the search space. In the exploit phase, the agent leverages its accumulated knowledge to apply proven effective strategies. It also incorporates unbiased external evaluations to avoid local optima and ensure global optimization of its strategy. Extensive testing on multiple real-world datasets shows that our automated planning framework consistently outperforms existing methods in various complex scheduling scenarios. This confirms its effectiveness and practicality in meeting diverse scheduling needs. © 2024 IEEE.

关键词： Reinforcement learning

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Leaf Diseases Detection using Android Kotlin App with Deep Learning Algorithm 5

Leaf Diseases Detection using Android Kotlin App with Deep L...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Kori, Daneshwari N. Nikkam, Pushpalatha S. Pujari, Jagadeesh D. Department of Information Science and Engineering SDM College of Engineering and Technology Karnataka Dharwad India

ISBN: (纸本)9798350361155

An Android app is being created using Kotlin to support the agricultural industry. Leaf issues might be brought on by a shortage of nutrients, fungi, diseases, or insects. The most typical bacterial, fungal, and viral diseases. To ddetermine the characteristics of the disease spot quantitatively in accordance with its colour, texture, shape, and other elements. We will find and recognize leaf disease using Deep Learning (DL) approaches. One of the techniques employed in this study to detect leaf disease is deep convolutional neural networks (Deep-CNN) with a pre-trained DL model called ResNet-50. Images are categorized using the TensorFlow image classification model. All of these parameters have been taken into consideration when developing a deep-CNN-based disease detection model for leaves using Keras. The ResNet-50 model then processes the testing data set using the validation parameters it has learned. We may take a leaf image from the camera and choose it from the gallery to use as input for the software, which will then determine what kind of ailment the leaf has as well as whether it is healthy or not. © 2024 IEEE.

关键词： Convolutional neural networks

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Construction of Cascaded Deep Neural Network with Optimization-Based Feature Selection in CT Images for Detecting Laryngeal Cancer 5th

Construction of Cascaded Deep Neural Network with Optimizati...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Naga Padmavathi, P. Sree Devi, M. Maheswara Rao, V.V.R. Gopala Gupta, Amara S. A. L. G. Department of Computer Science and Engineering Amrita Sai Institute of Science and Technology AP Paritala India Department of Computer Science and Engineering Shri Vishnu Engineering College for Women AP Bhimavaram India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation AP Vaddeswaram India

ISBN: (纸本)9789819767311

A sin laryngeal carcinoma is the most common kind of head and neck cancer to damage the soft tissues of the larynx. To prevent further medical difficulties and to provide better patient care, early stage laryngeal cancer identification is essential. The primary objective of this research is to provide computer-aided cancer diagnosis driven by deep learning. In order to do this, we create a brand-new Optimization-Based Cascaded Deep Neural Network (OCDNN) that distinguishes between good and unhealthy/cancerous tissues. Additionally, we also provide cascaded patch-based CNN (CP-CNN) picture segmentation. The models are assessed using accepted measures that are unique to the biomedical field. When compared to other models, the performance of our suggested OCDNN architecture was exceptional. Additionally, it fared better than other cutting-edge CNN models applied to medical domain challenges. The findings suggest that the suggested method is quite successful in identifying early indications of laryngeal cancer. The results demonstrate that the suggested strategy may help doctors identify laryngeal carcinoma early and precisely. The suggested OCDNN is discovered to attain 98.4% accuracy, 94% precision, 97.5% recall, and 98% *** paragraph of about 100 words to give a brief introduction to your work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computerized tomography

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3D shape knowledge graph for cross-domain 3D shape retrieval

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CAAI Transactions on Intelligence technology 2024年第5期9卷 1199-1216页

作者： Rihao Chang Yongtao Ma Tong Hao Weijie Wang Weizhi Nie School of Microelectronics Tianjin UniversityTianjinChina School of Life Sciences Tianjin Normal UniversityTianjinChina Department of Information Engineering and Computer Science University of TrentoTrentoItaly School of Electrical and Information Engineering Tianjin UniversityTianjinChina

The surge in 3D modelling has led to a pronounced research emphasis on the field of 3D shape *** contemporary approaches have been put forth to tackle this intricate ***,effectively addressing the intricacies of cross-modal 3D shape retrieval remains a formidable undertaking,owing to inherent modality-based *** authors present an innovative notion—termed“geometric words”—which functions as elemental constituents for representing entities through *** establish the knowledge graph,the authors employ geometric words as nodes,connecting them via shape categories and geometry ***,a unique graph embedding method for knowledge acquisition is ***,an effective similarity measure is introduced for retrieval ***,each 3D or 2D entity can anchor its geometric terms within the knowledge graph,thereby serving as a link between cross-domain *** a result,the authors’approach facilitates multiple cross-domain 3D shape retrieval *** authors evaluate the proposed method's performance on the ModelNet40 and ShapeNetCore55 datasets,encompassing scenarios related to 3D shape retrieval and cross-domain ***,the authors employ the established cross-modal dataset(MI3DOR)to assess cross-modal 3D shape *** resulting experimental outcomes,in conjunction with comparisons against state-of-the-art techniques,clearly highlight the superiority of our approach.

关键词： 3‐D multimedia

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