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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Ramalakshmi, V. Garg, Neha Soudagar, Manzoore Elahi M. Vekariya, Daxa Patil, Harshal Arun Shalin, L.V. Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering and Technology Department of CSE Andhra Pradesh Vijayawada521225 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat 391760 India Computer Science and Engineering Maharashtra Pune412115 India Bannari Amman Institute of Technology Department of Information Technology Tamil Nadu Erode638401 India

ISBN: (纸本)9798350327533

The electrocardiogram (ECG) is an essential instrument used by physicians that provides information about cardiovascular health and is the gold standard for detecting abnormal heartbeats. Traditional medical tests have limitations, which include delayed diagnoses and a high potential for misdiagnosis, especially when after the attention to cardiovascular health grows. Given the importance of early detection, medication, and rehabilitation for cardiovascular diseases (CVD), the rate of misinterpreting must be reduced. This research study develops a CNN-BLSTM (Convolutional Neural Network and Bidirectional Long Short-Term Memory), hybrid Deep Learning (DL) model for categorizing both normal and abnormal cardiac data. The proposed technique is evaluated using ECG signals from the MIT-BIH arrhythmia database, which comprises both normal and five types of abnormal signals. Further, this research study compares the performance of our model to that of existing transfer learning models such as ResNet-50 and AlexNet. With an overall accuracy of 98.11%, the proposed CNN-BLSTM model surpasses both ResNet-50 and AlexNet. Furthermore, the model delivers the greatest prediction scores for each type of heartbeat classification, indicating its effectiveness in accurate and comprehensive heartbeat signal analysis. © 2024 IEEE.

关键词： Cardiovascular Disease Deep Learning Electrocardiogram Heartbeat Normalization

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A Novel Technique to Parameterize Congestion Control in 6TiSCH IIoT Networks

arXiv

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arXiv 2024年

作者： Chakraborty, Kushal Dutta, Aritra Kumar Hussain, Mohammad Avesh Mohiuddin, Syed Raafay Choudhury, Nikumani Matam, Rakesh Mukherjee, Mithun Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Hyderabad Pilani India Dept. of Computer Science & Engineering Indian Institute of Information Technology Guwahati India Nanjing University of Information Science and Technology Nanjing China

The Industrial Internet of Things (IIoT) refers to the use of interconnected smart devices, sensors, and other technologies to create a network of intelligent systems that can monitor and manage industrial processes. 6TiSCH (IPv6 over the Time Slotted Channel Hopping mode of IEEE 802.15.4e) as an enabling technology facilitates low-power and low-latency communication between IoT devices in industrial environments. The Routing Protocol for Low power and lossy networks (RPL), which is used as the de-facto routing protocol for 6TiSCH networks is observed to suffer from several limitations, especially during congestion in the network. Therefore, there is an immediate need for some modifications to the RPL to deal with this problem. Under traffic load which keeps on changing continuously at different instants of time, the proposed mechanism aims at finding the appropriate parent for a node that can forward the packet to the destination through the least congested path with minimal packet loss. This facilitates congestion management under dynamic traffic loads. For this, a new metric for routing using the concept of exponential weighting has been proposed, which takes the number of packets present in the queue of the node into account when choosing the parent at a particular instance of time. Additionally, the paper proposes a parent selection and swapping mechanism for congested networks. Performance evaluations are carried out in order to validate the proposed work. The results show an improvement in the performance of RPL under heavy and dynamic traffic loads. © 2024, CC BY.

关键词： Internet of things

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Blockchain based Secure Data Storage Verification Algorithm for Smart City Environment 4

Blockchain based Secure Data Storage Verification Algorithm ...

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4th International Conference on Innovative Trends in information technology, ICITIIT 2023

作者： Vivekanandan, Manojkumar Premkamal, Praveen Kumar Johnpaul, C.I. Ebinazer, Silambarasan Elkana School of Engineering and Applied Sciences SRM University-AP Department of Computer Science & Engineering Andhra Pradesh Amaravati522502 India Kamaraj College of Engineering and Technology Department of Computer Science and Engineering Virudhunagar India St. Thomas College Thrissur India Indian Institute of Information Technology Kottayam Kottayam India

ISBN: (纸本)9781665494144

Blockchain based distributed ledger mechanism has got a wide range of applications in this era. The degree of security measurement is always a bottleneck. Since there are technologies to break it. Data sharing through cloud for smart cities, collaborative actions, remote activities based on the data at the source, etc., need to be secure and free from masquerading and tampering. In most of the cases the data is pushed into the cloud from access points, sensors, or remote access centers. Preventing the data access and identifying anonymous access to these sensors require an enhanced security mechanism that prevents the inconsistent data to be transferred to the cloud. We propose a blockchain based enhanced security system that protects the data from the access point it leaves for the cloud using a distributed ledger. The consensus mechanism ensures the trust of existing sources during the data transfer from the source to the cloud. The trust generated by the subsequent data blocks with the security hash key ensure the integrity of the data and validity of the actual source. This prevent the illegal access to the data sharing points. We have verified the degree of security offered by our proposed model using informal analysis. We found that our method has improved the security of data access. © 2023 IEEE.

关键词： Blockchain

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Fault prediction using fuzzy convolution neural network on IoT environment with heterogeneous sensing data fusion

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Measurement: Sensors 2023年 26卷

作者： Rajput, Dharmendra Singh Meena, Gaurav Acharya, Malika Mohbey, Krishna Kumar School of Information Technology and Engineering VIT Vellore India Department of Computer Science Central University of Rajasthan Ajmer305817 India

Using multi-source sensing data based on the Internet of things and fusion in conjunction with fuzzy convolutional neural networks to classify and predict mechanical failures has emerged as a very pertinent area of research. This study presents a fuzzy convolutional neural network-based technique to extract and diagnose the defect indicators utilizing the bearing dataset from Case Western Reserve University (CWRU). In addition, the proposed model's effectiveness is evaluated with the help of a model based on a direct convolution neural network. The strategy based on Fuzzification yielded superior results when applied to a fully linked layer for classification. When applied to the bearing dataset, the proposed approach produces an average classification accuracy of 99.87% using the fuzzy layer. The proposed method is evaluated compared to other methods based on machine learning and deep learning. The accuracy achieved using the proposed method is superior to that achieved using the existing approaches in every imaginable working environment. The proposed approach can be used to diagnose mechanical motor faults and provide earlier recommendations. © 2023 The Authors

关键词： Fuzzy inference

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AI Based Electric Automobile Battery Drain Forecasting System 4

AI Based Electric Automobile Battery Drain Forecasting Syste...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Santhosh, B.J. Benvin, Shalet Arvind, R. Aswini, V. Maranan, Ramya Thrinath, B.V. Sai Bgs Institute of Technology Department of Computer Science and Engineering Karnataka Mandya571448 India CMR Institute of Technology Department of Information Science and Engineering Karnataka Bangalore560037 India Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai600044 India Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India School of Engineering Mohan Babu University Department of Electrical and Electronics Engineering Andhra Pradesh Tirupati517102 India

ISBN: (纸本)9798350300888

While electric vehicles (EVs) are gaining appeal as a sustainable mode of transportation, issues like range anxiety and battery depletion prevent them from being widely adopted. To better plan for charging stops, we created a model that takes into account the time of day, the length of the trip, and the temperature outside. We constructed an accurate battery drain prediction model using machine learning methods, specifically the Support Vector Machine (SVM) algorithm. With strong correlation and low error rates, our findings show that the SVM algorithm is ideal for this job. Our findings might be useful to EV owners and fleet managers as a resource for reducing wasteful driving and maximizing range. Future work might examine the effect of battery deterioration on electric vehicle performance, leading to more nuanced models for maximizing EV efficiency and reducing transportation emissions. © 2023 IEEE.

关键词： Support vector machines

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Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

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computers, Materials & Continua 2022年第9期72卷 4249-4265页

作者： Muhammad Irfan Ali Raza Faisal Althobiani Nasir Ayub Muhammad Idrees Zain Ali Kashif Rizwan Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Saifur Rahman Omar Alshorman Samar Alqhtani Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadWah Campus22060Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and TechnologyIslamabad44000Pakistan School of Electrical Engineering and Computer Science National University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial ***,electricity demand and price forecasting play a significant role and can help in terms of reliability and *** to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG *** this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as *** minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant *** this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the ***,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test *** evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark *** proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.

关键词： Smart grid deep neural networks consumer demand big data analytics load forecasting price forecasting

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Resource Allocation in Cloud Computing Using Quasi-Opposition Learning Based Aquila Optimizer

Resource Allocation in Cloud Computing Using Quasi-Oppositio...

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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Kumari, K. Aruna Muvva, Vijaya Bhaskar Reddy Kumar, K. Sudheer Rai, B. Karunakara Shakir, Aboothar Mahmood Department of CSE SRKR Engineering College Bhimavaram Chinaamiram India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences -Shinas Shinas Oman School of Computer Science and Artificial Intelligence SR University Warangal India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9789819778751

The resource allocation is focus on enhancing the service provider proceedings and obtaining customer satisfaction by Service Level Agreements (SLA). Cloud technology has significantly transformed the organizations to manage and allocate their resources. The cloud users are choosing sources of application distribution exclusive of cooperating Quality of Service (QoS) necessities. To overcome this issue, this paper proposed a Quasi-Opposition Learning based Aquila Optimizer (QOL-AO) for resource allocation in cloud. The virtualization supports to implement task using resource availability and reduces the response time. The QOL-AO employs the hunting approach for obtain the effective and efficient resource allocation in cloud. The Performance of QOL-AO is estimate through execution time, response time and energy consumption for various number of Virtual Machines (VMs). The QOL-AO attained less execution time of 18.568 ms and response time of 0.095 s than existing algorithms like Emperor Penguin Optimization (EPO) and Particle Swarm Optimization (PSO). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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SSCCPC-Net: Simultaneously Learning 2D and 3D Features with CLIP for Semantic Scene Completion on Point Cloud 41st

SSCCPC-Net: Simultaneously Learning 2D and 3D Features with...

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41st computer Graphics International Conference, CGI 2024

作者： Duan, Wantong Bao, Yongtang Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Shandong University of Science and Technology Qingdao266590 China Jingdezhen Research Institute of Beihang University Jingdezhen333000 China Qingdao Research Institute of Beihang University Qingdao266000 China

ISBN: (纸本)9783031820236

Compare to traditional Scene Completion (SC), Semantic Scene Completion (SSC) is a challenging task that aims to generate complete and semantically consistent 3D scene from partial and sparse input data, which is fundamental to fully understanding the scene and being able to interact with it. Consequently, the SSC task has received much attention in recent years. Most of the methods are voxel-based approaches, but they have high computational and memory requirements. A few works based on point cloud do not sufficiently exploit the correlation between semantic segmentation and geometric completion subtasks, while focusing too much on point cloud shape features and ignoring the rich texture information that RGB images can provide. In this paper, we present SSCCPC-Net (Semantic Scene Completion with CLIP on Point Cloud-Net), a novel network architecture for point cloud semantic scene completion using a combination of 2D and 3D features. Inspired by recent works of large pretrained vision-language models in semantic segmentation, we explore to accomplish SSC task with the help of Contrastive Language-Image Pre-Training (CLIP) model. Specifically, we use the CLIP features for guidance to fuse the 2D features extracted from the RGB image and the 3D features extracted from the point cloud. The fused features are then fed into our designed Semantic-Completion Decoder for per-point semantic prediction and semantic labeling-assisted point cloud completion. Finally, we obtain the complete semantically point cloud. Numerous experiments have demonstrated that our method has higher effectiveness and generalizability compared to state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Deep Learning and IoT Based Assistance System for Autism Spectrum Disorder People 4

Deep Learning and IoT Based Assistance System for Autism Spe...

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4th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2022

作者： Swaminathan, K. Veeman, S. Dhilip Kumar, T. Ezhilarasi, S. Bhuvaneswari, P.T.V. Br, Kavitha Ramesh, Parameswaran Anna University Madras Institute of Technology Department of Electronics Engineering Chennai India Anna University Madras Institute of Technology Department of Computer Technology Chennai India Vellore Institute of Technology School of Information Technology and Enginering Chennai India

ISBN: (纸本)9781665494113

During the Covid-19 pandemic situation, the economic and social disruption is devastating. People could not get out of their homes and lead their normal life. schools across the country have switched to remote learning, which is also inevitable. Though there are some advantages in the online classes, the fact that many of the children suffered mental stress due to these classes could not be denied. Especially, when speaking about the autism people, they could not handle the stress like normal children. The caretaker is necessary to assist them all the time. Therefore, an assistance system is needed to monitor the stress level. In this research, prototype of deep learning and Internet of Things (IoT) based assistance system has been proposed. It monitors the stress parameters namely body temperature, pulse rate, skin conductivity and the facial emotion of the autism disorder people. Further, the hardware model (Raspberry Pi) has been developed to measure the stress level. The processed data from the model has been stored in the Thingspeak cloud platform for monitoring the autism people remotely. From the threshold stress parameters, the level of the stress can be predicted by the proposed stress management algorithm. © 2022 IEEE.

关键词： Internet of things

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LPCC Based Music Genrefication Using Hybrid Computational Model

LPCC Based Music Genrefication Using Hybrid Computational Mo...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Astha Sul Harshit Sarda Aishwarya Billa Tusar Kanti Mishra School of Computer Science and Engineering Vellore Institute of Technology Vellore India Dept. of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

This work deals with spontaneous music genrefication through computational models which in the recent times has been gaining importance rapidly. Through these hybrid computational models implemented users get an enhanced level of satisfaction when their choice of music files genre with least latency is gained. The paper includes the use of LPCC attributes to obtain the features of the music files along with robust classification models such as neural networks. For this purpose, one thousand audio files are taken as the sample set. The input audio file is pre-processed and suitable LPCC features are computed and stored into final vector. The pool of vectors of numerous audio samples are finally utilized to train a neural model. The proposed work trains the model for ten very distinct categories of music such as hip-hop, jazz, metal, pop, blues, classical, country, disco, reggae, and rock. Further, a comparison is also made between all the classifiers such as SVM, ANN and random forest (RF). Comparatively the best accuracy rate of 85.53\% has been achieved for the proposed work that validates its effectiveness.

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