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2nd International Conference for Innovation in technology, INOCON 2023

作者： Battula, Naga Datha Saikiran Kambhampaty, Harish Rohan Vijayalata, Y. Ashlin Deepa, R.N. Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Hyderabad India KG Reddy College of Engineering & Technology Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350320923

An increase in research and awareness of neurological disorders has caused several medical practitioners to consider taking the assistance of AI/ML and reduce the recovery time for patients. MRI-based detection of neurological disorders has become popular in the field of bio-medical analytics. In this paper, a novel two-stage approach was proposed, which split the task of disease identification and severity identification from MRI data. The proposed two-stage approach benefits from improved efficiency due to automated simultaneous testing of multiple diseases. This reduces the time to diagnosis or diagnostic delay. The proposed two-stage approach was able to yield an accuracy of up to 94.29%, which was significantly better than the simpler one-stage approach while also being flexible to future upgrades. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Research on Domain Text Classification Algorithm Based on Multi-Granularity Fusion 24

Research on Domain Text Classification Algorithm Based on Mu...

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4th International Joint Conference on Robotics and Artificial Intelligence, JCRAI 2024

作者： Yao, Xiaotong Wu, Yuntao Yu, Xiaopeng Huang, Yufei School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China School of Management Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798400710100

The current research on domain text classification is primarily based on word granularity to facilitate the extraction and representation of text information. However, this approach is susceptible to the influence of out-of-vocabulary words and is insufficient for inter-word and inter-sentence semantic representation. Given the capacity of sentence template to effectively summarize sentence semantics, this paper proposes a domain text classification algorithm based on multi-granularity fusion. The algorithm introduces sentence template information to enhance text features from a semantic perspective, constructs embedding vectors of characters, as well as word and sentence templates. It extracts the contextual semantic features of these vectors using BiLSTM, further identifies key features of the characters and words through the attention mechanism, and finally, the information at each granularity is fused using the weighted fusion module to obtain the final semantic feature vectors. These vectors are then input into the softmax function for classification prediction. Experimental results demonstrate that the sentence template information significantly improves the model's ability to represent text semantics. Additionally, the weighted feature fusion method effectively retains and utilizes more feature information. These two improvements enhance classification accuracy by 1.77% and 1.49%, respectively. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Information fusion

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Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2269-2291页

作者： Zefeng Zheng Luyao Teng Wei Zhang Naiqi Wu Shaohua Teng the School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China the School of Information Engineering Guangzhou Panyu PolytechicGuangzhou 511483China the Faculty of Information Technology Monash University20 Exhibition Walk ClaytonVIC 3800Australia IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China

Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by ***,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of *** address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target *** analyses show that DDS avoids repeated sampling during the *** the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly *** addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA *** experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.

关键词： Cross-domain risk dual density sampling intra-domain risk maximum mean discrepancy knowledge transfer learning resource-limited domain adaptation

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A link-quality-aware grouping-based efficient routing scheme in Software-Defined Vehicular Networks 17

A link-quality-aware grouping-based efficient routing scheme...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Malakar, Madhuri Mahapatro, Judhistir National Institute of Technology Rourkela Department of Computer Science and Engineering Odisha Rourkela769008 India

ISBN: (纸本)9798331531195

This paper delves into the existing routing schemes in Vehicular Ad hoc Networks (VANETs), critically examines their limitations, and proposes an efficient routing scheme for Software-Defined Vehicular Networks (SDVN). The proposed scheme employs a grouping mechanism to establish a robust path between the source and destination vehicles by using velocity vectors and cartesian coordinates of vehicles. Additionally, the scheme incorporates real-time vehicle information, such as GPS location, direction, speed, and vehicle density, and the Expected Transmission Count (ETX), as a link-quality routing metric for superior link quality. The ETX metric ensures the selection of better and more stable links, while the grouping mechanism identifies links with a high probability of breakage prior to their occurrence for efficient routing. The real-time link-quality information between the vehicles is dynamically generated and monitored at the control plane, ensuring up-to-date and accurate routing decisions. We evaluate the performance of the proposed scheme against traditional approaches. Simulation results show that the proposed scheme outperforms the existing schemes, achieving higher throughput, lower packet loss, reduced end-to-end delay, and significantly fewer path breaks with the network scenario of increasing vehicle density and speed. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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Stable and Energy-efficient Next-hop Router Selection (SE-NRS) for wireless Body Area Networks

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International Journal of Information technology (Singapore) 2023年第2期15卷 1189-1200页

作者： Kiran, Mekathoti Vamsi Nithya, B. Department of Computer Science and Engineering National Institute of Technology Tamilnadu Tiruchirappalli India

The wireless Body Area Network grabs the attention of researchers as the necessity for virtual or online healthcare is increasing day by day in this faster world (Sen and Yamin in Int J Inf Technol 13:829–837, 2021). Routing is a mechanism that serves the Wireless Body Area Network to route the packets to the destination by reducing delay and congestion. As the wireless Body Area Network is emergency-aware, timely and, loss-less packet delivery is more vital for the Quality of Service. In this context, finding the next-hop router connection which alive for a longer duration becomes a big challenge. To overcome this issue, this paper proposes a Stable and Energy-efficient Next-hop Router Selection algorithm to select the best router, thereby reducing the number of disconnections and connections at the coordinator node. To achieve this, the proposed Stable and Energy-efficient Next-hop Router Selection algorithm calculates a critical function for each available router by considering the router distance, router angle, signal strength, and the router’s history with a coordinator node. Then it selects the router with the highest value of the critical function, which guarantees that the connected router is stable for a longer duration and avoids unnecessary disconnections and connections. The simulation results reveal that the proposed Stable and Energy-efficient Next-hop Router Selection algorithm achieves better results than the existing algorithms in terms of throughput, delay, packet delivery ratio, and remaining energy. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Coordinator node Mobility Router distance Signal-strength Stable routing WBAN

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Gastrointestinal Disease Prediction Using Transfer Learning

Gastrointestinal Disease Prediction Using Transfer Learning

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International Conference on computer Vision and Internet of Things 2023, ICCVIoT 2023

作者： Sujatha, R. Helen, D. Hemamalini, U. Divya, V. Dharshini, A. Priya School of Information Technology & Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur Chennai India Department of Information Technology VELS Institute of Science Technology and Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9781839539848

According to the Global Cancer Observatory's 2018 estimations, gastrointestinal cancer is the third most common cause of cancer deaths and the fifth most common cause of being diagnosed with cancer worldwide. Diagnosis is crucial, and gastroscopy is used to detect stomach cancer early to enhance patient survival. This study proposed a Deep Learning based computer-Aided Diagnostics (CADx) method to identify gastroscopy disease. The dataset is trained with an adversarial training technique. The proposed approach uses the deep convolution neural network based on VGG16, VGG19, and InceptionV3. The dataset is trained with an adversarial training technique. The Inception V3 has the best accuracy among them, with an accuracy in the training of 94.44% and a validation accuracy of 84.75%, with a minor loss compared to VGG16 and VGG19 models. © The Institution of engineering & technology 2023.

关键词： Deep learning

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Machine Utilization Prediction in Cloud Using Informer Model 4

Machine Utilization Prediction in Cloud Using Informer Model

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Naik, Archana Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529635

This study investigates CPU utilization prediction in cloud environments using the Informer model, a deep learning framework optimized for long-sequence forecasting. By leveraging its ability to capture complex temporal patterns, the Informer model is benchmarked against GRU, LSTM, Prophet, and XGBoost across diverse CPU utilization datasets. Performance is evaluated using RMSE, MSE, MAE, and MAPE metrics. Experimental results demonstrate that the Informer model significantly outperforms the comparison models, excelling in capturing both gradual trends and abrupt fluctuations. Specifically, the Informer achieved a 47.51% reduction in RMSE over GRU, 49.90% over LSTM, 67.36% over Prophet, and 50.46% over XGBoost. These findings underscore the Informer model's superior adaptability and precision, making it a robust solution for dynamic resource utilization prediction in cloud environments. Its predictive accuracy is pivotal for enhancing workload management and optimizing resource allocation. © 2024 IEEE.

关键词： Prediction models

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An Energy Efficient Hybrid Communication Protocol for Large Area Wireless Sensor Networks 4

An Energy Efficient Hybrid Communication Protocol for Large ...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karthigeyan, Shreyas Bhol, Sampark Jain, Akshat Yadav, Karthik Sinha, Akash Padma Priya, R. School of Computer Science and Information Systems Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal462003 India

Energy conservation is an indispensable aspect of the protocols designed for Wireless Sensor Networks (WSNs). The communication protocols for WSN fall mainly under two categories: centralized and distributed. Centralized protocols are not feasible for real-world WSNs as the network can span across large areas where most of the nodes cannot directly communicate with the base station (BS). The routing issues in such networks can be addressed using multi-hop protocols. However, the process of BS guided clustering in such methods cannot be performed. Distributed clustering methods that minimize the intervention of BS in clustering can provide feasible solutions in such cases, but the energy expenditure is considerably high. This paper, hence, proposes a hybrid clustering based routing protocol for large WSN that employs certain aspects of centralized as well as that of distributed protocols. Experimental evaluation of the proposed protocol in terms of network lifetime gives promising results that clearly indicate the energy efficiency and consistency of the proposed work in different scenarios. The proposed work can prove beneficial in real-life scenarios where the deployed nodes are far away from the BS. © 2025 The Authors. Published by Elsevier B.V.

关键词： Hybrid power

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A Deep Learning based Ischemic Stroke Lesion Segmentation using Modified Multi-Scale 3D CNN

A Deep Learning based Ischemic Stroke Lesion Segmentation us...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Ruthra, E. Ruhan Bevi, A. Department of Electronics and Communication Engineering SRM Institute of Science and Technology SRM Nagar Chengalpattu T.N Kattankulathur India

ISBN: (纸本)9798350305708

Acute Ischemic Stroke (AIS) is the second-largest fatal accident that results in High morbidity, disability, and mortality worldwide. Clinicians can help patients receive the right therapies by doing an early and automated diagnosis of AIS. A computerized method for segmenting ischemia lesions should be developed to reduce time consumption and avoid clinical error. This paper introduces the segmentation techniques based on the Deep learning algorithm by creating an eleven-layer deep, double pathway, 3D Convolutional Neural Network. After analyzing a relevant multi-modal 3D MRI-like Diffusion Weighted Imaging (DWI) patch at various scales, the created system segments pathologies voxel-wise. For training and testing, this study uses 255 examples with five-fold validation. After postprocessing, it outperformed other approaches with a high Dice mean value of 0.82 on the datasets of ISLES 2022. The implemented network can process a 3D brain volume of different sizes within 3 minutes, making it relevant to broader research cohorts' automated analysis. © 2023 IEEE.

关键词： 3D CNN Deep Learning DWI Ischemic stroke Segmentation

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A Multi-leg Powered Constant Voltage On-Road Charging System With an LCC-S Compensation 5

A Multi-leg Powered Constant Voltage On-Road Charging System...

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5th IEEE International Transportation Electrification Conference, ITEC-India 2023

作者： Shanmugam, Yuvaraja Narayanamoorthi, R. Dominic, Savio A. Balaji, C. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350337808

A Dynamic Wireless Charging (DWC) system extends the travelling distance and lowers the Electric Vehicles heavy volume (EV) battery requirement. The High-frequency, High-Power inverter energizes the charging coupler embedded in the roadways. The EV batteries require a constant charging profile to extend their life. This paper suggests a constant voltage charging methodology for the DWC system with the LCC-S compensation network. A regression learning algorithm assists in determining the optimal value of the compensation network. The algorithm with a lower root means square value decides the optimal network value concerning the co-efficient of coupling. The five-leg inverter energizes the charging couplers, and the transmitter pads transfer the energy over 150 mm. The charging system delivers a nominal voltage of 350 V to the storage device. The overall efficiency of the proposed DWC system is 94.3% during constant voltage charging. © 2023 IEEE.

关键词： Electric inverters

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