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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Palaniappan, Ramya Surendran, Suseendran Balakrishnan, Saranya Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Madanapalle India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India KGiSL Institute of Technology Department of Artificial Intelligence and Data science Coimbatore India

ISBN: (纸本)9798331540067

The automatic segmentation of tumours or lesions from magnetic resonance imaging (MRI) pictures is a critical but difficult task in clinical settings, sometimes necessitating laborious and time-consuming techniques. Deep learning has made a significant advancement in the medical imaging field in recent years. U-Net has emerged as the predominant model used for segmenting medical images. Although the standard U-Net framework has shown excellent performance in segmenting multimodal medical pictures, it has been noticed that it lags in certain aspects when dealing with tough datasets. We propose specific improvements to the state-of-the-art U-Net model. We have introduced attention block in the skipped connections to highlight relevant features. Additionally, we have incorporated transformers in the encoding path to capture temporal dependencies in medical images. In this research work, we have introduced an innovative architecture that has the potential to replace the U-Net model for segmenting regions of interest (ROI) in medical images. This study utilizes a total of 1760 samples obtained from the ISBI 2015 and PH2 medical repositories. The experimental findings have demonstrated that our proposed model has obtained highly encouraging results of 78.02 Jaccard coefficient and IoU of 65.14. In addition, we conducted tests and compared the performance of the proposed model with the classical U-Net model using the same dataset. As a result, we observed a relative improvement in the performance of both 2.36 and 1.85, respectively. The proposed technology will function as a sophisticated toolkit for radiologists to precisely detect and diagnose anomalous tumour cells. © 2024 IEEE.

关键词： Semantic Segmentation

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Aspect-Based Sentiment Analysis for Social Multimedia:A Hybrid Computational Framework

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computer Systems science & engineering 2023年第8期46卷 2415-2428页

作者： Muhammad Rizwan Rashid Rana Saif Ur Rehman Asif Nawaz Tariq Ali Azhar Imran Abdulkareem Alzahrani Abdullah Almuhaimeed University Institute of Information Technology–PMAS Arid Agriculture University Rawalpindi46000Pakistan Department of Creative Technologies Air UniversityIslamabad44000Pakistan Computer Engineering and Science Department Faculty of Computer Science and Information TechnologyAl Baha UniversityAl Baha65799Saudi Arabia Digital Health Institute King Abdulaziz City for Science and TechnologyRiyadh11442Saudi Arabia

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest *** share their thoughts and feelings about various topics,including products,news,blogs,*** user reviews and tweets,sentiment analysis is used to discover opinions and *** polarity is a term used to describe how sentiment is ***,neutral and negative are all examples of *** area is still in its infancy and needs several critical *** and hidden emotions can detract from the accuracy of traditional *** methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative *** existing strategies are *** proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion ***,classification was performed using *** proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and *** results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

关键词： Aspects deep learning lexicon sentiments reviews

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Mining skyline frequent-utility patterns from big data environment based on MapReduce framework

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Intelligent Data Analysis 2023年第5期27卷 1359-1377页

作者： Wu, Jimmy Ming-Tai Li, Ranran Wu, Mu-En Lin, Jerry Chun-Wei Department of Information Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan Department of Computer Science Western Norway University of Applied Sciences Bergen Norway

When the concentration focuses on data mining, frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are commonly addressed and researched. Many related algorithms are proposed to reveal the general relationship between utility, frequency, and items in transaction databases. Although these algorithms can mine FIMs or HUIMs quickly, these algorithms merely take into account frequency or utility as a unilateral criterion for itemsets but the other factors (e.g., distance, price) could be also valuable for decision-making. A new skyline framework has been presented to mine frequent high utility patterns (SFUPs) to better support user decision-making. Several new algorithms have been proposed one after another. However, the Internet of Things (IoT), mobile Internet, and traditional Internet are generating massive amounts of data every day, and these cutting-edge standalone algorithms can not satisfy the new challenge of finding interesting patterns from this data. Big Data uses a distributed architecture in the form of cloud computing to filter and process this data to extract useful information. This paper proposes a novel parallel algorithm on Hadoop as a three-stage iterative algorithm based on MapReduce. MapReduce is used to divide the mining tasks of the whole large data set into multiple independent sub-tasks to find frequent and high utility patterns in parallel. Numerous experiments were done in this paper, and from the results, the algorithm can handle large datasets and show good performance on Hadoop clusters. © 2023 - IOS Press. All rights reserved.

关键词： MapReduce

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MSMVAN: Multi Step Multi Variate Deep Attention Network for Renewable Energy Forecast

MSMVAN: Multi Step Multi Variate Deep Attention Network for ...

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作者： Varshini, U. Shivani Sri Sree, R. Praneetha Perumal, Murukessan Srinivas, M. Subramanyam, R.B.V. The Department of Computer Science and Engineering National Institute of Technology Telangana Warangal506004 India The Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kurnool Kurnool518007 India

The importance of renewable energy in our everyday lives cannot be overstated, specifically highlighting solar and wind energies as two crucial sustainable power sources. Nevertheless, it is crucial to acknowledge that the amount of energy derived from renewable sources is subjected to variability, being influenced by environmental conditions. To ensure efficient management of energy load demands in diverse applications and enhance the performance of storage grids, precise energy production forecasting is vital. However, existing models doesn’t recognise the important variables for forecasting and fails in the presence of noisy or missing data. To address these issues, we proposed a MultiStep Multi Variate Deep Attention Network (MSMVAN) model with masked attention and dilated Fourier convolutions. The proposed approach utilizes a sequential model to analyze non-linear relationships in time series data. Further, dilated fourier convolutions are incorporated to capture multistep spatial and cross correlations, while attention mechanisms are employed to identify significant variables. As a result, the model acquires improved vector representations, leading to enhanced energy forecasting power. We evaluated our model on three standard datasets and from the experimental results we conclude that our proposed model outperforms the state-of-the-art models in forecasting solar and wind energy. © 2023 IEEE.

关键词： Time series analysis

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Dual-Path Spectrogram Refinement Network for Robust Speaker Verification

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Journal of Shanghai Jiaotong University (science) 2025年 1-9页

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The accuracy and reliability of automatic speaker verification (ASV) face significant challenges in noisy environments. In recent years, joint training of speech enhancement front-end and ASV back-end has been widely applied to improve the robustness of ASV systems. Traditional joint training directly uses the enhanced speech features as the input of the back-end. However, the diversity of noise types and noise intensities will excessively suppress the enhanced features, resulting in speech distortion or residual noise. To alleviate this problem, we propose a dual-path spectrogram refinement network that enables the enhanced features to learn supplementary information from the separated noise features. In addition, we incorporate coordinate attention into the overall joint architecture to capture more comprehensive frequency and temporal information of the speaker from different spatial positions. We conduct extensive experiments on the VoxCeleb1 test set, the out-of-domain noise test set, and the VOiCES corpus. The experimental results demonstrate that our proposed method significantly improves the accuracy and robustness of speaker verification systems in both clean and noisy environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Emotion Recognition from Electroencephalogram (EEG) Signals Using a Multiple Column Convolutional Neural Network Model

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SN computer science 2024年第2期5卷 213页

作者： Jha, Sonu Kumar Suvvari, Somaraju Kumar, Mukesh Department of Computer Science and Engineering National Institute of Technology Patna Patna 800005 India

Emotions are vital in human cognition and are essential for human survival. Emotion is often associated with smart decisions, interpersonal behavior, and, to some extent, intellectual cognition. From the recent literature on emotion recognition, we understand that the researchers are showing interest in creating meaningful "emotional" associations between humans and machines;there is a demand for accurate and scalable systems to detect human emotional states, as emotion recognition is needed to understand the mental status of such persons who cannot communicate their emotions, such as disabled people, mentally challenged persons, etc. Therefore, EEG signals provide a non-invasive method to identify the emotions of these disabled humans. The research community has recently been very interested in employing electroencephalography (EEG) for emotion classification since end-users have wearable EEG systems that may offer a portable, cheap, and straightforward technique for identifying emotions. Deep learning models have recently been extensively used to extract characteristics and recognize emotions from EEG recordings. Apart from that, various papers were reviewed in this research. This paper presented a multiple-column CNN network with a leaky ReLU activation function on the EEG brain wave and DEAP datasets. Multiple scalp electrode locations are used to collect EEG signals, and each electrode offers spatially unique data. The network can detect spatial correlations and extract characteristics that depict the spatial distribution of brain activity by employing a multiple-column CNN, which simultaneously processes signals from many channels. This makes it possible for the model to recognize emotions using the spatial information in EEG data. The result analysis was evaluated on different CNN models, and it was observed that an accuracy of 98.10% was achieved on the EEG brainwave dataset and 81% on the DEAP dataset. © 2024, The Author(s), under exclusive licence to Sprin

关键词： Attention Convolution neural network (CNN) Deep learning EEG Emotion recognition GCNN Multicolumn CNN

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A Comparative Study on the Properties of Carbon Fiber-Reinforced Polymer Composites Developed by Hand Layup and Vacuum Bagging Molding Techniques

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Journal of The Institution of Engineers (India): Series D 2024年第1期106卷 263-275页

作者： Amar, Thakur Abhinav Kumar, Amit Yadav, D.K. Department of Production and Industrial Engineering Birla Institute of Technology Jharkhand Mesra Ranchi835215 India Department of Mechanical Engineering National Institute of Technology Bihar Patna800005 India Department of Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur831014 India

The present study addressed the fabrication of carbon fiber-reinforced polymer (CFRP) composites using two different techniques: manual layup and vacuum bagging molding. The purpose was to compare their unique properties. CFRP composites employ woven roving mat carbon fiber as a reinforcing material, combined with an epoxy matrix consisting of Araldite and hardener. The resin to fiber ratio has been maintained at a 1:1 ratio, while the resin to hardener ratio has been maintained at a 10:1 ratio by weight. Various stacking sequences of carbon/epoxy composites (0/90) were made using both approaches. Optimization of several process parameters was carried out to enhance the mechanical properties of the polymer composites. Every laminate was composed of four to eight layers. Various carbon/epoxy composite orientations were subjected to testing until failure using a universal testing machine, in accordance with the relevant applicable standards (ASTM-D3039), in order to determine the ultimate tensile strength, % elongation (or ductility), and elastic constant. Furthermore, the adhesion intensity between the fiber and resin was assessed by conducting microstructural studies using a scanning electron microscope. The results demonstrated that the samples fabricated using the vacuum bagging mold technique exhibited superior tensile strength compared to the samples fabricated using the hand layup approach. © 2024, The Institution of Engineers (India).

关键词： Resins

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A reinforcement learning approach for widest path routing in software-defined networks

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ICT Express 2023年第5期9卷 882-889页

作者： Ke, Chih-Heng Tu, Yi-Hao Ma, Yi-Wei Department of Computer Science and Information Engineering National Quemoy University Kinmen 892 Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei 106 Taiwan

In this paper, a routing method based on reinforcement learning (RL) under software-defined networks (SDN), namely the Q-learning widest-path routing algorithm (Q-WPRA), is proposed. This algorithm processes the reward function according to the link bandwidth in the execution environment to find the optimal (i.e., widest) transmission path with the maximum bandwidth between the source and the destination through RL. The experimental results reveal that the Q-WPRA is outperformance than Dijkstra's algorithm and Dijkstra's widest-path algorithm to find the widest transmission path in SDN environment under different bandwidths, loss rates, and background traffic. © 2022 The Author(s)

关键词： Q-WPRA Reinforcement learning Reward function Software-defined networks Widest path

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Enhanced Bayesian Optimized Support Vector Machine (BO-SVM) Classification and Prediction of Heart Disease 4

Enhanced Bayesian Optimized Support Vector Machine (BO-SVM) ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Chairmadurai, P. Kavitha, P. Kamalakkannan, S. Pallavaram Department of Computer Science Chennai India Pallavaram Department of Computer Applications Chennai India Pallavaram Department of Information Technology Chennai India

ISBN: (纸本)9798331523923

Medical data analysis is increasingly used to plan, improve research techniques, and explain diagnoses. The allocation of medical resources is based on the prevalence of specific pathologies. Artificial intelligence (AI) offers robust software solutions to analyze existing data and provide optimal predictions of medical outcomes. The dataset for heart disease prediction consists missing values in important features like cholesterol, ECG results and lifestyle factors. This research focuses to solve the issues in the traditional machine learning algorithm and designing a system model incorporating multiple data processing algorithms to classify heart disease. The classification process allows for the generation of a predictive model from training and testing datasets. These datasets are processed using an algorithm for classification to create a new model that can analyze detailed data, potentially with these same classes, by combining computational techniques with mathematical tools. This study uses optimization strategies to improve this model's performance and maximize prediction outcomes. This study aims to provide a framework to forecast cardiac problems based on significant risk using various classifier techniques, including the Bayesian Optimized SVM, to variables. The UCI repository is the source of the dataset utilized in this investigation. Linear Discriminant Analysis is used for feature extraction to decrease dimensionality and enhance the model's capacity to identify relevant patterns in the data. To generate the results as a precision, Recall, f1 score and accuracy. © 2025 IEEE.

关键词： Support vector machines

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Spatio Temporal Tourism Tracking System Based on Adaptive Convolutional Neural Network

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computer Systems science & engineering 2023年第6期45卷 2435-2446页

作者： L.Maria Michael Visuwasam D.Paul Raj Department of Computer Science and Engineering Rajalakshmi Institute of TechnologyChennaiTamil Nadu600104India Department of Computer Science and Engineering R.M.D.Engineering CollegeChennaiTamil Nadu601204India

Technological developments create a lot of impacts in the tourism *** big data technologies and programs generate opportunities to enhance the strategy and results for transport ***,there is a difference between technological advances and their integration into the methods of tourism *** rising popularity of Freycinet National Park led to a master plan that would not address cultural and environmental *** study addresses the gap by using a synthesized application(app)for demographic surveys and Global Navigation Satellite System(GNSS)technology to implement research *** article focuses on managing visitors within the famous Freycinet National *** comprehensive structured data were analyzed in three phases,(1)identifying groups of visitors who are more likely to use the walking trails,(2)those who are more and less likely to visit during/peak crowding times,and(3)finally creating an integrated Spatio-temporal dependency model via a machine-based learning system for real-time *** research examines innovative techniques that can offer energy resources to managers and tourism agencies,especially in detecting,measuring,and potentially relieving crowding and over-tourism.

关键词： Spatio-temporal dependency machine-based learning global navigation satellite system(GNSS)technology smartphones

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