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2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, SPICSCON 2024

作者： Islam, Md Ariful Ahsan, Shawly Hoque, Mohammed Moshiul Department of Computer Science & Engineering Chittagong University of Engineering and Technology Chattogram4349 Bangladesh

ISBN: (纸本)9798331510213

Sentiment analysis (SA) of restaurant reviews is pivotal in understanding customer experiences and preferences, allowing businesses to enhance service quality and customer satisfaction. This study presents an automatic framework that leverages transformer-based techniques for SA of restaurant reviews in Bengali. To underscore the importance of this research, a new Bengali Restaurant Review Dataset (BRRD) has been meticulously developed, featuring 11,004 labeled restaurant reviews categorized into positive, negative, and neutral. The SA tasks were performed using various ML, DL, and transformer-based models, including LR, RF, DT, SVM, AdaBoost, CNN, BiLSTM, CNN + BiLSTM, XLM-R, mBERT, and BanglaBERT. Experimental evaluations reveal that BanglaBERT achieved the highest weighted f1-score of 0.90, outperforming the other models used in this study. © 2024 IEEE.

关键词： Customer satisfaction

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Traffic Sign Board Recognition and Voice Alert System Using Deep Convolutional Neural Network 6th

Traffic Sign Board Recognition and Voice Alert System Using ...

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

作者： Sharma, Reya Sathvika, Y. Sai Sreeja, T. Pratyusha, P. Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hyderabad India

ISBN: (纸本)9789819728381

Road signs are crucial for ensuring a secure and efficient flow of traffic. Negligence in failing to perceive or incorrectly interpreting traffic signs is one of the major factors in auto accidents. The suggested technology enables the display of the traffic sign and the transmission of a speech notification to the vehicle's speaker so that it can move forward safely. The suggested method uses Convolutional Neural Network (CNN) training to help identify and categorize images of commercial signs. A voice alarm is transmitted through the speaker to alert the drivers after the system recognizes the sign. The system's primary goal is to safeguard the driver, passengers, and pedestrians. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Traffic signs

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DeepRegYLab: A Dilated Convolution and Network Design Space Based Image Segmentation Model to Segment Healthy GI Tract of Cancer Patients to Automate Radiotherapy 6

DeepRegYLab: A Dilated Convolution and Network Design Space ...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Alam, Md. Jafril Zaman, Sakib Hakim, Md. Azizul Das, Sunanda Shill, Pintu Chandra Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna9203 Bangladesh

ISBN: (纸本)9798350385779

Gastrointestinal (GI) tract cancer is the most preva-lent type of cancer globally. It's crucial to distinguish between healthy and diseased cells before administering radiation treatment to cancer cells. To segment healthy GI tract organs for proper radiation therapy, we proposed DeepRegYLab, a dilated convolution, and a network design space-based image segmentation model. A RegNetY backbone, an encoder, and a CNN-based decoder were employed in the proposed model. The encoder consists of dilated convolution, 1 x 1 convolution, and an upsampling operation. RegNetY is a network design space containing convolution operation, SE block, batch normalization, and activation function. On the G I tract MRI scan dataset, our proposed model achieved good accuracy and minimal validation loss. The dice score, the IOU value, and the validation loss of our proposed model were 0.83, 0.77, and 0.12 respectively. The proposed model outperformed many other image segmentation models when it comes to segmenting healthy G I tract organs from MRI 2D scans. © 2024 IEEE.

关键词： Image segmentation

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Deep Learning Based Sentiment Analysis of Tamil–English YouTube Comments

Deep Learning Based Sentiment Analysis of Tamil–English You...

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International Conference on Computing and Machine Learning, CML 2024

作者： Subramanian, Malliga Kogilavani, S.V. Gowthesh, D. Lohith, S. Mithunajha, S. Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Erode India

ISBN: (纸本)9789819765874

With the exponential growth of user-generated content on YouTube, understanding the sentiments expressed in comments has become crucial. The research work aims to analyze the impact of online sentiments on social welfare through a comprehensive analysis. While sentiment analysis tools and resources may be more readily available for widely spoken languages, the increasing demand for extending sentiment analysis to low-resource languages like Tamil is also a pressing issue. In this work, transformer-based models such as BERT, RoBERTa, and XLMRobertA are used to extract the features and detect the sentiment of the Tamil-English YouTube comments and these models have been specifically designed for text-based multilingual aspects. The combined approach leveraging Support Vector Machine (SVM) and transformer models is enhanced through the application of SMOTE (Synthetic Minority Over-sampling Technique) and Random OverSampling techniques, contributing to the increased performance. These oversampling techniques are employed to address class imbalance in the dataset, ensuring a more robust and balanced model performance. Of all the proposed models, XLMRoBERTa outperforms with 63.60% of accuracy. In the study, a multilingual dataset of YouTube comments was collected, with a particular focus on low-resource languages. The challenges in sentiment analysis include limited code-mixing, and unique linguistic characteristics of these languages, which pose difficulties for traditional sentiment analysis approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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A Privacy-Preserving Quantum Blockchain Technique for Electronic Medical Records

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IEEE engineering Management Review 2023年第4期51卷 137-144页

作者： Venkatesh, Ranjitha Hanumantha, Brahmananda Savadatti Gandhi Institute of Technology and Management Department of Computer Science Engineering Bengaluru561203 India

Nowadays, electronic medical records are increasing rapidly. Electronic medical records consist of sensitive and confidential information. These pieces of information need to be protected from attackers during the exchange with patients. Blockchain technology can protect information from attackers during the exchange of information. There are various classical techniques to protect electronic medical records. These techniques can exchange information correctly but cannot resist collective and coherent attacks and have more communication and computation costs. In this article, we introduced a privacy-preserving quantum blockchain technique to protect electronic medical records. This technique can resist the intercept, intercept-resend, entangle-measure, man-in-the-middle, collective, and coherent attacks with less communication and computation costs. © 1973-2011 IEEE.

关键词： Quantum entanglement

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IV3BoostPCOS: A Comprehensive Polycystic Ovary Syndrome (PCOS) Detection Framework

IV3BoostPCOS: A Comprehensive Polycystic Ovary Syndrome (PCO...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Mahesswari, G. Umaa Maheswari, P. Uma Priya, B. Harini College of Engineering Guindy Anna University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331535476

Polycystic ovary syndrome (PCOS) is a prevalent gynecological illness characterized by hormonal irregularities and ovarian cyst production that affects women all over the world. Detecting numerous cysts using ovarian ultrasonography (USG) scans is a solid method for accurately diagnosing PCOS and developing treatment plans for individuals. A sophisticated computer-assisted cyst identification system is a feasible alternative to manual identification, which is pronetoe rrors. Hence, in this research, a sophisticated ultrasound image categorization method is proposed that aims at optimizing three powerful convolutional Neural Network (CNN) models to extract features from the ovarian ultrasonography (USG) images. The reduced feature set is then used to categorize the ultrasound images using the Extreme Gradient Boosting classifier (XGBoost) to categorize between PCOS and non-PCOS ovaries. The proposed methodology, which used the 'Inception V3' model without top classification 1 ayers f or feature extraction a nd t he XGBoost model as an image classifier, o btained 9 9.31% a ccuracy and reduced training execution time when compared to current PCOS detection methodologies. © 2024 IEEE.

关键词： Ultrasonography

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Unmanned Aerial Systems in Search and Rescue: A Comprehensive Review and Future Directions 5

Unmanned Aerial Systems in Search and Rescue: A Comprehensiv...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Kowshika, M. Ooviya, M. Pavithradevi, B. Rashika, K.V. Sampath Kumar, S. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore641202 India

ISBN: (纸本)9798350395235

Search and Rescue (SAR) operations are critical endeavors in disaster management and public safety, often involving high-risk situations where human lives are at stake. Traditionally, these operations have heavily relied on human resources and ground-based technologies, which can be time consuming, resource-intensive, and constrained by various limitations. In recent years, the integration of drone technology into SAR operations has revolutionized the field, offering a powerful tool to enhance the efficiency, effectiveness, and safety of rescue missions. This study provides an in-depth exploration of the role of drones in human search and rescue operations. It examines the various facets of this technology, including hardware, software, and operational considerations, and highlights the manifold benefits it brings to SAR missions. The most important obligation during a natural disaster is to locate and rescue any trapped individuals as soon as conceivable. Unmanned aerial vehicles (UAVs) have seen a surge in usage recently due to their excellent durability, adaptability, cheap cost, and simplicity of implementation. This article includes a fresh thermal imaging dataset that was obtained by drones. The study discovers the best observers to prune and fine-tune the survivor detection network based on the sensitivity of the convolutional layer due to the restricted computational power and memory of the microprocessor. © 2024 IEEE.

关键词： Drones

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Mobile Communication Voice Enhancement Under Convolutional Neural Networks and the Internet of Things

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Intelligent Automation & Soft Computing 2023年第7期37卷 777-797页

作者： Jiajia Yu Department of Electronic and Computer Engineering Southeast University Chengxian CollegeNanjing210088China

This study aims to reduce the interference of ambient noise in mobile communication,improve the accuracy and authenticity of information transmitted by sound,and guarantee the accuracy of voice information deliv-ered by mobile ***,the principles and techniques of speech enhancement are analyzed,and a fast lateral recursive least square method(FLRLS method)is adopted to process sound ***,the convolutional neural networks(CNNs)-based noise recognition CNN(NR-CNN)algorithm and speech enhancement model are ***,related experiments are designed to verify the performance of the proposed algorithm and *** experimental results show that the noise classification accuracy of the NR-CNN noise recognition algorithm is higher than 99.82%,and the recall rate and F1 value are also higher than *** proposed sound enhance-ment model can effectively enhance the original sound in the case of noise *** the CNN is incorporated,the average value of all noisy sound perception quality evaluation system values is improved by over 21%compared with that of the traditional noise reduction *** proposed algorithm can adapt to a variety of voice environments and can simultaneously enhance and reduce noise processing on a variety of different types of voice signals,and the processing effect is better than that of traditional sound enhancement *** addition,the sound distortion index of the proposed speech enhancement model is inferior to that of the control group,indicating that the addition of the CNN neural network is less likely to cause sound signal distortion in various sound environments and shows superior *** summary,the proposed CNN-based speech enhancement model shows significant sound enhancement effects,stable performance,and strong *** study provides a reference and basis for research applying neural networks in speech enhancement.

关键词： Convolutional neural networks speech enhancement noise recognition deep learning human-computer interaction Internet of Things

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Climate Change Impacts on Vaitarna River Basin Hydrology Using Downscaling Machine Learning Technique 4th

Climate Change Impacts on Vaitarna River Basin Hydrology Usi...

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4th International Conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Deshmukh, M.K. Department of Computer Science and Engineering College of Engineering and Technology Maharashtra Akola444104 India

ISBN: (纸本)9789819706990

Climate change's impact on hydrology is critical since it affects agriculture, vegetation, and livelihood. This investigation aims to determine whether climate change is possible in the Vaitarna River basin. The IPCC has studied temperature extremes and their effects on ecosystems (IPCC). Temperature variations are attributed to changes in solar energy, biological processes, and human activities. Climate models replicate how the climate's drivers interact using ocean, atmospheric, ice, and land surface models. Impact evaluation uses several climate models. Global Climate Models (GCMs) and GCMs are used to analyze changes in global temperature generated by doubling CO2 concentrations. In this experiment, free software SDSM 4.2 was employed. It's a blend of regression-based and stochastic weather generators in which local and large-scale data are connected. CCIS proposes the Statistical Downscaling Model as a downscaling technique. Downscaling uses GCMs to anticipate local conditions. GCMs are accurate atmospheric simulations based on large-scale spatiotemporal models. GCMs can't predict local and regional climate factors or their intensity. Dynamic downscaling didn't surpass statistical downscaling, according to a thorough study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Rivers

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Severity Levels Categorization for Detecting Diabetic Retinopathy Using YOLOv7 2nd

Severity Levels Categorization for Detecting Diabetic Retino...

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2nd International Conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Nirmala Devi, M. Subbulakshmi, B. Sivakumar, M. Teenu Prinica, T. Department of Computer Science and Engineering Thiagarajar College of Engineering Tamil Nadu Madurai India

ISBN: (纸本)9789819754113

A disease that damages the eyes called diabetic retinopathy (DR) is more frequently found in adults who are working. Normal, moderate, and proliferate are the three levels of diabetic retinopathy severity. The YOLOv7 algorithm and PyTorch framework are used in this paper to propose a novel model for assessing the DR severity in its various phases. For the precise and effective detection of lesions in retinal images, the suggested method makes use of a convolutional neural network model, data augmentation, and reinforcement learning techniques. The abnormality or damage visible in the retina of the eye is referred to as a lesion in retinal imaging. The validation set’s mean average precision for the model, which was trained on the blindness detection dataset, was 0.921 and 0.902 for intersection over union thresholds of 0.5 and 0.95, respectively. The proposed model gives promising outcomes for early diagnosis and treatment of DR, which is essential in preventing serious outcomes like blindness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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