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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Abhyudhay, S.R. Aditya, G.M. Upadya, Achyuta K. Naik, Archana Ushashree, P. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India

ISBN: (纸本)9798350382952

In the era of digital transformation, the hospitality industry faces unique challenges and opportunities. With travellers increasingly relying on online reviews to make informed decisions, the role of sentiment analysis in the hotel sector has never been more critical. This comprehensive research aims to provide a profound understanding of sentiment analysis in the context of hotel reviews and delves into preprocessing and the application of state-of-the-art sentiment analysis techniques. The sentiment analysis model, based on SVM, achieved promising results in categorizing hotel reviews into positive, negative sentiments. The evaluation metrics indicate an accuracy of 82%. Visualizations such as the confusion matrix and ROC curve further demonstrate the model's effectiveness in distinguishing between positive and negative sentiments. The findings of this research shed light on the distribution of sentiments in hotel reviews, providing actionable insights for the hospitality industry. © 2024 IEEE.

关键词： Sentiment analysis

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Enhancing Ophthalmic Diagnostics: A New Horizon for OCT Retinal Analysis

Enhancing Ophthalmic Diagnostics: A New Horizon for OCT Reti...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Priyanka, S. Selvam, Prabu Vellore Institute of Technology School of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350361537

Ophthalmic diagnostics play a critical role in the early detection and management of various ocular diseases. Among the advanced imaging modalities employed in ophthalmology, Optical Coherence Tomography (OCT) has emerged as a cornerstone technology for non-invasive, high-resolution imaging of ocular structures. This paper analyzes the efficiency of three deep learning models such as VGG16, ResNet50, and InceptionV3, in Optical Coherence Tomography (OCT) images. This study compares the efficiency of these deep learning models in classifying four different pathological retinal conditions: Normal Retina (NR), Choroidal NeoVascularization (CNV), Diabetic Macular Edema (DME), and Early Age-Related Macular Degeneration (EAMD). In addition, we have performed two preprocessing operations, image enhancement and data augmentation, to improve the image quality and to over the data imbalance, respectively. We assessed the performance of these deep learning models using a benchmark dataset. After the experiments, we found that the VGG16 outperforms ResNet50 and InceptionV3 by accomplishing an accuracy of 98.7%. © 2024 IEEE.

关键词： Optical coherence tomography

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TotCareBot: A Comprehensive Approach Towards Enhancing Early Childhood Education through Robotics 5

TotCareBot: A Comprehensive Approach Towards Enhancing Early...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Chougala, Manjula R Sushma, B. Bhayyar, Akshata S Bhandarkar, Abhay Pavithra, P.N. Ramaiah Institute of Technology Dept. of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331528539

Robots have emerged as versatile tools with significant potential to enhance teaching and learning environments, including classrooms, laboratories, play homes, crèches, and even at home. Their engaging nature can captivate young children, boosting motivation and interest. This paper proposes a methodology for an Educational Robot (ER) designed for play homes. The ER is capable of moving around the classroom, monitoring children, analyzing their emotions, responding appropriately, and alerting the teacher when necessary. The proposed system aims to leverage the positive impact of expressive behaviors in robots, such as storytelling and pampering. Our findings indicate that expressive robot narration is as effective as that of a human displaying similar behaviors and even surpasses a static, inexpressive human narrator. Furthermore, the robot excels in conveying educational content, although its limited emotional expression slightly reduces its effectiveness in narrating emotionally rich stories. This research underscores the potential of ERs to create dynamic and interactive learning experiences for young children, fostering both their cognitive and emotional development. © 2024 IEEE.

关键词： Educational robots

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Impact of Variable Sample Size on the Efficiency of Support Vector Machines in Cardiovascular Disease Detection 2

Impact of Variable Sample Size on the Efficiency of Support ...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Sarraju, Vijayalakshmi Pal, Jaya Kamilya, Supreeti Computer Science and Engineering Department Birla Institute of Technology Lalpur Ranchi Mesra India Computer Science and Engineering Department Birla Institute of Technology Ranchi Mesra India

ISBN: (纸本)9798350383867

Exploring the relationship between sample size and the performance of machine learning models in diagnosing cardiovascular diseases (CVD) aids in understanding the optimal data volume required to achieve reliable and accurate diagnostic predictions. This research focuses on examining how the size of the sample influences the efficacy of a particular machine learning algorithm, Support Vector Machine (SVM) in detecting cardiovascular diseases. The efficacy of the SVM models was assessed by employing datasets with different sample sizes. These include one original dataset and two statistically representative samples with sample sizes of 170 and 208 respectively. We evaluate multiple metrics, including F1-score, accuracy, specificity, sensitivity, precision, AUC and ROC curve. Based on the findings, it is evident that a larger sample size generally leads to improved precision and specificity. However, it is important to note that a decrease in sensitivity often accompanies this improvement. In addition, the study highlights the importance of using multiple performance metrics to comprehensively assess the efficacy of SVM models. ©2024 IEEE.

关键词： Support vector machines

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Motion Detection in Real-Time Surveillance Using Two Frame Differencing 1

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8th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Biswas, Tamal Bhattacharya, Diptendu Rudrapal, Dwijen Roy, Srijan Mandal, Gouranga National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India Vellore Institute of Technology Andhra Pradesh India

ISBN: (数字)9789819702107

ISBN: (纸本)9789819702091

This research article delves into the creation of a real-time system for detecting motion, particularly for surveillance purposes. The proposed approach relies on computer vision algorithms to analyze live video streams captured by a camera. By scrutinizing these feeds, the algorithms can discern alterations within the scene, specifically identifying objects in motion. The primary aim is to ensure that this system functions in real time, thereby furnishing instantaneous insights to security personnel. The paper expounds on the different steps integral to motion detection, encompassing procedures like background subtraction, foreground segmentation, and object tracking. To validate the system’s effectiveness, an evaluation is conducted using a collection of actual surveillance videos. The findings underscore the system’s capability to accurately detect moving objects across diverse scenarios. The article concludes by contemplating the system’s potential applications, which span security setups, traffic supervision, and ecological monitoring. The focal technique utilized in this study is Frame Differencing, a means of detecting object motion. This approach is adept at distinguishing moving objects within a given environment. In conjunction with this method, the paper emphasizes the viability of background subtraction to further enhance Frame Differencing, thereby augmenting its precision and efficacy. Upon applying this method to a camera, it is discerned that luminosity has a significant impact on the minimal value. Notably, a minimal value of 35 is identified as the most favorable choice. Here, threshold value 35 is selected because we have seen in our experiment that 35 gives the optimal result with our algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cameras

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Hybrid scientific article recommendation system with COOT optimization

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Data Science and Management 2024年第2期7卷 99-107页

作者： R.Sivasankari J.Dhilipan Department of Computer Science SRM Institute of Science and TechnologyChennai600089India

Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold-start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and mean reciprocal rank (MRR) by 5.7%.

关键词： Recommendation system COOT optimization Citation network Classification Long short-term memory(LSTM)

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Integrated Crowd Counting System Utilizing IoT Sensors, OpenCV and YOLO Models for Accurate People Density Estimation in Real-Time Environments 1

Integrated Crowd Counting System Utilizing IoT Sensors, Open...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Jayasingh, Suvendra Kumar Naik, Pintu Swain, Satyaprakash Patra, Kumar Janardan Kabat, Manas Ranjan Institute of Management and Information Technology BPUT Department of Computer Science and Engineering Odisha Cuttack India Indian Institute of Technology Department of Computer Science and Engineering Jammu India

ISBN: (纸本)9798350361780

Real-time crowd monitoring plays a pivotal role in effectively managing public spaces and ensuring safety. This study investigates the fusion of IoT devices and the YOLO object detection model to accurately count crowds. IoT devices facilitate the instantaneous collection of data from cameras, while the YOLO model adeptly identifies individuals within recorded video frames. The study rigorously assesses the performance of three YOLO variants: YOLO V5, YOLO V8 and YOLO V8 NAS. Findings reveal that YOLO V8 NAS surpasses YOLO V5 and YOLO V8 in mean average precision (mAP), achieving an exceptional mAP of 95.1%. This heightened precision is attributed to the integration of Neural Architecture Search (NAS) into the YOLO V8 NAS model, fine-tuning its architecture specifically for crowd counting tasks. It analyzes various networking models proposed in earlier studies for analyzing crowded scenes in public spaces. It emphasizes the potential of a hybrid model involving an IP camera module and a Deep Neural Network for effective crowd sensing. In this setup, the IP camera captures video footage, while the DNN detects individuals and assesses crowd density based on the count of people recognized. This approach presents an encouraging solution for real-time crowd monitoring and management in public environments. © 2024 IEEE.

关键词： Face recognition

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Deep Learning based Air Quality Forecasting using Satellite Data 6

Deep Learning based Air Quality Forecasting using Satellite ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sree, K. Navya Reddy, Mancharla Rakesh Saraswathi, S. Department of Computer Science and Engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. This research focuses on the development of an advanced air quality forecasting system using machine learning techniques. The system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. The model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. The system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. This research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies. © 2025 IEEE.

关键词： air pollution Air quality forecasts cardiovascular diseases data-driven strategies environmental policy global climate international collaboration machine learning NO2 O3 PM2.5 policy-making pollution mitigation proactive alerts public health real-time monitoring respiratory diseases spatial variability temporal variability transboundary pollution

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IoT-Enabled Predictive Maintenance and Analytic Hierarchy Process Based Prioritization of Real-Time Parameters in a Diesel Generator: An Industry 4.0 Case Study

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SN computer Science 2024年第1期5卷 145页

作者： Mohapatra, Ambarish Gajendra Mohanty, Anita Tripathy, Pradyumna Kumar Department of Electronics and Instrumentation Engineering Silicon Institute of Technology Bhubaneswar India Department of Computer Science and Engineering Silicon Institute of Technology Bhubaneswar India

Diesel generator (DG) is a secondary power source that is becoming increasingly common for supplying continuous power to business and residential buildings. These hybrid machines provide the necessary electrical energy using both a diesel engine and an electric generator. Monitoring crucial machine characteristics on a regular basis is crucial for increasing their efficiency. condition monitoring systems (CMS) powered by the Internet of Things (IoT) have taken the role of Remote Monitoring (RM). Moreover, a key component of Industry 4.0 is predictive maintenance, which may be accomplished by implementing IoT-enabled RM. This paper portrays two aspects of the DG unit. Firstly, the performance analysis of the DG unit using a 4G-enabled IoT node installed on the motherboard. This research work develops and tests a framework for monitoring and analyzing key metrics, including engine speed, voltage and current generated, power factor, coolant, fuel, and battery health status. Secondly, the Analytic Hierarchy Process (AHP) can be used to rank the performance indicators that are most important for the efficient monitoring of the DG system in the context of IoT-enabled DG monitoring. The AHP technique enables decision-makers to objectively assess the relative weights of several criteria and to prioritize them. Using AHP, decision-makers can weigh each of these criteria based on how important they are in relation to one another. The performance of the DG system may then be assessed using these weights, and potential areas for improvement can be found. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AHP CMS Condition monitoring systems Diesel generator Industry 4.0 IoT Predictive maintenance Remote monitoring

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Deep Learning-Based Maize Crop Disease Classification Model in Telangana Region of South India

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 627-637页

作者： Nagaraju, M. Chawla, Priyanka Hyderabad500043 India National Institute of Technology Department of Computer Science and Engineering Warangal506004 India

One of India's main crops, maize, accounts for 2-3% of global production. Disease detection in maize fields has become increasingly difficult due to a lack of knowledge about disease symptoms. Furthermore, manual disease detection methods take a lot of time and are not effective. Recent developments in convolutional neural networks (CNNs) have exhibited remarkable performance in disease recognition and classification. A CNN is a deep learning technique that extracts the features from an image and performs the disease classification effectively. The optimization of hyperparameters is a tedious problem that impacts the performance of a model. The main purpose of the present research is to support future research to configure suitable hyperparameters to a model. In the present work, a deep CNN is proposed for the classification of seven different diseases of maize crop. Several hyperparameters, such as image size, batch size, number of epochs, optimizers, learning rate, kernel size, and number of hidden layers, were tested with various values in the experimental approach. The obtained results show that running the model for 200 epochs improved the classification accuracy with 87.44%. It also states that choosing input image sizes of 168 × 168 and 224 × 224 resulted in a good classification accuracy of 84.66% and 85.23%, respectively. The proposed deep CNN model has attained 85.83% classification accuracy with the Adam optimizer and a learning rate of 0.001. However, the results achieved by other optimizers, such as root-mean-square propagation (81.95%) and stochastic gradient descent (79.66%), are not better when compared with the Adam optimizer. Finally, the results have provided a better knowledge in selecting appropriate hyperparameters to the application of plant disease classification. © 2024 IEEE.

关键词： Convolutional neural networks

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