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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Singh, Shalini Baloni, Dev Sarkar, Manash Department of Computer Science and Engineering Quantum University Roorkee India Department of Computer Science and Engineering Atria Institute of Technology Banglore India

ISBN: (纸本)9781032911571

This review paper explores emerging threats to information privacy and security within the dynamic landscape of Online Social Networks (OSNs), which serve as repositories of vast amounts of user data. The rise in social media use emphasizes how crucial it is to inform users about the dangers that come with it, like cyber-attacks and privacy violations. Even though various social media platforms have been made possible by technological advancements, consumers’ ignorance of these platforms creates serious security risks. Although academics have come up with creative security solutions for social networks, there are obstacles in the way of their actual application. It is essential to continuously assess security vulnerabilities in light of technology improvements. This study examines several dangers to online social networks and suggests countermeasures based on a variety of models, frameworks, and encryption methods. Despite advancements, privacy concerns remain unaddressed, requiring parents to actively supervise their children’s online activity. © 2025 the Author(s).

关键词： Differential privacy

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Customer Feedback and Sentiment Analysis for Hotel Services 2

Customer Feedback and Sentiment Analysis for Hotel Services

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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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Efficacy of image based deep learning CNN models for bolt loosening detection in steel structures

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Asian Journal of Civil engineering 2025年第6期26卷 2409-2417页

作者： Alapati, Mallika Ramesh Chandra, G. Abboju, Shivani Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India

Bolted connections are integral components in steel structures and often are vulnerable to loosening due to cyclic loading and fatigue. Detecting bolt loosening in early stages is critical to prevent sudden catastrophic failures. Recent advancements in image capturing and processing, combined with machine learning techniques have significantly improved the accuracy of detecting bolt loosening. Deep learning techniques, in specific those based on image analysis, have proven to be effective tool for damage detection in structural members. In this study, initially captured dataset of 120 images from a laboratory model is augmented to 576 images using the Roboflow platform. Investigations in this work are tied to one image data set and the metrics are analysed for how well a model performs. Usually, it depends on each use case as to which model would be best suited. In the present work, dataset featured two states of bolt loosening: tight and loose. Convolution Neural Network architectures AlexNet and ResNet are applied individually to the augmented dataset to predict the bolt loosening states as binary classification problem. The results demonstrated an accuracy of 87.931%, indicating that the AlexNet model effectively distinguished between loose and tight conditions. Whereas the ResNet algorithm exhibited relatively better accuracy of 90.8%. Further, an integrated hybrid model (CNN model with LSTM) is also applied on the same image data to further enhance the efficiency of the deep learning frame work. The performance of the trained CNN algorithms was further validated with image responses from various real time scenarios of various lighting conditions and found to be fairly good. This study highlights the potential of using deep learning techniques, specifically AlexNet, ResNet and CNN with LSTM for accurately detecting bolt loosening, thereby contributing to the safety and reliability of steel structures. © The Author(s), under exclusive licence to Springer Nature S

关键词： AlexNet Bolt loosening Bolted connections Convolution neural network architectures Image based deep learning ResNet architecture

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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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Generative Adversarial Networks for Motor Imagery Classification using Wavelet Packet Decomposition and Complex Morlet Transform

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Multimedia Tools and Applications 2025年 1-24页

作者： Shirodkar, Vaishali Edla, Damodar Reddy Kumari, Annu Department of Computer Science and Engineering National Institute of Technology Goa Kottamoll Plateau Goa Cuncolim403703 India Information Technology Department Goa College of Engineering Farmagudi Goa Ponda403401 India

The area of brain-computer interface research is widely spreading as it has a diverse array of potential applications. Motor imagery classification is a boon to several people with motor impairment. Low accuracy and datasets with few trial recordings present challenges for this classification. This research offers a novel approach to these problems in electroencephalography signal classification for efficient control of neuroprosthetic devices. By using wavelet packet decomposition the proposed method divides the EEG signal into eight frequency bands. Each of these bands were then evaluated by a one-dimensional convolution neural network to find the most discriminative band. Only EEG signals filtered with these bands were used to generate time-frequency images called scalograms using complex Morlet transform. These scalograms were then classified using a two-dimensional convolution neural network optimized for spatial-temporal feature extraction and classification. To mitigate data scarcity, generative adversarial networks were used for effective data augmentation. Experimental evaluation on a benchmark dataset demonstrated substantial improvement: the two-dimensional convolution neural network achieved 93.63% accuracy, surpassing the 85% achieved by the one-dimensional convolution neural network baseline. Precision, recall, and F1-score metrics validated robust performance across motor imagery tasks. Comparative analysis with the latest methods underscored the proposed model’s superiority in accuracy and reliability. Using wavelet transformations, convolution neural network architectures, and generative adversarial network-driven data augmentation presented a promising framework for advancing brain-computer interface applications, particularly in enhancing neuroprosthetic device control. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wavelet decomposition

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Arduino Based Multipurpose Solar Powered Agricultural Robot Capable of Ploughing, Seeding, and Monitoring Plant Health 1

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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Paul, Hrituparna Pandey, Anubhav Pandey, Saurabh Department of Computer Science and Engineering Shambhunath Institute of Engineering and Technology Prayagraj India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

Agribusiness is the primary occupation chosen by more than 40% of the worldwide populace. The types of equipment used to complete a variety of tasks in traditional farming operations are expensive and inadequately made to handle the tasks. So, in order to carry out farming operations, farmers require sophisticated equipment. The proposed study intends to construct a robot that can carry out tasks like grass cutting, spreading seeds and irrigating the plants. The proposed robot does not require an external power source because solar photovoltaic (pv) panels provide its power source. The PIC18F4520 interfaced Android application that controls the complete system transmits the signals to the robot for the necessary operations. Consequently, dc motors are used for seed planting and hard ploughing. For seed planting, consistent separation is maintained. Water is applied to the crop using a sprinkler with revolving nozzles. The grass-cutting tool consists of spinning blades with a knife edge sharpened on both sides to efficiently cut the excess grass. It combines multiple jobs, making it resourceful. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Seed

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Local Binary Pattern (LBP) and Transfer Learning Based Approach to Classify Lung and Colon Cancer

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SN computer science 2024年第6期5卷 783页

作者： Singh, Onkar Singh, Koushlendra Kumar Department of Computer Science and Engineering Manipal University Jaipur Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur

Several genetic disorders and other metabolic abnormalities work together to generate the lethal disease known as cancer. Today’s most contributing factors to mortality and disability in patients are lung and colon cancer. A World Health Organization (WHO) 2020 report listed cancer as one of the leading causes of mortality globally. About 2.735 million of these fatalities were caused by lung and colon cancer combined. A critical component of the patient’s treatment is the diagnosis of lung cancer by histopathology. Therefore, one of the leading research priorities, mostly in the domain of biomedical health information systems, is the identification and categorization of lung and colon cancer. The present article encompasses the Local Binary Pattern (LBP) and transfer learning-based approaches to classify lung as well as colon cancer. LBP has been used for extracting features, and transfer learning has been used for the classification of lung and colon cancer. Histopathological (LC25000) lung and colon datasets are used to validate the proposed methodology. The results of the proposed method have also been compared with different existing methods and reported in the article. Our proposed method has an average accuracy of 99.00% and a F1 score of 99.2%, whereas precision and recall have 99.4% for lung and colon cancer detection. The results of the investigation demonstrate that our suggested technique greatly outperforms current models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Histopathology images Local binary pattern (LBP) Lung cancer Transfer learning

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Unraveling Dyscalculia: Identifying Mathematical Learning Difficulties in Early Education

Unraveling Dyscalculia: Identifying Mathematical Learning Di...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Mukherjee, Kingshuk Kumar, Reitika Vasishat, Sam Bhargava, Naman Upadhyay, Siddhi Muhuri, Samya Thapar Institute of Engineering and Technology Department of Computer Science & Engineering Patiala India

ISBN: (纸本)9798350319019

This study presents a comprehensive intervention approach designed to address dyscalculia in early education. The strategy employs a comprehensive approach that incorporates colour identification, numerical enumeration, mathematical computation, and verbal communication. The initiative seeks to employ a technology for creating interactive learning modules that successfully engage students in various numerical activities. Student progress is evaluated by the application of fuzzy logic, which allows for a thorough understanding of each student's unique learning trajectory. The adaptive approach tailors the difficulty level of exercises based on the student's performance, ensuring a tailored learning experience. This intervention specifically focuses on primary school students, aiming to provide early support and intervention to those who are either at risk of or have already been diagnosed with dyscalculia. The objective of the study is to provide a robust foundation for forthcoming mathematical abilities. © 2024 IEEE.

关键词： Fuzzy logic

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MindCare: Machine Learning based Personalized Mental Health Monitor 4

MindCare: Machine Learning based Personalized Mental Health ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ushashree, P. Naik, Archana Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331540364

Mental health challenges are growing in recent years, emphasizing the need for effective monitoring and inter- vention systems. This paper presents a comprehensive approach to detect mental unstabilities by analyzing the text entered by the user. Utilizing a combination of sentiment analysis and machine learning techniques, the proposed system preprocesses text data to extract sentiment scores and other key features, which are then used to train models for accurate mental health assessments. The system also offers personalized recommendations based on the user's mental state. Experimental results demonstrate the effectiveness of the approach, with the model achieving accuracy of 82.31% for negative text classification. These results underscore the potential of the system to serve as a valuable tool in the early detection and management of mental health issues. © 2024 IEEE.

关键词： Electronic health record

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An Autoencoder-BiLSTM framework for classifying multiple types of lung diseases from CXR images

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Multimedia Tools and Applications 2024年第38期83卷 86091-86120页

作者： Ankayarkanni, B. Sangeetha, P. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Chennai600 119 India Rajiv Gandhi College of Engineering Research and Technology Maharashtra Chandrapur442403 India

Millions of people die from lung illness each year as a result of its rise in recent years. CXR imaging is one of the most widely used and reasonably priced diagnostic techniques for the diagnosis of many illnesses. Unfortunately, even for seasoned radiologists, accurately diagnosing sickness from Chest X-Rays (CXR) samples is challenging. To combat the pandemic, a reliable, affordable, and efficient way to diagnose lung disease has become essential. Consequently, a unique optimized Auto Encod-BI Long-Short Term Memory (Bi-LSTM) model is proposed in this research work. Pre-processing, segmentation, feature extraction, and multiple types of lung illness diagnosis are the four main stages of the suggested model. First, Laplacian filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to pre-process the gathered CXR pictures. Next, the Region of Interest (ROI) from the previously processed images are recognized by means of the newly enhanced MobileNetV2. The new Self-Improved Slime Mould Algorithm (SI-SMA) is used to fine-tune the hyper-parameters of MobileNetV2 in order to precisely identify the afflicted locations. Based on the phenomenon of slime mould oscillation, the conventional Slime Mould Algorithm (SMA) model has been modified with the creation of the SI-SMA model. Next, characteristics like the Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) are taken out. Finally, a unique AutoEncod-BiLSTM Framework—which is divided into three categories—is shown to automate the process of identifying illnesses in CXR pictures: pneumonia, COVID-19, and normal. The autoencoder and Bi-LSTM are combined to create the suggested AutoEncod-BiLSTM model. The retrieved features are used to train the AutoEncod-BiLSTM Framework. Moreover, the proposed model enhanced the disease detection efficiency than the existing models and the disease detection accuracy of the proposed model is about 99.1%. Furthermore, the suggested model attains better

关键词： Equalizers

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