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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Arthy, J. Raja, K. Srm Institute Of Science And Technology Department Of Computer Science And Engineering Chennai Tamilnadu Ramapuram600089 India

ISBN: (纸本)9798350364828

Globally, the search engine is extremely important in reducing the difficulty of information exploration. An internet spider, bot, or program known as a web crawler is used by search engines to maintain an index and retrieve online sites in response to user queries. Web pages are really crawled in order to be explored, starting from the seed URL. In this study, the standard protocols of the literature survey were followed to describe the crawler mechanism also analyzed and compared several research publications to explain the crawler types, individual architectural types, policies, methodologies, metrics, techniques, and equipments for every category was covered. Additionally, the maximum crawler algorithms and the open-source crawler tools were listed and described. This paper gives insight into choosing the optimal crawler algorithm for search engine optimization and for various applications. © 2024 IEEE.

关键词： Web crawler

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Exploiting machine learning and deep learning models for misbehavior detection in VANET

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International Journal of computers and Applications 2022年第11期44卷 1024-1038页

作者： Sultana, Rukhsar Grover, Jyoti Meghwal, Jitesh Tripathi, Meenakshi Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Jaipur India

There is an unprecedented proliferation of vehicular traffic data as the number of interconnected vehicles has increased in Vehicular Ad Hoc Networks (VANET) scenario to realize the goals of Intelligent Transportation Systems (ITS). As a result, the quantity and variety of attacks using transmitted data have risen. However, these broadcasted messages can be used to launch many security attacks as these messages are not encrypted. With the increase in the number of vehicles, the risk of misbehavior also increases as more malicious vehicles broadcast inaccurate and faulty messages. Various trust-based and deterministic misbehavior detection techniques have been proposed to identify such misbehavior. Existing solutions only address specific types of network intrusions. However, because VANET situations may involve a wide spectrum of attacks, a more robust misbehavior detection mechanism is required. This article compares the performance of several machine learning and deep learning architectures using key evaluation metrics. Furthermore, we have demonstrated a comparison of performance metrics with existing learning model-based techniques in different network scenarios. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Vehicular ad hoc networks

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Indian Sign Language Classification using Deep Convolutional Neural Network with Transfer Learning 2

Indian Sign Language Classification using Deep Convolutional...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Mehta, Parjanya Vrishin, K.P. Prajapati, Purvam Chavda, Siddharth Goel, Parth Devang Patel Institute of Advance Research and Technology Computer Science and Engineering Department Changa Anand India

ISBN: (纸本)9798350368413

The Indian Sign Language (ISL) classification model uses Convolutional Neural Networks (CNN) and analysis with Transfer Learning technology to understand ISL gestures by deaf people. This process entails categorizing hand signals into thirty-five groups, which comprise digits 1-9 (9) and alphabets A-Z (26). The developed model is a great support for people who want to know what the deaf say. The ultimate aim of this project is to categorize ISL Gestures into 35 distinct categories. These divisions are made using Convolutional Neural Networks (CNN). To achieve higher accuracy in classification, various transfer learning models such as ResNet50, Xception, DenseNet121, EfficientNetB5 and VGG19 were used in this study. Consequently, ResNet50 achieves an accuracy of 99.96%, Xception at 27.15%, DenseNet121 at 77.94%, and EfficientNetB5 at 50.93%. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Medical Imaging Resolution: Exploring SRGAN for High-Quality Medical Image Reconstruction 2

Enhancing Medical Imaging Resolution: Exploring SRGAN for Hi...

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2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Varshitha, S. Lavanya, N. Shirisha, M. Manmatti, Shilpitha Asha Rani, K.P. Gowrishankar, S. Dr. Ambedkar Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560056 India

ISBN: (纸本)9798350383287

In healthcare diagnostics, medical imaging is essential for accurate diagnosis and treatment planning. A major challenge is the limited resolution of some medical images, which can obscure crucial details and affect diagnostic accuracy, especially in telemedicine. This study examines the use of Super-Resolution Generative Adversarial Networks (SRGANs) to improve the resolution of medical images. SRGANs, known for enhancing conventional images, are adapted for medical imaging using a deep convolutional neural network (CNN) generator and discriminator, with a perceptual loss function guiding the reconstruction process. The research involves training the network on a varied dataset of low-resolution medical images, including MRI, CT scans, and ultrasound, aiming to capture intrinsic features of medical imagery. The performance of the model is assessed using a mix of quantitative and qualitative metrics. © 2024 IEEE.

关键词： Medical imaging

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WDRKNN: Weighted Distance Representation based Nearest Neighbor for Hyperspectral Remote Sensing Imagery Classification 14

WDRKNN: Weighted Distance Representation based Nearest Neigh...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sharma, Monika Biswas, Mantosh National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India University of Delhi Department of Computer Science Delhi India

ISBN: (纸本)9798350335095

Hyperspectral image (HSI) classification has enormous importance in the era of remote sensing and extensively sourced in a variety of promising applications. Most HSI classifications use the K-nearest neighbor (KNN) method due to its efficiency and simplicity. Although the KNN-based classification has lot of benefits, while this classification method has two big issues: firstly, the sensitivity of the k-neighborhoods affects KNN performance and secondly, moderate majority voting utilized there especially if the size of samples is quite small. To tackle these outliers, this paper suggests the weighted distance representation-based K nearest neighbor (WDRKNN) approach for improving the image classification results. In proposed WDRKNN, initially for each pattern class k nearest neighbors belongs to testing data samples are chosen and it has been employed to imply the testing data sample as their linear combinations. The localities related to the k-nearest neighbors for each pattern class represent as the weighted distance restrict their subsequent representations related constants. Decisively, the testing sample will be classified to a specific pattern class by the WDRKNN using the least representation-based distance between the classes. Experimental assessment on two HSIs: Pavia University and Indian Pines confirms that the suggested structure can significantly enhance HSI classification as compared to related state-of-the-art methodologies. © 2023 IEEE.

关键词： Remote sensing

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Sentiment Analysis in Gujarati Language with Dictionary Approach 6th

Sentiment Analysis in Gujarati Language with Dictionary Ap...

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6th International Conference on Information Systems and Management Science, ISMS 2023

作者： Dudhia, Devanshu J. Rana, Dipti Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India

ISBN: (纸本)9783031707889

In recent years, due to the availability of voluminous data on the web for Indian languages, it has become an important task to analyze this data to retrieve useful information. Because of the growth of Indian language content, it is beneficial to utilize this explosion of data. In this research, the deliberate choice of Gujarati language for sentiment analysis stems from a commitment to capture the distinct linguistic and cultural characteristics of the Gujarat region. For the purpose of sentiment analysis, created a Gujarati tweets dataset by crawling on particular hashtags. Once the dataset is prepared, performed all necessary pre-processing steps on our Gujarati dataset of tweets. After that, perform the sentiment analysis using a dictionary approach. In this approach, created dictionaries of positive and negative words manually and also took the help of some research work as well. With the help of dictionaries, prepared the training dataset and testing dataset and apply different classification algorithms on it. Through this survey, we get to know different methods used for sentiment analysis as it is used to determine the sentiment or emotion expressed in a piece of text, whether positive or negative. Also, get to know how to count the sentiment score of Gujarati tweets. Additionally, we explore the significance of our dataset and its potential for fostering future research endeavors in Gujarati language analysis. This comprehensive analysis contributes to a deeper understanding of sentiment dynamics in Gujarati, with implications for diverse applications in natural language processing and cultural studies. There are many potential applications for sentiment analysis such as marketing and customer service, social media monitoring, political analysis, public health, financial analysis, disaster response, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Tweets

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Real-time Obesity and Heart Disease Monitoring with IoT and Deep Learning

Real-time Obesity and Heart Disease Monitoring with IoT and ...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Singh, Jagendra Govindaraju, P. Kumar, Vinish Yamsani, Nagendar Umamaheswari, J. Munagala, Madhu Bennett University School of Computer Science Engineering & Technology Greater Noida India K S R Institute for Engineering and Technology Department of Electronics and Communication Engineering Tiruchengode India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India SR University School of Computer Science and Artificial Intelligence Warangal India Brainware University Department of Computer Science Kolkata India Koneru Lakshmaiah Education Foundation Department of of English Vaddeswaram India

ISBN: (纸本)9798350394412

The accelerating spread of junk food consumption is progressively putting the younger generations at risk of obesity. The concept of health prioritization is deeply rooted in the development of nurturing and general welfare in society. The job has further become more complex by the glaring statistics of people across the globe who are fighting obesity. The World Health Organization (WHO) puts a staggering estimate that approximately 1 billion people across the globe suffer from obesity-related issues, a statistic heavy in terms of impact primarily because obesity greatly affects one's health but also holds fatal risks. Diagnosing the early stages of obesity can just be simple and prevent one from being obese;however, several obstacles stand in the way of its early diagnosis. Symptoms or signs that one is in the trajectory of suffering from obesity generally go unnoticed by the individual, and the availability of specialists to diagnose such risks persistently on time is not always possible. In this regard, the current paper shall showcase an application hitherto unknown using cloud-based diagnosis, in collaboration with machine learning through Secure Access Internet of Things to predict obesity-related risks without having to depend on glaring physical parameter indicators - in this regard, redefining how such information could be sourced even without the need of regularly visiting health professional people. To ensure the best possible health predictability from this machine learning, we gathered and plotted the datasets consisting of obese and overweight people. Other than machine learning, we use a Secure Access IoT sensor network. This allows for real-time monitoring and diagnosis, making early detection achievable. Consequently, this system paves the way for mitigating and substantially reducing the complications associated with obesity. © 2024 IEEE.

关键词： Nutrition

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Ensuring Data Security and Routing Efficiency in Warehouse Sensor Networks Using Wireless Blockchain technology 11th

Ensuring Data Security and Routing Efficiency in Warehouse S...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Anitha Rajakumari, P. Karthick, S. Mahapatra, Rajendra Prasad Parwekar, Pritee Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad India

ISBN: (纸本)9789819717231

The integration of wireless sensor networks (WSNs) and blockchain technology has gained significant attention in recent years due to its potential in addressing security and privacy concerns in various applications. In this paper, we propose blockchain technology with wireless-based secure warehouse system (BCWS2) that leverages wireless blockchain technology along with an efficient routing policy to ensure data integrity, authenticity, and confidentiality in a warehouse environment. The proposed system consists of a network of battery-powered wireless sensors deployed within the warehouse premises to monitor various environmental parameters such as temperature, humidity, and light levels. These sensors collect data and transmit it securely to a blockchain network using cryptographic techniques to prevent unauthorized access and tampering. The blockchain network, powered by smart contracts, acts as a decentralized ledger, ensuring the immutability and transparency of the collected sensor data. Each sensor node contributes to the blockchain by forming a consensus on the validity of the data through a distributed consensus protocol. This enables secure and tamper-proof storage of the collected data, eliminating the need for a central authority or trusted intermediary. In order to enhance routing efficiency within the warehouse sensor network, we present a highly efficient routing policy that leverages machine learning algorithms. Our proposed routing policy aims to optimize the path selection process, ensuring effective and streamlined communication within the network. The policy dynamically adjusts the routing paths based on the current network conditions, such as node congestion, battery levels, and signal strength. The proposed routing approach significantly enhances network performance, minimizes energy consumption, and guarantees prompt delivery of sensor data to the blockchain network. This system effectively tackles various challenges encountered in conventiona

关键词： Blockchain

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Enhancing Sarcasm Detection with Contextual Features Through Multi Layered Perceptron Model 2

Enhancing Sarcasm Detection with Contextual Features Through...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Jannu Soloman, Shyam Sunder Baydeti, Nagaraju National Institute of Technology Nagaland Department of Computer Science and Engineering Nagaland Chumukedima797 103 India

ISBN: (纸本)9798350366570

Sarcasm Detection is a challenging task due to its often nuanced and context-dependent nature. Sarcasm detection has emerged as a critical area in natural language processing, finding applications in sentiment analysis, social media monitoring and customer feedback analysis. The proposed model focuses on the advancing the accuracy and robustness of sarcasm detection model by integrating contextual features and employing sophisticated neural network architecture. The primary objective of the proposed work is to analyze textual data and obtain the best possible features. Features such as sarcasymbols, attenuation indicators and selective stopwords are carefully selected to enhance the model's ability to detect subtle sarcasm cues. By utilizing vector representation of textual data, extracted features and deep neural network, the proposed model identifies sarcasm from textual data. To evaluate the performance of the approaches in the proposed model, the metrics F1-Score and accuracy are used. The proposed model resulted in an impressive accuracy and F1 Score for the binary classification problem in bifurcating the text as sarcastic and non-sarcastic with 86.31 and 87.17 percentages respectively. © 2024 IEEE.

关键词： Deep neural networks

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Enhancing Robustness of Deep Learning Model for Lung Cancer Detection: Adversarial Training and Gradient-based Interpretability

Enhancing Robustness of Deep Learning Model for Lung Cancer ...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Srivastava, Pragya Mishra, Asha Rani Chauhan, Sansar Singh Department of Computer Science and Engineering GL Bajaj Institute of Technology and Management UP Greater Noida India

ISBN: (纸本)9798350355338

Lung cancer is a common and deadly type of cancer that is found universal. Identifying it early is crucial for improving patient results. Alternatively, it can be difficult because of the complexity of analysing radiological images. This research presents a robust deep learning model for detecting cancerous lesions in chest CT scan images. The model, using the ResNet50 architecture, experience training and evaluation using a dataset comprising interpreted chest CT scans obtained from Kaggle. The dataset was split into training, validation, and test sets, ensuring an equal distribution of cancerous and non-cancerous scans. After comprehensive training and validation, the model achieved an impressive accuracy of 99.68% in distinguishing between cancerous and non-cancerous images. Furthermore, the utilization of Grad-CAM visualization techniques has augmented the interpretability of the model, offering substantial discernment into the precise areas of interest within the CT scan images. Moreover, the model has shown flexibility in opposing adversarial attacks, thereby emphasizing its dependability in clinical environments. In summary, this study highlights the capacity of deep learning models to enhance diagnostic precision in healthcare, thereby potentially improving patient care and outcomes. © 2024 IEEE.

关键词： Lung cancer

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