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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Dino, P. Ashwin Lakshmanan, S. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350365269

A brain tumor is a highly persistent disease, with over 84,000 people expected to receive a primary brain tumor diagnosis in 2023, resulting in an estimated 24,000 deaths from meningioma tumors in the same year. Magnetic resonance imaging (MRI) is the most effective technique for detecting brain tumors. Neural networks have demonstrated remarkable accuracy in solving image classification and segmentation problems. In this paper, we present a multimodal accurate classification based on neural networks to identify and classify brain tumors into glioma, meningioma, pituitary, or no tumors. Using different optimizer models has been integral to achieving higher accuracy, resulting in MobileNetV2 and MobileNetV3 consequently producing improved results for the multi-class tumor classification model. The proposed model exhibits significant performance improvements and excellent validation accuracy. © 2024 IEEE.

关键词： Tumors

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Advancing Football Game Analysis: Integrating computer Vision, Deep Learning, and Hybrid Techniques for Enhanced Video Analytics

Advancing Football Game Analysis: Integrating Computer Visio...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Benakesh, A.H. Rajeev, R. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350373783

This research is about football analytics with the help of deep learning and computer vision, through which we can track the movement of the ball and all ball possessions a particular team has. This will assist teams in properly analyzing their game and identifying areas where they may be lacking. The fusion of YOLO which is one of the most popular model architectures and object detection algorithm and deep learning techniques like 3DCNN, CNN, Faster R-CNN, LSTM, BLSTM and computer vision has revolutionized the landscape of sports analytics, particularly in the domain of football (soccer). This abstract provides a glimpse into the exciting field of football analytics through the lens of advanced technologies. In recent years, the application of deep learning models to football data has enabled the extraction of invaluable insight from the game. This abstract serves as a gateway to the exciting world of football analytics, where deep learning and computer vision come together to revolutionize the way we perceive and engage with the beautiful game. The possibilities are limitless, as ongoing research continues to refine models and algorithms, promising even deeper insights and a richer experience for football enthusiasts worldwide. © 2024 IEEE.

关键词： Gateways (computer networks)

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Enhancing Remote Sensing Image Classification: A Strategic Integration of Deep Learning Technique and Transfer Learning Approach 2

Enhancing Remote Sensing Image Classification: A Strategic I...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Yogesh, T. Devi, S. Vinita Sushila Hindustan Institute Of Technology And Science Department Of Computer Applications Chennai India

ISBN: (纸本)9798350381665

Image classification within the realm of remote sensing technology plays a vital role across various sectors, including military operations, geographical mapping, surveillance activities, and environmental monitoring. Procurement of sharp and accurate satellite imagery is presently uncomplicated. This investigation is geared toward implementing image classification through a sophisticated deep learning algorithm and assessing its prognostic capabilities. This study employs a comprehensive dataset comprising 5,631 satellite images, encompassing representations of cloud-covered regions, desert landscapes, verdant territories, and aquatic zones. The evolution and widespread adoption of deep neural networks, along with their methodologies, have experienced substantial growth. This research harnesses one of the most pivotal deep learning frameworks, Convolutional Neural Networks (CNNs). The efficacy of the CNN model deployed here is noteworthy, specifically employing the VGG-16 CNN architecture. The model exhibits significant performance improvement and achieves excellent validation accuracy. The evaluation findings demonstrate the model's strong ability to categorize satellite imagery accurately, which strengthens its generalization capacity. © 2024 IEEE.

关键词： Remote sensing

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Classification of melanoma skin cancer using convolutional neural network

Classification of melanoma skin cancer using convolutional n...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Lokesh, S. France, K. Eliyas, Sherine Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350378092

One of the deadliest illnesses in the world is skin cancer. Accurately diagnosing skin lesions in their early stages might help clinicians make better decisions by raising the likelihood of a cure before cancer develops. However, due to the imbalance and scarcity of most skin illness photos utilized for training, obtaining automated skin cancer classification is challenging. The categorization of skin cancer has recently made extensive use of a variety of deep learning-based techniques to address the aforementioned problems and provide positive outcomes. The topic of skin cancer categorization is examined in this study. The suggested study uses CNN learning models to classify skin cancer to overcome this issue. Our research has led us to the conclusion that 82% accuracy is being used. © 2024 IEEE.

关键词： Lung cancer

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Leveraging Machine Learning algorithms for Fake Profile Detection on Instagram 7

Leveraging Machine Learning algorithms for Fake Profile Dete...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Arunprakaash, R.R. Nathiya, R. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350372816

In recent years, Instagram has rapidly become one of the world's largest platforms for shedding pictures and videos and running businesses online. However, despite its widespread worldwide popularity, the platform faces severe challenges due to fake profiles that spread spam and offer security threats to users. Instagram is now working on Powerful machine learning systems - advanced technology for combating fraud online - to discover and break contacts with fraudulent accounts (often infringe IP rights). Using data sets from Kaggle, which present the attributes of profiles, posting behaviours, and follower demographics, we begin an exploratory analysis and data cleanup process. Following, we test the efficacy of three classification algorithms - Random forests, linear support vector machines, and K-nearest neighbours - to construct models and reciprocate them on our scrubbed data. If the random forest classifier is the most effective, it reaches over 92.5% accuracy in recognizing false accounts. This study reflects the strategic use of machine learning techniques to reinforce safety and satisfaction across social networks. Finally, the paper discusses how time dynamics, network structure, and high-level language processing may, in turn, promote the success of these efforts to benefit users. © 2024 IEEE.

关键词： and KNeighbors Classifier Fake Profile Detection Instagram Linear SVC Machine Learning Random Forest Classifier

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Deep Residual Network for Effective Lung Cancer Detection on Computed Tomography Images 2

Deep Residual Network for Effective Lung Cancer Detection on...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Hema Ambiha, A. Thiyagarajan, M. Rajamohanan, Rajasree Appu, M. Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India NGM College Department of Computer Applications Pollachi India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sree Narayana Guru College Department of Computer Science Coimbatore India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383652

Lung cancer has been listed as being one of the world's largest deadliest diseases. Lung diseases cause damage to lungs and can cause shortness of breath, turning fatal, if not treated at an early stage. The primary objective of this study is to identify malignant lung nodules in an input lung image and to categorize lung cancer and its severity. This study employs unique Deep learning approaches to determine the location of malignant lung nodules. The proposed approach uses Residual Network to detect and predict lung cancer from patients' computer Tomography (CT) scan images with hyper parameter optimization. The results are compared with Convolutional Neural Network, VGG Net, and Inception v3 architectures. The proposed approach attained an accuracy of 93.85 percent which is better than the existing approaches. © 2024 IEEE.

关键词： Lung cancer

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Bilingual Approach: Leveraging Deep Neural Network Techniques for Handwritten Signature Authentication

Bilingual Approach: Leveraging Deep Neural Network Technique...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Mohanraj, P. Janit, R. Sophia Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350382693

In an era defined by technological innovation, the importance of robust authentication mechanisms cannot be overstated, particularly in the realm of security management. Handwritten signature recognition stands as a pivotal component in identity verification processes. Our system harnesses the power of Deep Neural Networks (DNNs) to extract intricate hierarchical features from signature images, effectively capturing both global and local patterns with remarkable precision. To assess and confirm the effectiveness of our algorithm in distinguishing genuine signatures from fraudulent ones, we carefully compile datasets containing both new and existing signatures. Additionally, our work introduces an offline human signature system, underpinned by the sophisticated Histogram Equalization method, further augmenting the robustness of our approach. Our approach, which is based on research utilised in the educational system, is very helpful in protecting and validating academic documents. The addition of bilingual features increases the usefulness of our system by supporting a variety of linguistic signatures that are frequently seen in educational materials. This method guarantees a thorough and flexible solution for handwritten signature authentication in security and educational institutions. © 2024 IEEE.

关键词： Authentication Protocol

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A comparative analysis of using Deep Learning and Machine Learning technologies for intrusion detection for effective network traffic analysis 7

A comparative analysis of using Deep Learning and Machine Le...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Divya, S.B. Mary, S. Prince Department of Computer Science and Engineering Sathyabhama Institute of Science & Technology Chennai India Mangalam College of Engineering Department of Computer Applications India

ISBN: (纸本)9798350372816

The broad utilization of interconnectivity and interoperability of processing frameworks have turned into an irreplaceable need to improve our everyday activities. All the while, it opens a way to exploitable weaknesses that work out positively past human control capacity. The weaknesses consider network protection instruments fundamental to accepting correspondence trade. Secure correspondence requires safety efforts to battle the dangers and necessities headways to safety efforts that counter advancing security dangers. So, emerging technologies like Deep Learning/ Machine Learning (DL/ML) come in handy for integration and better prediction. So, in this paper, we depict various deep learning/ML strategies used in detecting the imbalance in network traffic. The document also includes research that has been proposed on the topic of intrusion detection in network traffic over the previous four years (2020-2024) by a variety of research specialists. The main achievements of the security measures are recognized in this review together with their quantitative and qualitative success indicators. Additionally, as a guide for upcoming discoveries, this research study examines the major discoveries and the rationale for the lessons discovered. © 2024 IEEE.

关键词： Network intrusion

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A deep multi-filtering network based on dilated mask RCNN framework with self-attention RNN model for segmenting and classifying bone fracture

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Multimedia Tools and applications 2024年 1-27页

作者： Jothi, R. Jayanthi, K. Department of Computer and Information Science Annamalai University Tamil Nadu Chidambaram India Department of Computer Applications Government Arts College C-Mutlur Tamil Nadu Chidambaram India

Accurate bone fracture identification is still a tough problem in medical imaging due to the difficulty of detecting fractures concealed by overlapping structures, varying picture quality, and minute fracture features. To tackle these difficulties, this study presents a comprehensive image processing pipeline designed to improve the quality and utility of input bone images for subsequent segmentation and classification of bone fracture. The preprocessing phase employs advanced techniques such as histogram equalization and guided filtering to significantly enhance image quality. One of the key highlights of the approach is the utilization of image augmentation, which transforms input images into multiple dimensions, thereby augmenting the dataset and increasing its diversity. This enables our model to generalize better and produce more robust results. Moving on to the segmentation stage, we introduce a novel approach by integrating the dilated region-based convolutional neural network (MRCNN) with the strategic application of recurrent Kalman filters (RKF) and recurrent fuzzy filters (RFF). This innovative architecture enhances the segmentation process while effectively addressing image compression issues and preventing information loss. As a result, our segmentation procedure achieves remarkable accuracy and efficiency. Following successful segmentation, we focus on feature extraction and classification. Feature vectors extracted from the dilated MRCNN are seamlessly integrated into the features extracted from the segmented image using techniques such as local binary pattern (LBP), histogram of oriented gradients (HOG), and speeded up robust features (SURF). Then the Self-Attention Recurrent Neural Network (SA-RNN) is utilized for the classification of bone images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Kalman filters

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Deep analysis of MFCC and MEL spectrogram features to recognize and classify stuttered speech

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Multimedia Tools and applications 2025年 1-20页

作者： Banerjee, Nilanjan Sethi, Nilambar Borah, Samarjeet Department of Computer Science and Engineering GIET University Odisha India Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India

Human thoughts, feelings, and ideas are expressed through speech. Stuttering, also referred to as stammering, is an impediment to speech that affects millions of people across the globe. Stuttering speech recognition is a good deal of research within the realm of speech signal processing. Classification of eight different types of stuttering including fluent, interjections, broken words, prolongations, word repetitions, phrase–repetitions, part-word repetitions, and sound repetitions is the main motivation in this research. Stuttering speech recognition is becoming easier with the advancement of machine learning and deep learning. The present study focuses on the efficacy of stuttering speech recognition through the application of machine learning & deep learning. Analysis in this study began with traditional machine learning algorithms, such as Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). It is seen, feature extraction is an essential component in achieving accurate classification for machine learning algorithms. MFCC & MEL spectrogram features are extracted for this experiment. Furthermore, Delta2 MFCC and Delta2 Mel Spectrogram are derived by second-order derivatives on MFCC & Mel Spectrogram features. Therefore, a variety of these features are employed as input to the model. In comparison with all the aforesaid traditional machine learning models, the KNN model results in the highest accuracy of 69.1% based on MFCC-40 input features. To improve accuracy, we move to a deep learning approach. In this experiment, the convolutional neural network (CNN) model produces better outcomes than traditional machine learning. The study presents a comprehensive analysis of the CNN model with MFCC & MEL spectrogram features, as well as its efficiency. It is noted that MFCC-40 input features in CNN model results in the highest accuracy of 89% after 400 epochs. © The Author(s), under exclusive licence to Springer science+Bus

关键词： Feature Selection

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