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Detection of Bone Cancer using Machine Learning: A Multi-Model Approach 3

Detection of Bone Cancer using Machine Learning: A Multi-Mod...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Enuga, Pushya Saie Raag Singh, Sanjali Santhi, V. Vellore Institute of Technology School of Computer Science and Engg. Chennai India

ISBN: (纸本)9798331509675

Bone cancer diagnosis remains a critical challenge due to the intricate nature of medical imaging data and the need for precise interpretation. This study introduces a machine learning framework leveraging Convolutional Neural Networks (CNNs) and transfer learning to automate bone cancer detection. We benchmarked four models - ResNet50, ResNet101, EfficientNet-B7, and a custom lightweight CNN - on a dataset comprising over 8,000 X-ray images. Among these, ResNet101 demonstrated the highest diagnostic performance, achieving an accuracy of 94.46 %, which surpasses ResNet50 by 4.26 %, EfficientNet-B7 by 32.46 %, and the custom CNN by 7.39 %. The custom CNN, designed for low-power scenarios, achieved a commendable accuracy of 87.07 %, highlighting its suitability for resource-constrained environments. Data augmentation techniques, including random flipping, rotation (±20°), zooming (0.8 to 1.2), and contrast adjustment (±20 %), enhanced model generalization. Grad-CAM visualizations offered interpretable insights into model predictions, empowering clinicians with clear decision-making frameworks. Comparative analysis with existing literature underscores significant advancements, identifies remaining gaps, and suggests opportunities for future research. This work highlights the promise of CNN-based solutions in advancing medical diagnostics and provides a roadmap for deploying efficient AI systems in clinical practice. © 2025 IEEE.

关键词： Convolutional neural networks

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Adapting to Evasive Tactics through Resilient Adversarial Machine Learning for Malware Detection 18

Adapting to Evasive Tactics through Resilient Adversarial Ma...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Krishna, G. Bala Sravan Kumar, G. Ramachandra, Mummadi Sampurnima Pattem, K. Rani, D. Sandhya Kakarla, Geeta Cvr College of Engineering Department of Computer Science Engg. Rangareddy Ibrahimpatnam India Cvr College of Engineering Department of Computer Science Engg.-Cyber Security Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Hyderabad India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

This paper presents the Adaptive Resilience-based Convolutional Network (ARCNet), a sophisticated machine learning framework specifically designed to detect advanced, evasive malware. ARCNet combines convolutional and recurrent neural networks, making it highly adaptable to changing cyber threats. Its core components, the Adversarial Learning Module (ALM), Predictive Analysis Engine (PAE), and Dynamic Adaptation System (DAS), significantly boost its detection power. Tests using a synthetic dataset show ARCNet's superiority over traditional models like the Support Vector Machine (SVM). It achieved 95.2% accuracy under normal conditions (compared to SVM's 89.4%) and maintained 92.5% accuracy even during adversarial attacks (against SVM's 80.3%). Notably, ARCNet's detection rates improved from 78.5% to 86.7% in five months after integrating the DAS. These results confirm ARCNet's efficiency in tackling complex malware challenges, contributing greatly to cybersecurity. The study underscores the importance of evolving and enhancing machine learning methods to keep pace with the rapidly changing landscape of cyber threats. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Block chain Blockchains Challenges of Block chain Consensus algorithm Data privacy Digital signatures Distributed ledger Faces Organizations Smart contracts

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Synergistic Skin Cancer Classification: Vision Transformer alongside MobileNetV2 4

Synergistic Skin Cancer Classification: Vision Transformer a...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Kumar, M Ranjith Priyanga, S. Anusha, Janani Srinivasan Chatiyode, Veda Santiago, Jerome Revathi, P. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Amrita School of Computing Dept. of Computer Science and Engg. Amrita Vishwa Vidyapeetham Chennai India C. Abdul Hakeem College of Engg. and Tech. Dept. of Computer Science and Engg. Melvisharam Vellore India

ISBN: (纸本)9798350349900

Skin cancer is one of the most common types of cancer in the world, and it poses major health risks due to its ability to spread quickly and metastasize. Early and accurate identification is crucial for treatment success and improved patient outcomes. This proposed work combines the MobileNetV2 and Vision Transformer (ViT) architectures to create a hybrid automated skin cancer classification technique. This technique aims to increase the accuracy and efficiency of dermatological diagnosis tools by combining MobileNetV2's effective feature extraction capabilities with ViT's self-attention mechanism. After testing on the HAM10000 dataset, this hybrid model outperformed individual models with a remarkable 96.3% classification accuracy. Not only did the integration of ViT and MobileNetV2 improve the classification accuracy but it also demonstrated how various deep-learning architectures work together to tackle complex image analysis tasks. Classifying skin cancers is critical to the medical industry because it allows for the early diagnosis of various dermatological disorders, allowing prompt intervention and treatment. These benefits can eventually improve patient outcomes and save lives in huge numbers. The findings of this research highlight the potential of deep learning to transform dermatological diagnostics and open the door to creating systems that will significantly impact clinical practice by detecting skin cancer with greater accuracy and efficiency. © 2024 IEEE.

关键词： Lung cancer

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Assessing the Accuracy of Regression and Classification Models in Air Quality Prediction 5

Assessing the Accuracy of Regression and Classification Mode...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Vishwakarma, Harsh Kumar Guru Prasad, M.S. Mittal, Vanshika Mittal, Mudit Naik, PavanKumar Chandrappa, S. Dept. of Computer Science and Engg. Dehradun India School of Computing and Information Technology Reva University Bangalore India Dept. of Computer Science and Engg. Bengaluru India

ISBN: (纸本)9798350356236

This research paper addresses the pressing issue of air quality monitoring and prediction using machine learning algorithms. With a dataset comprising pollutant levels from various states, we employed a comprehensive approach to data analysis, visualization, and preprocessing. The study aimed to predict the Air Quality Index related to key pollutants such as SO2, NO2, RSPM, and SPM. We utilized linear regression, decision tree regression, and random forest regression for prediction tasks and logistic regression, decision tree classification, random forest classification, and k-nearest neighbors for classification tasks. The proposed method was evaluated using different performance matrixes for regression and classification tasks, respectively. The results demonstrated the usefulness of the proposed models in predicting and classifying air quality levels. The proposed model provides a robust framework for air quality management and can be a valuable tool for environmental monitoring and policymaking. © 2025 IEEE.

关键词： Random forests

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Identification of Affective States from Malayalam Audios Based on Valence Arousal Prediction

Identification of Affective States from Malayalam Audios Bas...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： George, Talit Sara George, Jini Pournami, P.N. National Institute of Technology Dept. of Computer Science and Engg. Calicut India Providence College of Engineering Dept. of Computer Science and Engg. Chengannur India

ISBN: (纸本)9798331540067

Affective state plays a key role in emotion recognition, influencing various applications from mental health evaluation to human-machine interaction. These states can be described using two primary aspects: valence, which indicates the pleasantness of the speech, and arousal, which relates to the intensity of the emotion. This study aims to automate the prediction of valence and arousal values from speech data, addressing the limitations of manual annotation and human error. The IIITSAINT-EMOMDB dataset, created from Malayalam films, uses the Circumplex Model of Affect to map emotions such as happy, sad, angry, and calm. The approach leverages machine learning models, including Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), for prediction. Experimental results demonstrate that the traditional models like SVR and ANN are outperformed by the CNN model, which achieved a mean squared error (MSE) of 0.3 for valence and 0.1 for arousal. These findings highlight the power of automated emotion recognition in large-scale audio datasets, offering promising applications in affective computing and psychological analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Early Detection of Brain Aneurysms: A CNN-Driven, Real-Time Approach with Angiography Imaging 5

Enhancing Early Detection of Brain Aneurysms: A CNN-Driven, ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Aruna Kumara, B. Gudodagi, Raveendra Madan, H.T. Kavitha, D.D. Manjunatha, H.M. Proudhadevaraya Instititute of Technology Dept. of Computer Science & Engg. Hosapete India School of Electronics and Communication Engg. REVA University Bengaluru India NMAM Institute of Technology Nitte India HKBK College of Engineering Dept. of Computer Science & Engg. Bengaluru India Bapuji Institute of Engineering and Technology Electrical & Electronics Engg. Davanagere India

ISBN: (纸本)9798331518097

Brain aneurysm is a swell or balloon in a blood vessel in the brain that can cause severe health problems to the patients. Due to its potentiality, if it ruptures it leads to serious neurological disorder such as: coma, hemorrhagic stroke, and even death. Early identification of brain aneurysm plays a crucial role in treatment. However, the traditional methods such as computerized Tomographic Angiography (CTA), Magnetic Resonance Imaging (MRI) takes more time and complex procedures. The relentless advancement of medical imaging technologies continues to transform diagnostic paradigms in neurology. In this context, this study integrates the convolutional neural networks (CNNs) into real-time brain aneurysm detection from angiography image. This paper presents a CNN-based model designed to rapidly and accurately identify cerebral aneurysms from angiography images. The proposed method, initially prepares the dataset by collecting diverse angiography images from medical archives. Later, the images undergoes with various preprocessing methods such as: denoising, contrast enhancement, normalization, resizing, and augmentation to enhance the quality of data, and model generalization. Then, the method automatically extracts the blood vessel structure and aneurysm characteristics such as: irregular shapes, unusual bulges, and densities within the vessels as features. Finally, the extracted features passed through the connected layers and the CNN classifies the whether an image contains aneurysm or not. The CNN model is trained with stochastic gradient descent (SGD) algorithm, and the trained model is deployed on a standard Graphics Processing Unit (GPU) for real time inference. The results showed that, the performance of the method was superior compared to the traditional methods. © 2024 IEEE.

关键词： computerized tomography

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Deep Learning-Based Approach for Calorie Estimation in Indian Foods 2

Deep Learning-Based Approach for Calorie Estimation in India...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Chauhan, Naman Revathy, S. Murali, P. Basker, N. Saranya, S. Natarajan, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Electronics and Communication Engg. Chennai Ramapuram India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engg. Chennai India Sona College of Technology Department of Computer Science and Engg. Salem India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India School of Computer Science and Engg Vellore Institue of Technology Chennai Campus India

ISBN: (纸本)9798350373110

People in the modern era are increasingly concerned with their diet and food choices to prevent developing chronic diseases like high blood pressure and diabetes. With the increasing usage of smartphones and technology, an application to automatically monitor their diet would be a great boon, especially for the younger generation. This research work investigates the process of estimating the calories present in the food images. This research work was carried out in three phases. The first phase involves the categorization of food images, such as boiled, fried in oil, or junk. This research work introduces a novel deep neural model by incorporating the MobileNet-V2 framework. In the second phase, the quantity of the labelled food is estimated. Further, it involves computing the calories present in the food using the Google search API. The details of the ingredients present in each type of food are used for this purpose. Classification accuracy, food amount calculation precision, and calorie estimation accuracy are used to evaluate the workings of the proposed model. This research work has secured plausible results with an overall accuracy of 95.21%, comparatively better than the existing approaches. © 2024 IEEE.

关键词： Calorie Estimation Deep Learning Google Search API Image Classification MobileNet-V2 Neural Network

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Performance Analysis of a Scalable Federated Learning-Based Agricultural Network 4

Performance Analysis of a Scalable Federated Learning-Based ...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Chakraborty, Shatanik Sarkar, Maharnab Gayen, Avijit Jana, Angshuman Chakraborty, Nilotpal Meghnad Saha Institute of Technology Dept. of Computer Science & Engg. Kolkata India Govt. College Of Engineering & Textile Technology Dept. of Computer Science & Engg. Berhampore India Indian Institute of Information Technology Dept. of Computer Science & Engg. Guwahati India Techno India University Dept. of Computer Science & Engg. West Bengal India

ISBN: (纸本)9798350387063

Although agriculture is a substantial part of the Indian economy, it encounters several challenges. Precision agri-culture is a farming management strategy that aims to overcome these challenges. It optimizes several components of the farming process using data and technology, resulting in increased crop yields while reducing waste and raising total resource use effi-ciency. Thus, in its context, we present a study on the development of a Federated Learning-based Agricultural Network consisting of a popular federated learning algorithm FedAvg to perform image classification on an open dataset, Plant Village. In this work, we specifically analyze the performance of the proposed Federated Learning-based Agricultural Network in terms of its scalable deployment. The proposed network yields an average classification accuracy of 97.52 % on test data (C ≈ 70), and provides a host of other benefits it derives from its federated nature, such as upholding the privacy of local client data as only the model updates are shared between the clients and the aggregation server. The prime objective of our work has been to study and explore the possibility of applying federated learning in the agriculture sector so that more efficient and privacy- preserving smart farming networks could be developed. © 2024 IEEE.

关键词： Agribusiness

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Mask Region-Based Convolutional Neural Network for Disease Detection in Rice Leaf 3

Mask Region-Based Convolutional Neural Network for Disease D...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Putta, Narsaiah Kumari, T. Suvarna Naik, K Jairam Vasavi College of Engineering Dept. of Computer Science & Engg. Telangana Hyderabad India Chaitanya Bharathi Institute of Technology Dept. of Computer Science & Engg. Telangana Hyderabad India National Institute of Technology Raipur Dept. of Computer Science & Engg. Chhattisgarh India

ISBN: (纸本)9798350354171

Many approaches have been proposed for the problems, like manual diagnosis of crops and soil by experts. But this one is actually a very time-consuming activity. With the advancement of technology, many approaches have been made using machine learning, like image processing techniques, deep convolutional neural networks, segmentation techniques based on neutrosophic logic, etc. However, most of these methods were slow or had implemented complex algorithms that made them unsuitable for being used as an Android app. So, in this paper, a rice leaf disease detection and classification model using region-based convolutional neural networks and deep learning has been proposed. Using this approach, we have successfully developed a model to detect and classify diseases with an accuracy of 94.4%. Determining the type of disease in crops may help the farmers take timely measures and, therefore, avoid the loss. © 2024 IEEE.

关键词： Convolutional neural networks

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A Synergistic Ensemble Approach for Enhanced Time Series Forecasting in Crime Analytics

A Synergistic Ensemble Approach for Enhanced Time Series For...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Rani, Reenu Mehta, Rohan Israr, Sabeeh Tamboli, Umar Rohith, M. Chavan, Sanvi School of Advanced Sciences Vellore Institute of Technology Vellore India School of Computer Science Engg. & Info. Systems Vellore Institute of Technology Vellore India School of Computer Science & Engg. Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350379716

This study proposes a novel ensemble approach of RF Regression, ANNs, and ARIMA for time series forecasting. To address the objectives of the present research, sensitive and restricted crime data obtained from the Mumbai police has been used. It covers a number of years and entails detailed records of various categories of offenses that have been committed in the area monthly. Several techniques of data preprocessing are applied to ensure that the data in the given set is fit to train the model and they include data cleaning, scaling, and handling of missing values. The hybrid ANN-ARIMA-RF approach captures the special advantages of each component technology: ANNs are employed for nonlinear patterns while ARIMA is employed for the linear patterns in residuals and RF is applied on both models outputs. The hybrid of ARIMA and ANN outperforms the individual ARIMA and ANN models when applied to the exclusive crime dataset to forecast future crime rates. This is because the developed model improves the prediction accuracy as shown by the lowest Mean Absolute Percentage Error. This study differs from other works due to the utilization of a unique dataset and the integration of several forecasting methods that improve upon the individual ones. © 2024 IEEE.

关键词： Prediction models

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