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检索条件"机构=Department of Computer Science and Engineering-AI&ML"

共 779 条记录，以下是411-420 订阅

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Advancing Medical Imaging: A Focus on Efficient Net for Brain Tumor Classification

Advancing Medical Imaging: A Focus on Efficient Net for Brai...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： R Shashidhar R Manasa K M Megha P Priyanga A S Manjunath M Roopa Department of Electronics and Communication Engineering JSS Science and Technology University Mysuru INDIA Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru INDIA Department of Computer Science and Engineering(AI & ML) RNS Institute of Technology Bengaluru INDIA Department of Computer Applications JSS Science and Technology University Mysuru INDIA

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

In the field of medical diagnosis, the increasing occurrence of brain tumors calls for creative approaches to detect and intervene at early stages. This proposed work focus on the Efficient Net (EffNet) architecture to address the challenges associated with brain tumour recognition and sorting from Magnetic Resonance Imaging (MRI) scans. The main objectives are outlined to develop a robust model capable of discriminating between various tumour types and normal brain tissues. Four distinct classes—glioma tumour, pituitary tumour, meningioma tumour, and no tumour compose the dataset, providing diversity for training and evaluation. Using projected method got the accuracy of 98.08% accuracy. Our proposed method detects the all the four classes effectively.

关键词： Training Accuracy Image recognition Magnetic resonance imaging Brain modeling Medical diagnosis Information technology Medical diagnostic imaging Tumors Sorting

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Enhancing Cybersecurity Through Combined Convolutional Neural Network-Gated Recurrent Unit Approach for Distributed Denial of Service Attack Detection

Enhancing Cybersecurity Through Combined Convolutional Neura...

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Innovative engineering sciences and Technological Research (ICIESTR), International Conference on

作者： Shilpa Choudhary Chris Harry Kandikattu Sandeep Kumar MVV Prasad Kantipudi Munish Kumar Department of Computer Science and Engineering(AI & ML) Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Educational Foundations Vijayawada India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

In cyber-security, integrating GRU with CNN has proven to be a solid and effective method for detecting Distributed Denial of Service (DDoS) attacks. This work presents a robust approach that combines the temporal sequence modelling capabilities of GRUs with the spatial feature extraction capabilities of CNNs. Integrating these two deep learning architectures provides a comprehensive solution, allowing precise detection and reduction of harmful activity inside network traffic. This work enhances DDoS detection methods and emphasizes the increasing importance of using advanced deep learning techniques to strengthen cyber-security measures. The suggested methodology effectively tackles the dynamic nature of DDoS attacks by leveraging the strengths of CNNs and GRUs. This approach offers a diverse and efficient way to protect network integrity. Integrating CNN and GRU in DDoS detection is crucial in strengthening network security against emerging attacks in the ever-changing digital ecosystem. This study highlights the flexibility and effectiveness of incorporating sophisticated neural network structures to improve the robustness of cyber-security systems in response to changing threats.

关键词： Neural networks Ecosystems Telecommunication traffic Network security Denial-of-service attack Solids Feature extraction Robustness Convolutional neural networks computer crime

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Prediction of Residual Energy in Batteries using CNN-BiGRU and Attention Mechanism Model

Prediction of Residual Energy in Batteries using CNN-BiGRU a...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： R. Ganesh Mali Ram Yadav Amit Gupta Kaushalya Thopate Mohammad Ishrat Mohit Lohani Department of Computer Science and Engineering (AI&ML) Institute of Aeronautical Engineering Hyderabad Telangana India Department of Physical Education Lovely Professional University Phagwara Punjab India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Engineering Vishwakarma Institute of Technology Pune Maharashtra India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andra Pradesh India BITS Pilani Rajasthan India

Maintaining a continuous energy supply and making the most of available energy necessitates accurate forecast of battery residual life (RL). The stability (SoC) of a battery management system is essential for leveraging reliable predictions of the battery’s charge state. Since battery deterioration is not linear, it is difficult to make accurate predictions about the So$\mathrm{C}'$s expected lifespan that are substantially longer than current estimates. The proposed method uses an F-divergence algorithm-based data quality evaluation methodology to investigate data unequal distribution, which improves the accuracy and consistency of the batteries. To avoid over-fitting and make more precise predictions, Principle Component Analysis (PCA) is often used to choose which features should be included in a model. CNN-BiGRU-AM based model is employed for training in the proposed method. When compared to two other models, such as CNN and GRU, the proposed method performs better.

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Deep Reinforcement Learning for Dynamic Compliance Management in Blockchain Supply Chains: An LSTM Perspective

Deep Reinforcement Learning for Dynamic Compliance Managemen...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Jinugu Ranjith Mohammed Al-Farouni R. Anitha Ediga Poornima Kamal Sharma Neelam Sanjeev Kumar Department of CSE CMR College of Engineering & Technology Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of IT New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of AI&ML GRIET Bachupally Hyderabad Telangana India Department of Mechanical Engineering GLA University Mathura Department of Computer Science & Engineering (E.Tech.) SRM Institute of Science & Technology Vadapalani Chennai

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Supply chain management is one of the industries that benefit greatly from implementing blockchain technology since it supplies chains with a high level of transparency, security, and traceability. Nonetheless, keeping up to date with different regulatory requirements or specifications that change frequently and still meeting contract requirements still pose a major problem. In this paper, we introduce a new method in the dynamic compliance management of blockchain implemented supply chain system by the use of deep reinforcement learning (DRL) and Long Short-Term Memory (LSTM) networks. Through DRL, supply chains can obtain real-time insights in learning from the environment and apply the knowledge effectively to satisfy regulatory standards as well as contractual terms. The inclusion of the LSTM component allows the system to capture sequential dependency and long-horizon effects within the supply-chain data. To prove the efficiency of our strategy, we provide simulation results as well as the analysis of real-world applications that discuss the positive shift in percentages and potential increase in productivity.

关键词： Industries Supply chain management Simulation Supply chains Deep reinforcement learning Aerodynamics Blockchains Security Long short term memory Standards

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Intrusion Prediction Using Machine Learning Techniques

Intrusion Prediction Using Machine Learning Techniques

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： D. Divya Priya Sabitha Valaboju D Paulraj Ajmeera Kiran Nagaram Ramesh Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of Computer Science and Engineering (AI & ML) CVR College of Engineering Hyderabad Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu India Department of Information Technology BVRIT Narsapur

In this day and age, substantial advances in the realm of security are being achieved at a quick pace. Because of the unexpected increase in the number of internet users, there is an increase in issues connected to hardware failure, software deployment failure, and memory allocation, which directly or indirectly results in data loss. [1] Network invasions entail the theft of important network resources, usually always through exploiting flaws in the group of networks and/or their data. Several hardware, software, and network elements can all contribute to network intrusion. Ineffective communication protocols, certain devices following their own protocol, proper file size constraints not defined, and poor firing are all software factors. Outdated routers, too many devices connected to a single router, and incompatible hardware are all examples of hardware factors. Network hacking, bad network architecture, and oversubscription are examples of unrelated factors. Our study intends to analyse the dataset, which includes all of the aforementioned parameters, using Ad boost classification, Stacking classification, Random Forest classifier, and Gaussian Nave Bayes Classifier.

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Cloud-Native Hybrid Deep Learning Pipeline for Network Traffic Classification using Convolutional Recurrent Neural Network

Cloud-Native Hybrid Deep Learning Pipeline for Network Traff...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： E. G. Satish Komuravelly Sudheer Kumar Myasar M. Adnan Krithika M MandapatiVenkata Rama Sundari Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science & Artificial Intelligence SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of BCA/IT Vels Institute of Science Technology and Advanced Studies Pallavaram India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Identifying encrypted traffic from emerging applications is important and challenging as traditional traffic classification approaches fail to achieve the desired level of accuracy. This necessitates the elaborate strategies to learn about the encrypted network flow. The research focuses on developing a cloud native hybrid Deep Learning (DL) pipeline for Network Traffic Classification (NTC). Initially, the ISCXVPN2016 dataset is considered that consists encrypted and non-encrypted traffic where the raw data is pre-processed with packet length normalization. The features are extracted applying Convolutional Neural Networks (CNNs) to define statistical flow features including inter-arrival time, packet size and flow duration. The data from network traffic is processed and classified using Convolutional Recurrent Neural Network (CRNN) with microservices executed in serverless containers. The hyper parameters in the DL models are then tuned using grid search and the random search algorithms. Experimental results showed that the proposed cloud native CRNN pipeline achieved accuracy of 98.67%, precision of 95.46%, and 94.32% when compared to the existing NTC models Stacked Autoencoders (SAE) based CNN (SAE-CNN) and Autoencoders based CNN (AE-CNN).

关键词： Deep learning Recurrent neural networks Accuracy Pipelines Microservice architectures Telecommunication traffic Containers Feature extraction Cryptography Convolutional neural networks

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Hybrid PCA-Based Machine Learning Models for Predictive Analytics in Urban Health Monitoring Systems

Hybrid PCA-Based Machine Learning Models for Predictive Anal...

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Smart and Sustainable Developments in Electrical engineering (SSDEE), IEEE International Conference on

作者： Shreyas Rajendra Hole Girish S Bhavekar Arvind Kumar Prajapati Vinothkumar Kolluru Suraj Rajesh Karpe Jayavrinda Vrindavanam Computer Science & Engineering (AI&ML) Dayananda Sagar University Bangalore India Department of DIC G H Raisoni College of Engineering Nagpur MH India Electrical Engineering Department Motilal Nehru National Institute Technology Allahabad Prayagraj U.P. India Department of data science Stevens Institute of technology Hoboken NJ U.S.A. Electrical Engineering CSMSS Chh Shahu College of Engineering MH India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

In this paper, a hybrid PCA-based approach is proposed for predicting the respiratory imbalance in an urban health monitoring system. To do this, three models namely Random Forest, XGBoost, and Multi-Layer Perceptron (mlP) were integrated into one system to improve the probability of prediction while maintaining the trade-off with the dimension reduction - a common variable in big data tasks (which, this effort is focused on). Several variables regarding the environment and health of the patients were used to form a dataset that was later transformed with the help of PCA meaning the results would get better because models with better dimensionality tend to perform better. The Hybrid PCA + mlP model produced the best accuracy which 99% and AUC - ROC which 0.90, there were also 99% produced by XGBoost but AUC - ROC was 0.83. Random forest produced “slightly” less accurate predictions of 95% with an AUC - ROC of 0.72 however; this proved the best method in terms of the computational burden since the training time recorded was about 0.55 seconds, which was lower than that of XGBoost 0.45 seconds and mlP 0.94 seconds. These findings show that hybrid PCA based models do not only improve the accuracy of predictions made but also reduce the amount of computational resources needed, which is ideal for tasks that occur in real time whereby smart urban health monitoring is a part of a bigger CPS system.

关键词： Training Analytical models Accuracy Computational modeling Predictive models Real-time systems Computational efficiency Monitoring Random forests Principal component analysis

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Application of computer Software in Chemical Data Processing using Spider Monkey Optimization Algorithm

Application of Computer Software in Chemical Data Processing...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： R Rana Veer Samara Sihman Bharattej Rana Veer Samara Sihman Brahmatej Rupavath Abbas Hameed Abdul Hussein Madugula Anjaneyulu B.P Upendra Roy Department of Computer Science Saint Leo University St Leo USA Indiana Wesleyan University Marion USA College of Pharmacy Ahl Al Bayt University Karbala Iraq Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India Department of Electronics and Communication Engineering Channabasaveshwara Institute of Technology Gubbi Tumkur India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

The development of chemical-based products and functional materials is crucial for modern technologies, yet it often entails significant costs and time delays. Artificial intelligence and machine learning present novel approaches to leverage data for addressing these challenges; however, they encounter specific hurdles in the chemical domain. To address these issues, a software platform has been introduced to facilitate training and development of Recurrent Neural Networks (RNN). This platform incorporates the Spider Monkey Optimization (SMO) algorithm to optimize model’s hyperparameters, including neurons number, rate of learning, and activation function. This software package goes beyond neural network training and development by encompassing design and operating-parameter optimization, essential aspects of chemical engineering processes. Within this framework, the weights that were optimized and bias obtained from neural network were stored and utilized in other SMO process for optimizing important process variables, like swiftness and temperature, according to user specifications.

关键词： Training Recurrent neural networks Software packages Software algorithms Neurons Artificial neural networks User interfaces Optimization Biological neural networks Chemicals

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Smart Mood Detection: Real-Time Emotion Analysis for Human-computer Interaction Using Deep Learning

Smart Mood Detection: Real-Time Emotion Analysis for Human-C...

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2025 International Conference on Artificial Intelligence and Data engineering, aiDE 2025

作者： Chandrappa, S. Banerjee, Tapabrata Saha, Sohan Zaid, Mohd Aditya Pai, H. Guru Prasad, M.S. Rajesh, I.S. Dept. of Computer Science and Engineering Bengaluru India Bengaluru India MIT Art Design and Technology University Department of CSE Pune India Department of Computer Science and Engineering Dehradun India BMS Institute of Technology and Management Dept. of AI & ML Bengaluru India

ISBN: (纸本)9798331527518

A convolutional neural network (CNN)-based face emotion recognition system that analyzes human emotions in real-time, including happy, sorrow, rage, and surprise, is described. Existing approaches often struggle with accurately recognizing subtle or complex emotions due to limited feature extraction capabilities and the need for large, diverse training datasets. Additionally, they face challenges in adapting to real-world conditions, such as variations in lighting, angles, and occlusions. In order to improve the model's generalizability, the proposed research used a preprocessing pipeline that included gray scale conversion, scaling, normalization, and augmentation techniques including rotation and flipping on a labeled and balanced dataset of facial photos. Dropout and regularization techniques were used to reduce over fitting, and the CNN architecture was meticulously adjusted to maximize speed. High accuracy and resilience across emotion categories are demonstrated by preliminary results, indicating the model's potential for real-world uses in security surveillance, healthcare, and human-computer interaction. © 2025 IEEE.

关键词： Convolutional neural networks

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Musical Instrument Recognition Using Machine Learning Approaches

Musical Instrument Recognition Using Machine Learning Approa...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Chethan B K Bharath K N Kiran Puttegowda Manjula A V Sunil Kumar D S Anitha K Yathiraj G R Department of Computer Science Engineering (AI&ML) Vidyavardhaka College of Engineering Mysuru India Department of ECE Dayananda Sagar Academy of Technology and Management Bengaluru India Department of ECE Vidyavardhaka College of Engineering Mysuru India Department of ECE The National Institute of Engineering Mysuru India Department of Computer Applications East Point College of Higher Education Bengaluru India Department of ECE GSSS Institute of Engineering Technology for Women Mysuru India Department of CSE Coorg Institute of Technology Ponnampet India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

This work focuses on musical instrument recognition in miscellaneous audio sources, a fundamental issue of the machine auditory system. We contrast traditional machine learning techniques against deep learning strategies in a clinical dataset solved under a supervised multi-label, multi-output classification paradigm. To enhance the performance of the system, we adjust the analysis parameters for window size, hop size, and label threshold levels. Furthermore, we explore the benefits of auxiliary feature sets as alternative data representations, specifically: Auditory features are based on two categories: (1) four-band spectral decomposition of stereo sound data, left, right, mid, and side channels and (2) the harmonic and residual model of the audio signal. Two methods for fusing the models trained on these representations are suggested and we use the best of these methods to increase recognition accuracy. These enhancements lead to a vast improvement; with a 14.25 % increase M-F score for the macro average and a 24.17 % improvement in the exact match ratio overall compared to the baseline.

关键词： Deep learning Integrated circuits Communication systems Instruments Data visualization Auditory system Harmonic analysis Acoustics Data models Background noise

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