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

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Improving Data Encoding for Enhanced ai Performance in Complex Datasets via Quantum Feature Space Mapping: Harnessing Quantum Algorithms

Improving Data Encoding for Enhanced AI Performance in Compl...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Gurpreet Singh Walia Sathiya Priya S KV Karthikeya D. Suresh P Sudheer V. Syambabu Lead Enterprise Security and Networking Architect NetApp Inc. Durham NC Department of ECE Panimalar Engineering College Chennai AT&T Sattva Knowledge City Hyderabad Department of ECE St. Joseph's Institute of Technology Chennai Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The purpose of this study is to investigate the potential for quantum feature space mapping to be utilized in the enhancement of data encoding approaches for artificial intelligence (ai) systems that are capable of managing complicated datasets. Traditional artificial intelligence models frequently face challenges when confronted with the high-dimensionality and non-linearity of such datasets. This can hinder their performance in areas such as pattern recognition, optimization, and predictive modeling. Quantum computing, which is able to make use of quantum superposition and entanglement, provides a method that is revolutionary in nature by means of quantum feature space mapping. By encoding data into quantum states, this technology enables the construction of feature spaces that are tenfold bigger than those that would be possible in classical conditions. This makes it much simpler to differentiate between intricate patterns that would otherwise be indistinguishable. The purpose of this research is to investigate the application of quantum algorithms, such as Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC), with the intention of enhancing the capacity of artificial intelligence to learn from complex and high-dimensional data. We demonstrate that quantum feature space mapping leads to more efficient and accurate data classification, regression, and clustering tasks by comparing the performance of quantum-enhanced artificial intelligence models to that of their classical counterparts on difficult datasets. Additionally, the research addresses important difficulties, such as the constraints of the quantum hardware that is now available and the requirement for quantum circuits that are scalable. Quantum computing has the potential to improve the performance of artificial intelligence in the management of complicated, high-dimensional data, which may ultimately pave the way for advances in domains such as genetics, finance, and cryptogra

关键词： Support vector machines Quantum computing Quantum algorithm Quantum entanglement Computational modeling Space mapping Quantum state Encoding Hardware Quantum circuit

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Block Chain Technology to Secure Data Management in Cloud Computing

Block Chain Technology to Secure Data Management in Cloud Co...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： R. Gowtham Raj P. Aswini Ali Abdulhussein Hameed H. Pal Thethi Gudapalli Karuna Deepika Arora Department of Mechanical Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq LPU Phagwara India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad Telangana India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Decentralized blockchain technology has gained a lot of interest as a potentially groundbreaking financial innovation for the digital age. Specifically, it is secure because it generates hash values, encrypts data, and verifies peers who exchange virtual currency. The international banking sector projects a maximum market for blockchain technology based on security. Furthermore, blockchain has potential uses outside of the IoT ecosystem, and these uses are likely to grow. The accessibility and effectiveness of cloud computing have led to its widespread adoption across all IT settings. Effective deployment of blockchain technology into cloud computing requires resolving several issues and concerns, such as scalability, interoperability, and regulatory compliance, despite the security advantages provided by this integration. Furthermore, the dynamic nature of technology means that the latest advancements can significantly influence its application in cloud data management security. The study explores the idea of the technology of blockchain and the current research trends around it. They will investigate safe applications of blockchain technology on the cloud, as well as modifications to it.

关键词： Cloud computing Technological innovation Reviews Scalability Software Blockchains Peer-to-peer computing Safety Security Servers

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Smart Agriculture: Explainable Deep Learning Approach with Gradient-Weighted Class Activation Mapping

Smart Agriculture: Explainable Deep Learning Approach with G...

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Mohammad Khaja Shaik Mudarakola Lakshmi Prasad Y Sowmya Reddy S Asif D Kalpana Pundru Chandra Shaker Reddy Electronics and Communication Engineering St. Ann's College of Engineering and Technology Chirala Andhra Pradesh India Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Telangana India Computer Science and Engineering (AI&ML) CVR College of Engineering Hyderabad India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana India Computer Science and Engineering (Data Science) Malla Reddy Engineering College (Autonomous) Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

In light of the pressing need to guarantee food security in the face of threats posed by climate change, an increasing global population together with rising incomes, worsening soil erosion, and dwindling water resources, crop yield forecasting has assumed more significance in recent years. A relatively new area of study, “Explainable ai” seeks to provide context for the findings produced by Deep learning models. Nevertheless, there is a pressing need for more comprehensive evaluations of deep learning models for object classification in images. Due to this, the research suggests two steps for implementing Explainable ai: (1) assessing the deep-learning(DL)-strategies accuracy utilizing evaluation methods, and (2) evaluating the feature recognition skill of an scan when recognized by DL utilizing Grad-CAM for model construal. Compared to EfficientNetV2S and Xception, the results reveal that MobieNet model has lower dependability but higher accuracy. When taking the ratio match rate into account, though, MobileNetV2 achieves the best accuracy. Building intelligent agriculture support systems (with the help of automated fruit-picking robots that discard bad fruits) is one application of the study's findings. Another is the utilization of the Explainable ai model's output to determine the best deep learning model for processing.

关键词： Climate change Deep learning Extended reality Agriculture Food security Smart agriculture Agricultural robots Resource management Crop yield Weather forecasting

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Graph-Based Machine Learning Approaches for Fraud Detection in Financial Networks 7

Graph-Based Machine Learning Approaches for Fraud Detection ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kotiyal, Arnav Hussein, Layth Deepak, A. Rana, Aryan Manjunatha Dixit, Krishna Kant Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamilnadu Chennai India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gla University Department of Electrical Engineering Mathura India Mlr Institute of Technology Department of CSE-AI & Ml Telangana Hyderabad India

ISBN: (纸本)9798350350067

Fraud detection in financial networks presents a significant challenge due to the complexity and volume of transactions. Traditional detection methods often struggle with scalability and accuracy when faced with evolving fraudulent schemes. In this paper, we explore the application of graph-based machine learning techniques to enhance fraud detection in financial networks. By representing financial transactions as graph structures, we capture the relationships between entities, allowing for the identification of suspicious patterns that may indicate fraudulent behavior. We examine several graph-based algorithms, including Graph Neural Networks (GNNs) and Random Walk-based approaches, and compare their performance with conventional machine learning methods. The use of graph representations facilitates the extraction of features such as transaction flows and network centrality, which are critical in identifying anomalies. Furthermore, we propose an enhanced detection model that integrates community detection techniques with supervised learning for improved accuracy. Our experimental results, conducted on a real-world financial dataset, demonstrate that graph-based models outperform traditional models in detecting fraud with higher precision and recall. The study highlights the potential of these techniques to provide scalable and robust solutions for real-time fraud detection, contributing to a more secure financial ecosystem. © 2024 IEEE.

关键词： Adversarial machine learning

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BERT-based Models for Predicting Protein-Protein Interaction Sites

BERT-based Models for Predicting Protein-Protein Interaction...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Malviya, Bhupendra Alisher, Yigitaliyev Sharma, Mahima Rajalakshmi, G. Hussein, Layth Yaswitha, G. Ies College of Technology Department of Computer Science & Engineering M.P Bhopal India Fergana Medical Institute Of Public Health Department Of Urology And Oncology Doctoral Fergana Uzbekistan Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Saveetha Institute of Medical and Technical Sciences Department of Ai & Ml Tamilnadu Chennai India

ISBN: (纸本)9798350390759

In this work, a BERT-based model is proposed for predicting potential PPI sites with the aid of its remarkable contextual understanding feat. Thus, the model was adjusted to a set of positive PPI sites, containing 268 sites in total with the accuracy of 0. 89, precision of 0. 88, recall of 0. IL is 0. 78, accuracy of 0. 85, precision of 0. 83, and F1-score of 0. tested on face images achieving a performance of 87% which is higher than that of conventional models such as CNNs and SVMs. Only hyperparameter tuning and cross-validation have been used to make sure that the model is not overfitting to the data and it can produce reasonable results on unseen data as well. The results of this study also indicate that BERT-based models can be effectively utilized in the bioinformatics field, opening up myriad possibilities for future utilization of computational approaches in the prediction of PPIs. © 2024 IEEE.

关键词： Prediction models

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air Quality Control using Machine Learning

Air Quality Control using Machine Learning

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： K. RamaKrishna Reddy D. Vidya Shree N. Vasu N. Muthukumaran K. Saketh Patel N. Vamshi Karthik Rao Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

In recent times, the world has witnessed remarkable advancements driven by various technologies such as advanced computing, self-driving vehicles, and robot-assisted surgeries, among others. However, these technological strides have come with a significant environmental cost, contributing to the pollution of vital natural resources such as air and water. The contamination of air and water worsens health issues, particularly for individuals with respiratory ailments such as asthma and lung infections, posing risks to the overall population’s well-being. This research study has carried out a qualitative research and presents the challenges that demand careful navigation and interpretation in the near future.

关键词： Support vector machines Atmospheric modeling Data visualization Surgery Robot sensing systems Water pollution Decision trees

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Deep Learning Algorithms in DNA Sequencing: Transforming Genetic Testing and Analysis

Deep Learning Algorithms in DNA Sequencing: Transforming Gen...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： V. Ramu Nagendar Yamsani M. Praveen Kumar D. Ragunath T. Venkata Deepthi S Arulraj Department of CSE(AI&ML) Kakatiya Institute of Technology and Science Hanamkonda Telangana India Department of School of Computer Science and Artificial Engineering SR University Warangal India Department of Computer science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science and Engineering Bannari Amman Institute of Technology Athani Main road Sathyamangalam Erode Tamil Nadu India Department of Mechanical Engineering Malla Reddy Engineering College Main Campus Maisammaguda Hyderabad Telangana India Department of ECE Rathinam Technical Campus Coimbatore Tamilnadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Deep learning algorithms have revolutionized DNA sequencing, providing unprecedented accuracy and efficiency in genetic testing and analysis. Traditional methods struggle with the vast complexity and volume of genomic data, leading to high costs and extended processing times. To address these challenges, we developed a novel deep learning framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The CNN component captures spatial features from DNA sequences, while the LSTM component handles sequential dependencies, ensuring comprehensive analysis. Our model was trained on a dataset of 10 million sequences and validated using crossvalidation techniques. We incorporated model quantization and pruning to optimize performance and reduce computational costs. The proposed framework achieved up to 98.7% accuracy in identifying genetic mutations, outperforming traditional methods by 12%, and reduced processing times by 50%, with an average rate of $\mathbf{2, 0 0 0}$ sequences per second. Comparative analysis showed a 15% improvement in precision and a 20% reduction in false positives compared to existing models. This study presents a transformative approach to DNA sequencing, leveraging advanced deep learning algorithms to significantly enhance genetic testing and analysis, providing faster, more accurate, and cost-effective solutions for handling genomic data complexities. Future work will focus on further refining the model and exploring its application in personalized medicine and real-time genetic analysis, underscoring the potential of deep learning to revolutionize genetic research.

关键词： Deep learning Sequential analysis Accuracy Computational modeling DNA Genomics Real-time systems Convolutional neural networks Bioinformatics Long short term memory

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Sine-Cosine Zebra Optimization Algorithm with Mixture of Experts based Long Short-Term Memory for the Detection and Classification of Cyber Treats

Sine-Cosine Zebra Optimization Algorithm with Mixture of Exp...

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

作者： N. Tejasri R. Archana Reddy Mohammed Hussein Fallah Savita Muchakhandi Gudapalli Karuna Malla Reddy Engineering College for Women (UGC-Autonomous) Hyderabad India Department of Computer Science and Engineering SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical & Electronics Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The use of internet application is rising rapidly where the number of users is increasing day by day creates traffic in the network which led path for the growth of cybercrimes. To avoid the cyber treats various advance technologies have been introduced for the detection of unauthorised users in the network. The existing methods tried to improve the accuracy of cyber threat section model using various algorithms but did not reach the target due to the issues such as imbalanced data, variable constraints and overfitting. The paper proposed Sine-Cosine Zebra Optimization Algorithm with Mixture of Experts-Long Short-Term Memory (SCZOA-MoELSTM) for the detection and classification of cyber treats. The min-max normalization rescaled the ranges of the data. The Sine-Cosine Zebra Optimization Algorithm (SCZOA) was used for the selection of optimal features. Mixture of Experts-Long Short-Term Memory (MoELSTM) captured the long-range values of the features that enhanced the performance of cyber threat detection and classification. The proposed SCZOA-MoELSTM model obtained accuracy of 99.04% and 96.85%, precision of 98.22% and 95.73%, Detection Rate (Recall) of 98.98% and 95.67%, F1-Score of 98.59% and 95.69% respectively for CICIDS2017 and UNSW-NB15 datasets compared to the existing Long Short-Term Memory (LSTM).

关键词： Training Measurement Accuracy Feature extraction Threat assessment Classification algorithms Pattern recognition Optimization Long short term memory Overfitting

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ai-Powered Object Detection for Autonomous Vehicles: A Comparative Study of Machine Learning Models

AI-Powered Object Detection for Autonomous Vehicles: A Compa...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Anurag Shrivastava Vipashi Kansal Amandeep Nagpal Manjunatha Krishna Kant Dixit K. Varada Rajkumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Lovely Professional University Phagwara India Department of Mechanical Engineering New Horizon College of Engineering Bangalore India Department of Electrical Engineering GLA University Mathura India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Object detection is an integral piece of autonomous cars learning their surroundings and making safe judgements about where and how to drive in real time. This research compares and contrasts multiple machine learning models for object identification in autonomous cars in order to assess accuracy, computational economy, and real time performance. Then, we present how various well known proposed models (CNN, YOLO, SSD, R-CNN) perform in changing driving conditions. It researches the models capabilities to run real time applications within autonomous cars, as well as the trade off of speed and accuracy between different models. We also study how hyperparameter adjustment, data augmentation and transfer learning impact these models’ performance. Through thorough research on benchmark datasets, this work shows the benefits of the adoption of hybrid models, which mix speed and accuracy, to increase item recognition in real world contexts. This study contributes to the expanding corpus of knowledge on ai driven solutions for autonomous systems by having useful insights regarding the improvement of autonomous vehicle object identification algorithms.

关键词： YOLO Accuracy Biological system modeling Computational modeling Transfer learning Real-time systems Sensors Automobiles Object recognition Autonomous vehicles

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Industrial Image Processing-IoT and Big Data Sensor Value Analysis

Industrial Image Processing-IoT and Big Data Sensor Value An...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Muthuswamy Jayanthi Sajja Suneel Manish Gupta Ali Albawi Banoth Ramesh Dinesh Kumar Yadav Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

In the contemporary period, there is a need for the surveillance of many industrial characteristics such as temperature measurements of machinery, voltage supply to equipment, internal environmental conditions inside the manufacturing, pressure levels, and register monitoring and management, among others. The change in the interpretation of the values may have a significant impact. Monitoring is crucial for factors such as these and for the placement of things. As an illustrative instance, let us contemplate the arrangement of oil or gasoline drums that are vertically stacked inside a confined space. Monitoring these metrics has undergone continuous modifications throughout the last several years. Software technologies such as image processing and machine learning oversee specific industrial characteristics in this context. Furthermore, the use of significant data approaches is employed to analyse the diverse sensor readings to predict future values or gain insights into the behaviour of the particular machine. Furthermore, machine learning methods may be expanded to automate the whole system. The Arduino Mega, a centralized processing unit for the whole system, governs the hardware component. The system exhibits stability and demonstrates efficacy in its monitoring capabilities. This technology has the potential to provide enhanced safety protocols for small-scale enterprises as well.

关键词： Temperature measurement Voltage measurement Surveillance Machine learning Stability analysis Software Safety Registers Petroleum Thermal stability

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