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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： J. R. V. Jeny K. Nikhil Goud Divya Sai Leela Amrutha Kishore Azmira Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

The heart is a very important organ in the circulatory system. Heart disease is extremely severe and can even lead to death, therefore it is essential to make an accurate and timely diagnosis. However, health data analysis may be complicated because there may not be any evident patterns. Cardiovascular diseases (CVDs) are the number one cause of death throughout the world. Early detection of them is vital for successful management. For this purpose, electrocardiograms (ECGs) are used which indicate electrical activities in our hearts; however, computers and machine learning systems are ways by which we could analyze patients’ data more swiftly. It concentrates on using deep as well as machine learning techniques in assisting diagnosing diseases. The purpose was to evaluate how well deep learning could identify four significant heart conditions: arrhythmia, myocardial infarction or heart attacks, history of myocardial infractions (past heart attacks), and normal patterns. We utilized a real-world ECG dataset reflecting actual scenarios and complexities. The Proposed model performed even better than the existing ones with an accuracy of 98.23% implying that it could correctly identify heart issues in ECG images 98.23% times out of 100%. Also recall, precision and F1 score were very high which are other measures about how good a model is performing. By using Machine Learning & Deep learning Algorithms like Support Vector Machine, Efficient Net, Residual Neural Network, and *** Algorithms created their own deep learning model specifically for predicting heart problems.

关键词： Heart Deep learning Support vector machines Machine learning algorithms Accuracy Computational modeling Cardiac arrest Electrocardiography Myocardium Prediction algorithms

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Re-Thinking of a Transformer on Real Time Text Information

Re-Thinking of a Transformer on Real Time Text Information

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： M. Mahesh Bellamkonda Akshara Bandela Shiva V. Pranav Sujith Reddy Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The field of text prediction systems faces a dual challenge of improving accuracy, enhancing user experience, Learning from large and complex data. Existing Natural Language Processing (NLP) methods often fall short in delivering precise prediction. To address these challenges, we propose an innovative approach integrating Deep Learning techniques, notably a sequential model, with the Django Framework and user interface technologies. The proposed system aims to enhance text prediction accuracy by training information from huge corpus, thus generating domain-specific predictions. Central to our approach is the utilization of backpropagation in Deep Learning, enabling precise fine-tuning of the predictive model. By providing a web-based interface, users can seamlessly interact with the system, while backend Deep Learning operations ensure efficient and accurate results. The proposed system aims to leverage the capabilities of the sequential model in Deep Learning. By extracting knowledge from thetrained information from corpus, the sequential model predicts and generates the next n user-specified words. The proposed approach improves the text prediction accuracy and enhances user experience by ultimately advancing applications in various domains.

关键词： Deep learning Training Accuracy Predictive models User interfaces Transformers User experience Natural language processing Real-time systems Faces

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Retinares: An Integrated Deep Learning Framework for Diabetic Retinopathy Classification and Real-Time Severity Prediction

Retinares: An Integrated Deep Learning Framework for Diabeti...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： R. Praveen Kumar A. Vivek Vardhan G. Sai Sanjay P. Susmitha Ramana S. Sai Pavan Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Diabetic Retinopathy (DR) is a leading cause of blindness among diabetics, necessitating accurate and timely diagnosis. Current object detection-based methods often misclassify healthy retinal images, hindering effective patient care. This research proposes a ResNet-based classification framework to address this challenge. By focusing on precise classification rather than object detection, our approach significantly reduces false positives. Leveraging a Kaggle dataset, we demonstrate the model's superior performance in assessing DR severity compared to existing methods. A user-friendly Flask-based interface enables easy image upload and classification results. Our findings, evaluated using accuracy, precision, recall, and Fl-score, highlight the potential of our model to improve DR diagnosis and patient outcomes.

关键词： Deep learning Diabetic retinopathy Electric potential Accuracy Communication systems Focusing Object detection Blindness Retina Real-time systems

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Energy Efficient Green Routing for UAVs Ad-Hoc Network

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Intelligent Automation & Soft Computing 2023年第7期37卷 1111-1127页

作者： M.Muthukumar Rajasekar Rangasamy Irshad Hussain Salman A.AlQahtani Department of Computer Science and Engineering Sree Sowdambika College of EngineeringTamil NaduIndia Department of Computer Science and Engineering-AI&ML School of EngineeringMalla Reddy UniversityHyderabadIndia Faculty of Electrical and Computer Engineering University of Engineering and Technology Peshawar25000PeshawarPakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia

The purpose of this article is to propose Stability-based Energy-Efficient Link-State Hybrid Routing(S-ELHR),a low latency routing proto-col that aims to provide a stable mechanism for routing in unmanned aerial vehicles(UAV).The S-ELHR protocol selects a number of network nodes to create a Connected Dominating Set(CDS)using a parameter known as the Stability Metric(SM).The SM considers the node’s energy usage,connectivity time,and node’s *** the highest SM nodes are chosen to form *** node declares a Willingness to indicate that it is prepared to serve as a relay for its neighbors,by employing its own energy state.S-ELHR is a hybrid protocol that stores only partial topological information and routing tables on CDS *** of relying on the routing information at each intermediary node,it uses source routing,in which a route is generated on-demand,and data packets contain the addresses of the nodes the packet will transit.A route recovery technique is additionally utilized,which first locates a new route to the destination before forwarding packets along *** simulation for various network sizes and mobility speeds,the efficiency of S-ELHR is *** findings demonstrate that S-ELHR performs better than Optimized Link State Routing(OLSR)and Energy Enhanced OLSR(EE-OLSR)in terms of packet delivery ratio,end-to-end delay,and energy consumption.

关键词： Connected dominatingset hybridrouting UAVAdhocnetworks link state stability

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Water Quality Prediction for Smart Aquaculture

Water Quality Prediction for Smart Aquaculture

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： R.Praveen Kumar T. Prabhu J. Sowmya A.Nithin Kumar Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This work presents a unique hybrid approach combining different techniques for precise water quality prediction in smart aquaculture systems. The proposed model integrates the different algorithms with complex pattern recognition, resulting in a robust and efficient predictive system. Incorporating feature engineering and transfer learning enhances the model's adaptability to diverse aquaculture settings, ensuring accurate predictions of critical water parameters. Moreover, our methodology utilizes real-time sensor data, enabling proactive management of water quality conditions. Through comprehensive validation, our hybrid model outperforms existing methods, offering a groundbreaking solution for optimizing smart aquaculture operations. This research contributes to the advancement of predictive technologies, providing a novel and effective tool for sustainable aquaculture practices.

关键词： Adaptation models Accuracy Communication systems Transfer learning Water quality Predictive models Prediction algorithms Real-time systems Pattern recognition Aquaculture

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Advancements in Handwritten Text Recognition and Correction: Bridging the Gap Between Analog and Digital Writing 15th

Advancements in Handwritten Text Recognition and Correction:...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Mohan, J. S. Shyam Sathwik, Abbaraju Sai Ganesh, M.N.V.S. Tejaswi, M. Hema Naseeba, Beebi Madhavi, K. Reddy Department of Computer Science and Engineering GITAM University Bangalore India VIT-AP University Amaravati India Department of AI&ML School of Computing Mohan Babu University Tirupati India

ISBN: (纸本)9783031889912

Handwritten text recognition and correction is the process of converting handwritten text into digital form and correcting errors within the recognized text. Natural language processing tools, computer vision methods, and machine learning algorithms are combined to achieve this. The recognition process involves extracting features, and classifying characters or words. The correction process uses a language model to identify errors and suggest corrections based on context and grammar rules. Handwritten text recognition and correction has numerous applications in fields such as document digitization, signature verification, and historical document analysis. However, it remains a challenging task due to the variability of handwriting styles and the complexity of human language. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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Comparative Analysis of Sorting Algorithms: Time Complexity and Practical Applications

Comparative Analysis of Sorting Algorithms: Time Complexity ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Manish Rai Hemlata Parmar Suhani Sharma Department of Computer Science and Engineering(AI&ML) Manipal University Jaipur Jaipur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In the field of computer science, sorting algorithms are crucial because they facilitate the effective processing and arrangement of data in a variety of scenarios, including data analysis, searching, and optimal system operation. The objective of this study is to look at and compare different sorting algorithms to see how well they work and how useful they are in different situations. The study tests popular sorting algorithms like Quick Sort, Merge Sort, and Bubble Sort on different input sizes, types of data (sorted, reversed, and random), and how they are implemented in the C++ programming language. The study shows how they work in terms of time complexity, resource use, and real applications through a systematic analysis. The most important results show that algorithms work differently depending on the inputs. For example, Quick Sort does better in most situations, while Merge Sort stays stable in the worst ones. This work also finds situations where simpler algorithms, like Bubble Sort, are good for small data sets. This paper is unique because it gives real-life cases, suggestions for choosing the best algorithm based on specific needs, and suggestions for how to do that. The goal of these results is to help programmers, teachers, and experts pick the right sorting algorithms for their computer tasks.

关键词： Machine learning algorithms Data analysis Systematics Systems operation Instruction sets Machine learning Internet of Things Time complexity Optimization Sorting

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Understanding the Psychological Needs at Play in Disinformation 1

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Khattar, Kaveesh Das, Bhaskarjyoti Department of Computer Science and Engineering PES University Karnataka Bengaluru India Department of Computer Science and Engineering in AI & ML PES University Karnataka Bengaluru India

ISBN: (数字)9789819736904

ISBN: (纸本)9789819736898

The work described in this paper explores the intersection of computer science, linguistics, and psychology in the realm of disinformation on social media. By analyzing textual content on Twitter, specifically propaganda, fake news, and rumors, we investigate the underlying motivations and patterns behind the production of disinformation. We apply the Human Needs Theory (HNT) to understand the psychological processes driving the emotions expressed in disinformation tweets. Our findings highlight distinct patterns across different types of disinformation, shedding light on the complex relationship between linguistic behavior and psychological factors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Social psychology

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An Ensemble Approach to Identify Intrusions in Edge IoT and IIoT Networks

An Ensemble Approach to Identify Intrusions in Edge IoT and ...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Vishnu Kurnala Sujith Reddy Kalam Department of Computer Science and Engineering (AI&ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

This work presents an IDS for Internet of Things (IoT) that makes use of the ml-EdgeIIoT-dataset to safeguard critical IoT infrastructures. The proposed model integrates CNN and GRU with an XGBoost layer to enhance predictive accuracy. The CNN-GRU architecture is specifically engineered to effectively capture both spatial and temporal features. The CNN component in this design is tasked with capturing local patterns, whereas the GRU component is responsible for managing sequential dependencies. The results are combined with the initial characteristics and improved using XGBoost to generate the ultimate forecasts. The ensemble approach ensures robust real-time data processing, enabling fast and accurate risk identification. The effectiveness of the Intrusion Detection System (IDS) is evaluated using the four accuracy metrics, demonstrating its outstanding efficiency and resilience across various workloads. This comprehensive architecture prioritizes the security and dependability of the IoT system and is adaptable to evolving cyber threats. Our Intrusion Detection System (IDS) establishes a higher benchmark for taking initiative and adapting to changing circumstances in order to defend IoT networks. It offers robust safeguards to ensure the stability and security of these networks. The study emphasizes the significance of employing sophisticated machine learning methods to enhance the detection capabilities and overall robustness of IDS in Internet of Things (IoT) environments.

关键词： Measurement Accuracy Intrusion detection Predictive models Stability analysis Robustness Real-time systems Security Ensemble learning Resilience

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Intelligent Application for Dog Emotion Detection using EfficientNet-B0

Intelligent Application for Dog Emotion Detection using Effi...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Pradnya Narkhede Anuradha Thakare Sakshi Shitole Ayush Dongardive Vrushali Guldagad Department of Computer science and Engineering(AI&ML) Pimpri Chinchwad College of Engineering Pune

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

The present research paper introduces an innovative methodology for classifying canine emotions by employing the EfficientNet-B0 algorithm. The recognition of emotions by animals, specifically canines, is a crucial factor in comprehending their conduct and improving the quality of human-animal relationships. This research study addresses the task of precise dog emotion recognition, like happy, sad, angry, relaxed. In this study, the model, fine-tuned, is used to cover different emotions in dogs and has a classification accuracy of 89%, thus showing its potential to recognize emotions like anger, happiness, relaxation and sadness among dogs. The Efficient net- B0 model is meticulously trained on an augmented dataset known as Stanford’s Dog Emotion dataset, which consists of images of dogs with labels indicating their emotional state: angry, happy, relaxed, or sad. Metrics that show constant performance, such as accuracy, loss, AUC, and precision when viewed visually, show consistent performance. According to the data, EfficientNet-B0 is good at correctly detecting dogs' emotions, which could be useful in veterinary care, animal behavior research, and studies of how people interact with animals.

关键词： Training Measurement Deep learning Emotion recognition Accuracy Computational modeling Dogs Medical services Feature extraction Planning

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