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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Srinivasu, Parvathaneni Naga Sravya, Valluri Sri Tagore, Vusala Deepak Vinoothna, Veedhi Mokshada, Yakkala Prasad V Potluri Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350361780

Bone cancer is an epidemic of the skeletal system that is impacted by a complex interaction of genetic variables and inherited susceptibility. It is critical to develop a readily accessible detection and classification method for early detection of this cancer. As a consequence, a computerized procedure for distinguishing between cancerous and healthy bones is necessary. This paper is dedicated to developing a system that uses multiple magnetic resonance imaging (MRI) scans in digital imaging and communications (DICOM) format of cancerous and healthy bones that exhibit comparable structural traits from different individuals to identify and classify bone cancer using convolutional neural networks (CNN). The proposed approach can be broken down into three parts: MR image improvement, feature encoding, and categorizing. After preprocessing an image, the features are retrieved using discrete wavelet transformation (DWT). Finally, neural networks (NN) differentiate between normal and diseased bone. This method hybridizes one of the most efficient CNN architectures, Visual Geometry Group (VGG) 19. An effective strategy for cancer detection is proposed based on spatial fuzzy C-means (SFCM) clustering. A rigorous performance evaluation showed that the proposed CBCD method is extremely successful in detecting bone cancer, with an amazing accuracy rate of 92.9%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Creating a Protected Virtual Learning Space: A Comprehensive Strategy for Security and User Experience in Online Education 4th

Creating a Protected Virtual Learning Space: A Comprehensive...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Boddapati, Mohan Sai Dinesh Desamsetti, Sri Aravind Adina, Karunasri Uppalapati, Padma Jyothi Murty, P T Satyanarayana P. B. V, RajaRao Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram534202 India

ISBN: (纸本)9783031488870

The pandemic has a significant impact on how people conduct meetings, both in corporations and in schools. Online meetings have become a popular way to connect people from all over the world, lowering the expenses and time associated with travel. Various video conferencing systems and communication tools have aided in this trend towards online meetings. Many countries have moved to online classrooms as an alternative to traditional face-to-face instruction in the educational sector. It has also enabled educational institutions to adapt to changing circumstances and continue to educate students. One of the major concerns is security. As online platforms become more popular, the potential of infiltration activities such as hacking or unauthorized access increases. This study proposes a comprehensive strategy for improving security and user experience in online education. The framework focuses on detecting existing participants, detecting intruders, restricting intruders, and restricting abusive messages. It employs authentication mechanisms, user behaviour analysis, network monitoring, and machine learning algorithms to validate participant identities, differentiate legitimate users from prospective invaders, restrict unauthorized access, and promote courteous conversation. The framework proves its usefulness in minimizing security concerns and promoting a secure online learning environment through simulations and case studies. © 2024, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Intrusion detection

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Enhancing Accuracy with Recursive Feature Selection Using Multiple Machine Learning and Deep Learning Techniques on NSL-KDD Dataset 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Mohanty, Subrat Kumar, Satendra Agarwal, Mayank Department of Mathematics Indian Institute of Technology Patna India Department of Computer Science and Engineering Indian Institute of Technology Patna India

ISBN: (数字)9789819995189

ISBN: (纸本)9789819995172

The world has moved toward digital revolution and more and more services are now being available online. This has presented significant challenges in ensuring the availability, integrity, and confidentiality of networks. Therefore, the implementation of intrusion detection systems (IDS) that actively monitor network activity and dynamically analyze incoming traffic has become essential. In modern computer networks, IDS play a critical role in identifying and alerting administrators of any malicious activities that could threaten data *** research aims to evaluate machine learning (ML) and deep learning (DL) techniques for intrusion detection using the NSL-KDD dataset. While having many features may seem advantageous, it doesn’t always lead to improved performance on large datasets. Reducing and picking just the right set of features may improve both speed and accuracy. This is why a method called recursive feature elimination (RFE) is utilized to select the most relevant features for the NSL-KDD dataset. In order to test the efficacy of ML and DL methods in IDS, we have undertaken a comprehensive experiment. Specifically, we tested the effectiveness of several algorithms, including CNN, RNN, LSTM, GB, CART, and CatBoost. Our study involved evaluating the performance of various algorithms and comparing them, and the detection accuracy [lowest-highest] for all the attacks are as follows: DoS [98.79–99.81%], Probe [96.34–99.64%], R2L [96.15–98.66%], U2R [79.98–99.73%]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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Identifying Marine Algae Habitats: A Sea Water Classification Approach 8

Identifying Marine Algae Habitats: A Sea Water Classificatio...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Rao, Manjula Gururaj Shrisha, S.K. Shreesha Noronha, Shaun Priyanka, H. Shishir NMAM Institute of Technology Nitte Karkala India PESU Computer Science & Engineering Bangaluru India

ISBN: (纸本)9798350350593

Water serves as the primary source for living organisms, but the availability of drinkable water on Earth is limited, mainly found in icebergs and the sea. Determining the pH of water is crucial for various applications, including water treatment to make it drinkable. Sea water is the main sources for many organisms' growth. One of them is the algae. Monitoring parameters like pH, dissolved oxygen, and water circulation is essential for maintaining a stable environment for algae cultivation. Understanding marine water specifications and the role of algae is vital for environmental management and sustainable aquaculture. A hybrid method is proposed, comprising modules for assessing water drinkability and classifying algae habitats. Using machine learning algorithms, the model achieves high accuracy rates of 78% for drinking water and 84 % for algae classification. © 2024 IEEE.

关键词： Algae

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A model for screening eye diseases using optical coherence tomography images

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International Journal of computers and Applications 2022年第4期44卷 396-400页

作者： Gupta, Sanchay Chandra, Siddharth Maheswari, N. Sivagami, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

Age-Related Macular Degeneration (AMD) is one of the most common type of eye ailments which generally affects elderly people of age above 60 years and which damages their central vision. Diabetic Macular Edema (DME) is an another form of irreversible vision loss that occurs in diabetic people because of the accumulation of fat in retinal nerves. Therefore it is necessary to screen AMD and DME at an early stage, as it would help in the treatment and prevention of complete blindness. This paper aims to create a system for screening AMD and DME on Optical Coherence Tomography images using Image processing techniques in order to improve the feature to be extracted from the images using Convolutional Neural Network. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Support vector machines

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Real-Time Fire Detection in Unmanned Ground Vehicles Integrating YoloV5 and AWS IoT

Real-Time Fire Detection in Unmanned Ground Vehicles Integra...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Banerjee, Saptajit Das, Ronodeep Rathinam, Rajesh Dhanalakshmi, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350315127

This paper aimed at the development of a remotely controllable 4-wheeled unmanned ground vehicle (UGV) capable of transmitting realtime sensor data and live video feeds over the internet. The central innovation lies in the utilization of an advanced object detection model, to detect fires within the live video feed. By leveraging a Raspberry Pi 4 with 2GB RAM, AWS IoT Core, MQTT, HTTP, and TCP protocols, the UGV achieves seamless data exchange. AWS IoT Core serves as both MQTT broker and subscriber, with the UGV as publisher for sensor readings and subscriber for control commands. The web app acts as the publisher for control commands, generating signals based on user input. The Remote. it software plays a pivotal role in facilitating a cloud-based HTTP service for live video streaming, thereby significantly enhancing global accessibility. Moreover, diverse remote operation methods including keyboard, onscreen, and speech controls, provide flexible means for user interaction. The integration of YOLOv5, Tensorflow, and OpenCV yields a robust fire detection capability, effectively triggering SMTPbased email alerts. This system effectively showcases the seamless fusion of robotics, IoT, web technologies and machine learning, thereby contributing to safety enhancement and inspiring a wave of future advancements. © 2023 IEEE.

关键词： Web services

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Strength of Ensemble Learning in Voting Classifier for Crop Yield Prediction 15

Strength of Ensemble Learning in Voting Classifier for Crop ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Tripathi, Deeksha Biswas, Saroj K. Das, Akhil Kr. Bhattacharya, Arijit Purkayastha, Biswajit Department of Computer Science and Engineering National Institute of Technology Silchar India Department of Computer Science Gour Mahavidyalaya West Bengal India

ISBN: (纸本)9798350370249

Crop Yield Prediction (CYP) plays a vital role in mitigating emerging food security challenges, particularly when the rapid shifts in global climate patterns occur. Machine Learning (ML) is crucial for CYP because it can estimate the crop production based on different characteristics such as environmental characteristics, soil fertilizers, and crop management. Researchers have used lots of ML techniques with distinct kinds of data analytics techniques. Thus, this study proposes an ensemble model, named Voting-based Ensemble Expert System for Crop Yield Prediction (VEESCYP) to predict the best crop yield for the Assam state of India. This study tries to present the strength of a voting classifier with Ensemble Learning (EL) approaches. The proposed VEESCYP model has performed some data preprocessing tasks before classification, such as the Multiple Imputation by Chained Equation (MICE) to treat the missing values, Isolation Forest (IF) for outlier detection, Genetic Algorithm (GA) for feature selection and Robust Scaling (RS) for data normalization. To show the strength of EL techniques in voting classifier, the proposed VEESCYP model compares with some standard ML algorithms, a Voting-based EL technique using standard ML algorithms and some existing models presented in the literature. © 2024 IEEE.

关键词： Adversarial machine learning

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Addressing Power Grid Stability With Grid-Forming Converters in Future Energy Systems

Addressing Power Grid Stability With Grid-Forming Converters...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Saxena, Kumud Vijay, Sandip Kamalraj, R. Noida Institute of Engineering and Technology Department of Computer Science Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science Bangalore India

ISBN: (纸本)9798350357769

Synchronous generators (SGs) have been a major component of the power system's historical reliance on inertia and grid stability. However, since there is a visible lack of inertia with the growing integration of power-electronics-interfaced clean energy sources, the grid's stability has faced issues during the last ten years. Now, grid-following (GFL) converters are mostly used on the presumption that inertial stimuli are enough to control system stability. However, this assumption turns out to be insufficient for the future low-inertia grids that are projected. Grid-forming (GFM) converters, which emulate the functions of conventional synchronous machinery, have emerged as a possible approach to overcome these issues and enable low-inertia grids. Although the literature provides information on small-signal instability performance study of GFM converters, large-signal instability research is still lacking. Moreover, a multitude of topologies and variations of GFM conversion have been put forward;nevertheless, a thorough comparative analysis that encompasses all GFC variants and specifically addresses large-signal stability concerns is noticeably lacking. By consolidating and contrasting all currently available GFM control techniques from the perspective of large-signal stability concerns, this work aims to close this gap. The goal is to set the stage for further study and development of GFM mode converters designed particularly for large-signal stability analysis and stabilization in the next low-inertia grids. This thorough analysis not only resolves the existing shortcomings that have been highlighted but also lays the groundwork for furthering our knowledge of and use of GFM conversions in the dynamic world of power systems. © 2023 IEEE.

关键词： Grid-Following Converters Grid-Forming Converters Large-Signal Stability Inertia Power Grid and Renewable Energy Small-Signal Stability

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Classification of Skin Lesion Using Image Processing and ResNet50 2nd

Classification of Skin Lesion Using Image Processing and Res...

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2nd International Conference on Big Data, Machine Learning, and Applications, BigDML 2021

作者： Pradhan, Adarsh Saha, Subhojit Das, Abhinay Barman, Santanu Department of Computer Science and Engineering Girijananda Chowdhury Institute of Management and Technology Azara Guwahati India

ISBN: (纸本)9789819934805

One of the most critical concerns of healthcare is cancer and the prevalence of skin cancer in the population has been increasing gradually and ominously. In this paper, we have proposed a deep learning model using a pre-trained neural network architecture called ResNet-50, and classify the dermoscopic images collected from the International Skin Imaging Collaboration (ISIC) archive into either benign or malignant. Our objective here is to make the medical diagnosis of skin cancer much more cost-effective and as efficient as possible. First, the input images have to undergo image preprocessing to remove unwanted noise, then we perform image augmentation to increase the number of images. Next, by implementing Otsu’s method for thresholding coupled with masking using bitwise AND operation, we segment the lesion portion of the image. Our model acquired satisfying results with an accuracy of about 85% and the AUC (Area Under The Curve)–ROC (Receiver Operating Characteristics) value of about 0.852. The precision, recall and F1-score values for benign class stand at around 0.89, 0.82 and 0.85 respectively, whereas, for malignant class, it is about 0.80, 0.88 and 0.84 respectively. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Harnessing Artificial Intelligence for Preventing and Detecting Addiction in Digital Healthcare and social media among students of age group 12 to 18 5

Harnessing Artificial Intelligence for Preventing and Detect...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Akhil, Karnam Sireesha, Jangili Sai, Gundu Venkata Reddy, Katanguri Sai Shashidhar Haripriya, Jonnalagadda Vnr Vignana Jyothi Institute Of Engineering And Technology Department Of Computer Science And Engineering Hyderabad India

ISBN: (纸本)9798350330861

The comprehensive review explores the application of artificial intelligence (AI) and machine learning techniques in studying and addressing social media addiction in the context of digital healthcare. The review covers a wide range of topics, including the use of affective expressions, like insecurity or withdrawal symptoms and AI to detect mental distress. AI plays a vital role in detecting and preventing mental disorders related to social media usage, AI-based approaches for identifying individuals at risk of developing addictive behaviors on social media platforms, and the utilization of AI for detecting mental illness through social media data. It highlights how AI algorithms can analyze user-generated content on social media platforms to identify signs of mental distress, allowing for timely interventions and support. By detecting early warning signs, AI can facilitate personalized interventions and real-time monitoring to mitigate social media addiction. It showcases the potential of AI algorithms to analyze social media content and identify indicators of mental health conditions, providing valuable insights for early detection and intervention. Additionally, the review emphasizes the integration of AI as a preventive measure for social media addiction. By exploring the application of AI, particularly deep learning algorithms, in detecting depression among Twitter users, highlighting the potential of AI to identify individuals in need of support. Moreover, the review discusses the expanding field of digital psychiatry, the promotion of digital well-being, and the impact of social media addiction. It also examines the role of psychiatric symptoms in predicting internet addiction among adolescents and the importance of understanding contributing factors for the development of preventive measures. © 2023 IEEE.

关键词： Diseases

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