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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Saini, Rashmi Singh, Suraj Govind Ballabh Pant Institute of Engineering and Technology Dept. of Computer Science and Engineering Pauri Garhwal India

ISBN: (纸本)9798350364590

Land use and land cover (LULC) classification is crucial for planning and sustainable development, especially in mountainous regions. This study focuses on the Tehsil Karnaprayag, situated in the vicinity of the Greater Himalaya. With a total area of 1858.903 km2 and elevations ranging from 647m to 7641m. Using multispectral Sentinel-2 imagery and Machine Learning techniques i.e. Extreme gradient boosting (XGBoost) and K-nearest neighbor (KNN). This study aims to classify seven distinct land cover classes: snow, forest, water bodies, grassland, agricultural land, fallow land, and built-up areas. The classification results demonstrate the effectiveness of XGBoost classifier achieving an accuracy of 90.22% and a kappa value of 0.882, while KNN achieved an accuracy of 88.59% with a kappa value of 0.862. The analysis reveals that forest and fallow land are the predominant land cover types in the study area, constituting nearly 60% and 15% of the total area respectively. Notably, XGBoost exhibits the highest class-specific accuracy of 97.60% for the snow class, demonstrating its utility in discriminating between challenging land cover types. This research highlights the importance of LULC classification in informing land management strategies and facilitating sustainable development initiatives in mountainous regions. The findings provide valuable insights into the spatial distribution and composition of land cover in Tehsil Karnaprayag, essential for effective planning and conservation efforts in the Himalayan ecosystem. © 2024 IEEE.

关键词： Sustainable development

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Quantifying Salt and Pepper Noise Using Deep Convolutional Neural Network

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Journal of The Institution of Engineers (India): Series B 2022年第4期103卷 1293-1303页

作者： Kumain, Sandeep Chand Kumar, Kamal Department of Computer Science and Engineering National Institute of Technology Uttarakhand Srinagar India

Noise present in an image is an active challenge in the field of Digital Image Processing. Most computer vision applications such as object detection and edge detection are heavily influenced by the presence of noise. The presence of noise mandates pre-processing. Determining the noise level (whether high or low) is a demanding field in current times. Salt and pepper noise is the kind of noise that is commonly caused by dirt debris at the capturing tool and appears as black and white dots in an image. This work proposes a model to identify the level of salt and pepper noise in an image using a deep convolutional neural network (CNN or ConvNet). Once the noise levels are calculated, an appropriate de-noising filter can be applied. The proposed model achieves 98 percent of classification accuracy for salt and pepper quantification task using a data-set proposed in this work, which contains images having a different level of salt and pepper noise. © 2022, The Institution of Engineers (India).

关键词： Salt and pepper noise

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Guarding the Beats by Defending Resource Depletion Attacks on Implantable Cardioverter Defibrillators 1

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10th International Conference on Mathematics and Computing, ICMC 2024

作者： Mitra, Anisha Chowdhury, Dipanwita Roy Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur India

ISBN: (数字)9789819720699

ISBN: (纸本)9789819720682

Implantable Medical Devices (IMDs) have revolutionized the treatment of critical diseases. However, the increasing reliance on these life-saving devices’ wireless functionality has made them vulnerable to cyber-attacks. Implantable Cardioverter Defibrillator (ICD) has emerged as a leading IMD owing to the worldwide surge in cardiac diseases. Given the resource-constrained ICD environment, there’s a pressing need to develop tailored security measures for protection, moving beyond traditional approaches. In this paper, we present resource depletion attack scenarios in an ICD environment where attackers can exploit the ICD’s wireless connectivity function. We propose some comprehensive approaches to mitigate such attacks, offering a significant step forward in safeguarding the well-being of patients. This research contributes to the ongoing efforts to secure the Internet of Medical Things (IoMT) ecosystem and underscores the importance of cyber-security in modern healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Defibrillators

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters 1

Cloud Based Automated Control System Workshops and Rooms for...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar, Dinesh Kuntal, Ravinder Singh Deep, Pragya Chamoli, Anup Shekhar Singh, Pinki Mandal, Rishabh SRM Institute of Science & Technology Department of Computer Science and Engineering UP Ghaziabad India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India IIMT College of Engineering Department of AI & DS Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun248001 India Accurate College of Engineering & Management Department of Computer Science and Engineering Greator Noida India Tula's Institute Department of Computer Science and Engineering UK Dehradun India

ISBN: (纸本)9798331518578

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing. © 2025 IEEE.

关键词： Smart manufacturing

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Deployment of ML Techniques for Optimization of Energy in Residential & Commercial Structures: A Review 5

Deployment of ML Techniques for Optimization of Energy in Re...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Mahajan, Prerna Dahiya, Priya Upadhyay, Rimpi Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Artificial Intelligence Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350383522

Every aspect of daily life in buildings (whether commercial or residential) relies on a constant supply of energy. Energy is used whenever a building's lights are turned on, the temperature is adjusted (using space heaters or air conditioners), or a device is used (such as a computer or printer in an office building or a microwave, water heater, or washing machine in a home or hotel). The HVAC (Heating, Ventilating, and Air Conditioning) system of a commercial building is responsible for a significant percentage of the structure's overall energy use. In order to keep all these devices running properly in present and in the coming years, it is crucial that fuel is enhanced or used as effectively as possible. This study provides a comprehensive review of the literature on the use of Machine Learning techniques to the problem of energy optimization in buildings. © 2024 IEEE.

关键词： Commercial buildings Energy optimization HVAC system Machine Learning

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An Intelligent Human Pose Detection Comparing with Random forest and Recurrent Neural Network 8

An Intelligent Human Pose Detection Comparing with Random fo...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Naveen Kumar, R. Surendran, R. Madhusundar, N. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

This research study intends to compare the effectiveness of Random Forest and Recurrent Neural Network (RNN) models in order to construct an intelligent human posture identification system for real-time applications. This entails gathering a variety of annotated human pose datasets, identifying pertinent characteristics, and applying Random Forest and RNN models. The goals of training and optimization are to improve speed, accuracy, and resource efficiency. Metrics including accuracy, precision, recall, and F1 score will be used to assess the models' performance, with an emphasis on how well they apply in real-time. We will compare the Random Forest and RNN models in detail, taking into account their advantages and disadvantages in various settings. The ultimate goal comprises putting the selected model into practice in a real-time setting, optimizing it in response to performance feedback, and optimized for deployment in environments with limited resources. © 2024 IEEE.

关键词： Adversarial machine learning

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Real-Time Stress Detection Using Facial Images and Convolutional Neural Networks

Real-Time Stress Detection Using Facial Images and Convoluti...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Sindu, M. Ashwin, T. Shyam Sunder, V. Shivasankaran, S. Poongothai, P. Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Mathematics Tamil Nadu India

ISBN: (纸本)9798331535476

Stress is one of the prevailing issues in today's world, deeply impacting the actual well-being of individuals. In this paper, we introduce an innovative method to detect stress that deviates from the mainstream approaches, which rely solely on physiologic signals and self-reporting. Our non-invasive, efficient, and scalable real-time system, built on the facial image data processed by CNNs, makes real-time human stress detection possible. The system captures facial expressions through video or static images, which it then processes within a CNN model to identify stress-related features. CNNs are particularly innovative in that they are adept at automatically extracting and learning hierarchical features from extremely complex, unstructured data like facial images. Facial expression datasets were used to train and test the proposed model in different stress conditions. The model achieved a very good accuracy level in differentiating stressed from non-stressed states, thus putting it on par with professionals. Real-time testing of the system's performance was done on edge devices such as smartphones to prove its potential for use in real-life applications related to mobile health. As facial image data is utilised by the system, thereby killing the need for wearable sensors or invasive physiological monitoring, the approach would be more accessible for detecting stress levels. This research showed that CNN models developed based on facial images can transform stress monitoring. Its implications are wide, applicable in health care, workplace settings, and even self-manageable personal stress. In future work, the model should be generalised for diverse populations, the system's real-time capabilities enhanced, and multimodal data further integrated to increase detection accuracy and reliability. © 2024 IEEE.

关键词： Convolutional neural networks

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Next-Gen Phishing Detection System Based on Federated Learning Integrated CNN-LSTM for SMS Communication 5

Next-Gen Phishing Detection System Based on Federated Learni...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Dafni Rose, J. Jennifer Nissitta, M. Jaya Prabha, S. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385649

Phishing is one of the most common types of cybercrime. It is a method used by attackers to reveal personal information. The possibility of malicious activity is increasing in the rapidly changing the internet, where technological improvements enable seamless connectivity and information exchange. Conventional phishing detection techniques find it difficult to detect the dynamic strategies used by the malicious actors. This research study makes use of a hybrid Convolutional Long S hort-Term Memory (ConvLS TM) neural network to present a novel method for improving the detection rate of phishing in text-based communications. The core of the proposed system lies in combining the ConvLS TM architecture with a Federated Learning (FL) framework. The FL tackles the privacy and data decentralization challenges. This makes it possible to train the model on a variety of entities or organizations. The federated hybrid ConvLS TM model achieves higher accuracy, precision, and recall rates than conventional phishing detection techniques, as shown by the testing results. The proposed model can generalize well over a wide range of datasets from various organizations by utilizing the federated method, which further makes it a reliable and useful tool for detecting phishing attacks in an era where data safety and accuracy plays a major role. © 2024 IEEE.

关键词： Long short-term memory

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Fraud Detection in Audit Data Using Machine Learning Algorithm 2

Fraud Detection in Audit Data Using Machine Learning Algorit...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Ajitha, E. Monishkumaran, S. Kumar, S. Nithish St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350365269

Accounting records are regarded as analytical documents that financial businesses produce on a regular basis to provide multiple viewpoints on their financial condition. Since many investors and auditors rely heavily on these reports to make decisions, certain institutions may falsify them in order to deceive the public and perpetrate fraud. The principal goals of discovery of accounting records forgery are to recognize anomalies brought about by these biases and separate reports that are likely to include fraud from those that are not. Among the most common approaches for analyzing data in this field is binary classification, however it requires an established tagged datasets, which is sometimes unobtainable in real-world situations because fraudulent samples are uncommon. This study presents a novel strategy that addresses the great degree of scale in the parameter area and resolves the shortage of non-fraudulent samples. It is based on machine learning and ensemble models. Additionally, a new datasets is created by gathering ten Iranian bank's annual financial statements and then extracting the three kinds of attributes this study suggests. © 2024 IEEE.

关键词： Smart contract

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A novel centroid based sentence classification approach for extractive summarization of COVID-19 news reports

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International Journal of Information Technology (Singapore) 2023年第4期15卷 1789-1801页

作者： Banerjee, Sumanta Mukherjee, Shyamapada Bandyopadhyay, Sivaji Computer Science and Engineering National Institute of Technology Silchar Assam Silchar 788010 India

A COVID-19 news covers subtopics like infections, deaths, the economy, jobs, and more. The proposed method generates a news summary based on the subtopics of a reader’s interest. It extracts a centroid having the lexical pattern of the sentences on those subtopics by the frequently used words in them. The centroid is then used as a query in the vector space model (VSM) for sentence classification and extraction, producing a query focused summarization (QFS) of the documents. Three approaches, TF-IDF, word vector averaging, and auto-encoder are experimented to generate sentence embedding that are used in VSM. These embeddings are ranked depending on their similarities with the query embedding. A Novel approach has been introduced to find the value for the similarity parameter using a supervised technique to classify the sentences. Finally, the performance of the method has been assessed in two different ways. All the sentences of the dataset are considered together in the first assessment and in the second, each document wise group of sentences is considered separately using fivefold cross-validation. The proposed method has achieved a minimum of 0.60 to a maximum of 0.63 mean F1 scores with the three sentence encoding approaches on the test dataset. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Extractive text summarization Query focused summarization Sentence classification

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