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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Chandrashekar, Chirag Dhamale, Akshat Keshav Praveen Joe, I.R. Vellore Institute of Technology School of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350335095

Machine learning algorithms to identify and categorize cyber hazards have garnered attention recently. Malicious URLs, IP addresses, and files provide the greatest cybersecurity risks. This abstract describes how machine learning-based classification of dangerous URLs, IP addresses, and files identifies and mitigates threats. The article begins with hazardous URL categorization, which uses machine learning to identify if a URL is risky depending on its attributes. URL classification is challenging since there are many legitimate URLs. Researchers have developed many machine learning algorithms that assess URL domain names, length, and keywords to identify fraudulent URLs. Second, machine learning-based IP address classification is discussed. Classifying IP addresses helps identify and combat DDoS and botnet attacks. To discover rogue IPs, machine learning techniques like clustering, classification, and regression examine IP reputation, geolocation, and traffic patterns. File categorization - using machine learning to identify and categorize potentially hazardous files - concludes the study. If users open harmful files, viruses, Trojans, and other malware may infect their systems and steal personal data. Using file type, size, and behavior, supervised and unsupervised machine learning systems may identify dangerous files. This study discusses machine learning methods for detecting and banning risky websites and files. Machine learning algorithms can detect and mitigate cyber risks through analysis and categorization. These algorithms have false positives and negatives, overlook dangers, and require a lot of training data. Thus, machine learning must be employed with intrusion detection systems and firewalls to guard against cyberattacks. © 2023 IEEE.

关键词： Feature extraction

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Securing Medical Image Transmission using Memetic Algorithm 2

Securing Medical Image Transmission using Memetic Algorithm

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Thangapalani, L. Dharini, R. Keerthana, R. Sri Sairam Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

In recent times, medical industry faces adverse issue in terms of protecting the medical records of people all over the world. In remote areas, in case of severe health issues, if they want to contact the doctors in urban areas, the new technology has been introduced is teleradiology where you can send the data through IoT based distributed systems. Securing the medical images while transferring the medical records is an important task to maintain the privacy. Then for securing these records, we have to implement two techniques that i steganography and cryptography. These technologies act as a second layer security in the IoT based distributed systems. Here we will be using "Memetic algorithm", a bio-inspired genetic algorithm. This algorithm is used effectively to encrypt and decrypt the *** paper proposes a novel approach called "Memetic Optimization"for intensifying the security and efficiency of Medical Data(MD) transmission through cryptography. The proposed method involves using memetic algorithms to optimize the key generation process and improve the randomness of the keys used for encryption. By incorporating memetic optimization techniques into the cryptographic process, the security of MD transmission is strengthened, making it more resistant to attacks by unauthorized users. The results of our experiments show the effectual of the proposed method, and its potential for improving the security and efficiency of MD transmission. © 2023 IEEE.

关键词： Steganography

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Privacy Preserved Effective Bill Collection System Using Blockchain 7th

Privacy Preserved Effective Bill Collection System Using Blo...

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7th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2022

作者： Pooja, L. Madhuri, J. Department of Computer Science and Engineering Bangalore Institute of Technology Bangalore India

ISBN: (纸本)9789811993039

Electronic invoices are generated using blockchain technology. Additionally, it has the potential to fundamentally alter how invoices are produced, transactions are validated, and payments are executed. These distributed ledgers continue to be the most well known and are regarded as the cornerstone of cryptocurrencies. They are composed of blocks, and each block contains a transactional record. On a decentralized blockchain network, a document that is accessible and editable by multiple users simultaneously keeps account of who made changes and when. It is impermeable and translucent. The proposed system is used for enforced bill collection system ensuring user privacy using the smart contract. Since every transaction is visible to both parties and every record or block is linked together and encrypted, there is no need for a middleman. It will be possible for users to make automatic payments to a service provider’s digital wallet by using an invoicing system built on the blockchain. Transactions can be easily tracked and monitored, and thanks to blockchain technology, a complete history of all transactions can be downloaded. This study suggests using smart contracts and accountable ring signatures to secure blockchain transactions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Transformer-Based Hate Speech Detection in Assamese

Transformer-Based Hate Speech Detection in Assamese

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2023 IEEE Guwahati Subsection Conference, GCON 2023

作者： Ghosh, Koyel Sonowal, Debarshi Basumatary, Abhilash Gogoi, Bidisha Senapati, Apurbalal Central Institute of Technology Computer Science and Engineering Assam Kokrajhar India

ISBN: (纸本)9798350337785

Hate speech or hate content detection in a text is one of the emerging research in Natural Language Processing. Over several years, the popularity of social media has sky-rocketed, where anyone can share their feelings, desire, anger, etc. Sometimes, these posts include offensive information that hurts others, intentionally or unconsciously. Depending on the forum's visibility, such posts instantly spread over millions of people, which may lead to violence. In these circumstances, it is an essential task to identify whether the comment is hateful. In the Indian language context, this task presents greater complexity due to its multilingual nature and limited availability of resources. This situation motivated us to analyze hate speech in the Assamese language. The paper's contribution is in two aspects: firstly, it involves the development of a tagged dataset in Assamese comprising 4,000 sentences;secondly, by fine-tuning the mBERT-cased (bert-base-multilingual-cased) and the Bangla-BERT (sagorsarker/bangla-bert-base) model using the Assamese data to detect instances of hate speech effectively. Based on what we know, it is one of the pioneering attempts at detecting hate speech in the Assamese language. © 2023 IEEE.

关键词： Speech recognition

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The COVID'19 Dashboard with Implementation of Face Mask Detection and Social Distancing Detection Algorithm

The COVID'19 Dashboard with Implementation of Face Mask Dete...

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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Nair, Meghna Manoj Sai, Gadiparthy Harika Pattabiraman, V. Sriraman, Harini School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350399387

With the outbreak of the global pandemic, India seemed to reach its peak with regard to the number of confirmed positive cases in the months of April and May. Hence, the decision was made to develop a data visualization project with one of the efficient visualization tools Tableau to help people analyze the scenario of the cases across the country. To contribute to state-wise and country-wise analysis of COVID cases in India, 2 dashboards have been developed. The first dashboard consists of the analysis of cases across the country giving a holistic and overall view of the number of deaths, positive cases, and density of cases in each state which is done through color variation. On the other hand, the second dashboard gives a detailed state-wise analysis of cases with the necessary parameters and details catering to every individual state as per the preference of the user. On merging these components, users can get an all-inclusive analysis based on different parameters on the COVID'19 cases across India at a glance. In order to prevent a further spike in cases, implementing a face mask detection system will also take place after conducting a thorough analysis of the possible machine learning algorithms. Two major object detection algorithms were taken into consideration and based on the conclusion drawn, the best algorithm - RCNN was used to implement the face mask detection system. This project is solely motivated by the current extreme situation in the world and as an attempt to provide a solution to combat the same. © 2023 IEEE.

关键词： COVID-19

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Experimental Evaluation of Remote Sensing–Based Climate Change Prediction Using Enhanced Deep Learning Strategy

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 642-656页

作者： Madhavi, Macharapu Kolikipogu, Ramakrishna Prabakar, S. Banerjee, Sudipta Maguluri, Lakshmana Phaneendra Raj, G. Bhupal Balaram, Allam Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India School of Commerce and Management Studies Dayananda Sagar University Bangalore India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Maharashtra Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram Guntur India School of Agriculture SR University Warangal India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Climate change is one of the most pressing global challenges of our time, with far-reaching impacts on ecosystems, economies, and human societies. Accurate prediction of climate change patterns is crucial for developing effective mitigation and adaptation strategies. Remote sensing data, with its ability to provide comprehensive and continuous observations of the Earth’s surface, plays a vital role in monitoring and predicting these changes. However, the complexity and high dimensionality of remote sensing data present significant challenges for traditional predictive models. In this study, we present an Enhanced Deep Learning Strategy for climate change prediction using remote sensing data, integrating a Cascaded Inception-LGBM model. The proposed model combines the feature extraction capabilities of the Inception module with the predictive power of the Light Gradient Boosting Machine (LGBM). The methodology was evaluated on various climate variables, including temperature, precipitation, and CO₂ levels, achieving an accuracy of 97.22%. Comparative analysis with state-of-the-art models demonstrated the superior performance of our approach, particularly in terms of RMSE, MAE, and R² metrics. Robustness tests further confirmed the model’s generalization capabilities under different data conditions. This study underscores the potential of advanced deep learning techniques in enhancing climate change prediction accuracy and offers insights into the key drivers of climate variability. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change prediction Deep learning Environmental monitoring Feature extraction Inception module Light Gradient Boosting Machine Model accuracy NDVI Remote sensing Spectral indices

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An Approach to Analyze the Permanent Magnet Motor Torque Ripple for Improving Torque Power Curves used in Electric Vehicles 15

An Approach to Analyze the Permanent Magnet Motor Torque Rip...

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

作者： Neelakandan, Varatharaj Prabakaran, B. Balaji, D. Suresh, P. Maheswaran, S. Indhumathi, N. Hindustan Institute of Technology and Science Chennai India Vellore Institute of Technology School of Computer Science and Engineering TamilNadu Vellore India Kongu Engineering College Department of Electronics and Communication Engineering TamilNadu Erode India

ISBN: (纸本)9798350370249

An electric car's permanent magnet synchronous motor (PMSM) will be designed and evaluated for performance in this research article. In order to satisfy the unique needs of the electric car and improve its overall performance, the goal is to achieve the highest torque density and the lowest torque ripple. The study's goal is to increase the electric vehicle's power density to do this, which calls for the use of a high-power-density motor. This is addressed by conducting an optimization procedure to increase the generated torque while making sure that the chosen motor stays within the defined size and volume restrictions. The efficient use of permanent magnet (PM) materials with strong thermal limiting capacity, which can enhance the power, is a critical component of this improvement. The efficient use of permanent magnet (PM) materials with high thermal limiting capacity, which may boost the power output under both peak and continuous loads, is a vital component of this optimization. The most important motor design parameters are carefully chosen throughout the optimization process to have the greatest influence on motor performance. Using the finite element software program 'CAE Software,' the research team tests and executes the suggested motor design. The overall goal of this project is to build a PMSM that is highly effective and adapted to the needs of electric vehicles. The goal of the study is to optimize critical motor parameters and use modern PM materials with higher thermal properties in order to increase torque density and reduce torque ripple of the motor. © 2024 IEEE.

关键词： Permanent magnets

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Decentralized Smart Irrigation with Remote Data Visualization 6th

Decentralized Smart Irrigation with Remote Data Visualizatio...

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6th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2022

作者： Kumar, Sanjeev Sharma, Vivek Department of Computer Science and Engineering Technocrats Institute of Technology Bhopal India

ISBN: (纸本)9789811995118

Every sector is evolving faster than ever in this fast-growing world. Smart technologies connected with different things are using the internet to collect and analyze data better to understand the manufacturing line, vehicles, etc. Same technologies can be used in the same way in the agriculture sector. The proposed system has multiple nodes, also known as things, connected to the Internet. These nodes are independent individual units, usually ESP8266, which can collect data using temperature, humidity, and moisture sensors for soil. This data can be stored and visualized in the cloud easily. This data can further be used along with weather reports to manage the irrigation system. Each node can independently control the irrigation of its area or sector. Data is stored on Google Firebase Real-Time Database. All the data can be visualized, and the farmer or cultivator can control irrigation through web clients. Thus, this system can manage irrigation and plantation on vast land with just one centralized portal with a click. It will improve the overall efficiency of the farming sector for farming and plantation in a greenhouse with a lesser workforce. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Irrigation

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Gesture-Driven Virtual Mouse with a Voice Assistant 6

Gesture-Driven Virtual Mouse with a Voice Assistant

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Rahul Kumar, S. Harika, Lakshmi Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394702

Hand gesture recognition plays a pivotal role in facilitating human-computer interactions. The system utilises OpenCV to perform real-time hand tracking and gesture recognition, enabling cursor movement control. Along with a novel Voice command detection by the microphone activates various functions, including clicking actions, launching/stopping gesture recognition, Google searching, finding locations on maps, file navigation, and obtaining the current date/time. The system processes the video stream to segment the hand region. It recognises gestures through contour analysis, translating them into mouse controls. By combining hand gesture recognition and speech recognition, the system enables intuitive hands-free control of the mouse cursor and access to additional functions through voice. The hand region is isolated by applying colour segmentation in the HSV (Hue, Saturation, Value) colour space. The contour is detected to track hand shape and gestures. Structural analysis of the contour and convex hull enables gesture recognition. This virtual mouse system enables handsfree computer control, improving accessibility. By eliminating the need for a physical mouse device, it provides a more convenient and intuitive user experience. The proposed virtual mouse system eliminates the necessity for a physical mouse device, allowing hands-free control. This advances human-computer interaction technology by providing a more accessible and convenient interaction method. © 2023 IEEE.

关键词： Gesture recognition

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Multimodal Autonomous Verbal Assessment with Visual Inspection 2

Multimodal Autonomous Verbal Assessment with Visual Inspecti...

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2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing, PCEMS 2023

作者： Agrawal, Meet Kathale, Atharva Purohit, Sahil Sainis, Kalyani Kumar, Praveen Radke, Mansi Visvesvaraya National Institute of Technology Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350310719

This paper proposes an autonomously assessing system for the verbal examination of candidates. The system uses audio-video inputs and processes them to detect the candidate's spoken answer, and compares it to the model answer in the dataset with the corresponding question. The semantic similarity score will be calculated and used to recommend the next question from the database using various types of recommendation systems discussed in the paper. Additionally, the system employs video analysis techniques to detect and prevent modern malpractices like multiple faces and reading from notes during the examination process. The proposed system aims to improve the efficiency and fairness of verbal examinations by eliminating human bias and accurately evaluating the candidate's understanding of the subject. The system performance will be evaluated using a dataset of spoken answers and the results will demonstrate its effectiveness in improving the efficiency and fairness of the verbal examination process. © 2023 IEEE.

关键词： Semantics

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