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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Pagrut, Shrijeet Jawandhiya, Pradip Shri Sant Gajanan Maharaj College of Engineering Department of Computer Science and Engineering Maharashtra Shegaon India Pankaj Laddhad Institute of Technology and Management Studies Department of Computer Science and Engineering Maharashtra Buldhana India

ISBN: (纸本)9798331528713

Nowadays Research and Development of Novel and Efficient drones becomes the core part of strategic practices of every nation. There is a growing need of using a variety of Unmanned Aerial Vehicles with significant capabilities in Civilian, Military and Commercial Applications. UAVs flight patterns such as Vertical Takeoff Landing and Horizontal Takeoff Landing, its maximum altitude capacity, range are discussed. Designing of Low Flying drones are classified into Small, Micro and Nano-Pico-SmartDust(NPSD) categories. Use and Impact of different materials like Titanium alloy in design is provided. Additionally, current activities in the market related to small drones and future research direction in the field of utility and applications drones are discussed. © 2024 IEEE.

关键词： Drones

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Multilingual Global Translation using Machine Learning

Multilingual Global Translation using Machine Learning

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Dandge, Harsh Lokhande, Meghana Jadhao, Viraj Patil, Swapnil Powar, Sarvesh Pimpri Chinchwad College of Engineering Department of Computer Engineering India Symbiosis Institute of Technology Symbiosis International University Department of Computer Engineering Lavale Pune India

ISBN: (纸本)9798350397208

Language remains as a communication between two people while allowing them to understand and express each other's emotions, ideas, and views. In light of the globalization of the IT business, platforms that support several languages have become a standard. Text recognition is one of the most researched topics and is done with the help of Deep Neural Networks (DNN). People find it easy to read text, but computers have a hard time doing it. However, there are also several challenges associated with creating and managing multilingual websites, including: Content creation, Technical implementation, implementing a multilingual website can be technically challenging, content optimization, Maintenance and updates. By taking a paragraph as input and processing the handwritten English characters, the article hopes to convert them into a computer-readable format paragraph with support for symbols and cursive writing, then train a neural network system to do so. Machine Learning algorithms (LSTM, FLITE, VAD, etc.) are studied for the various multilingual language conversion. The model is based on numerous natural language processing concepts and was created by adding multilingual capabilities to Google's previous Speech Recognition model. The article aims to develop a multilingual recognition model that will make it possible for non-readers to interact with digital devices in their native tongue. © 2023 IEEE.

关键词： Websites

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Review of Efficient Load Balancing Technique to Improve QoS Parameters Fog Computing 2nd

Review of Efficient Load Balancing Technique to Improve QoS ...

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2nd International Conference on Innovations in Data Analytics, ICIDA 2023

作者： Sharma, Arti Mahapatra, Rajendra Prasad Sharma, Vineet Kumar Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad201204 India Department of Computer Science and Engineering KIET Group of Institutions Ghaziabad201206 India

ISBN: (纸本)9789819734658

Fog networking is an aspect of the IoT (Internet of Things) idea, which sees most of the products used by humans on a daily basis connected to one another. Smart phones, smart health monitoring equipment, as well as other similar technologies are examples. The goal of fog computing (FC) is to lessen the burden on CC. Fog computing advances processing of data, communication, storing and analysis nearer to edge nodes and applications. To alleviate some of the drawbacks of cloud computing, a significant number of apps and services are shifting to fog computing. The main problem in fog computing is how to improve service quality. It is clear to observe that the SNNC and EBS have improved QoS to a great extent. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fog computing

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Detection and Classification of Novel Attacks and Anomaly in IoT Network using Rule based Deep Learning Model

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SN computer Science 2024年第8期5卷 1056页

作者： Chakraborty, Sanjay Pandey, Saroj Kumar Maity, Saikat Dey, Lopamudra Department of Computer and Information Science (IDA) REAL AIICS at Linköping University Linköping Sweden Department of Computer Science and Engineering Techno International New Town Kolkata India Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering Sister Nivedita University Kolkata India Department of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata India Department of Biomedical and Clinical Sciences Linköping University Linköping Sweden

Attackers are now using sophisticated techniques, like polymorphism, to change the attack pattern for each new attack. Thus, the detection of novel attacks has become the biggest challenge for cyber experts and researchers. Recently, anomaly and hybrid approaches are used for the detection of network attacks. Detecting novel attacks, on the other hand, is a key enabler for a wide range of IoT applications. Novel attacks can easily evade existing signature-based detection methods and are extremely difficult to detect, even going undetected for years. Existing machine learning models have also failed to detect the attack and have a high rate of false positives. In this paper, a rule-based deep neural network technique has been proposed as a framework for addressing the problem of detecting novel attacks. The designed framework significantly improves respective benchmark results, including the CICIDS 2017 dataset. The experimental results show that the proposed model keeps a good balance between attack detection, untruthful positive rates, and untruthful negative rates. For novel attacks, the model has an accuracy of more than 99%. During the automatic interaction between network devices (IoT), security and privacy are the primary obstacles. Our proposed method can handle these obstacles efficiently and finally identify, and classify the different levels of threats. A brief set of comparisons on detection accuracy and time measurements with some state-of-the-art methods are discussed in this paper. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection Classification Deep Learning Machine Learning Network-attack

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Backpropagation Shuffled Leaping Neural Network Implementation in Classification of DDoS Packet Flow Traffic in Data Mining

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SN computer Science 2024年第8期5卷 1131页

作者： Umamaheswari, N. Kumutha, K. Kalaivani, M.S. Ramya, R. Tagore College of Arts and Science Tamilnadu Chennai India Department of Computer Application- PG Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Computer science and Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India

All web applications remember the ultimate goal of storage services, Distributed Denial of Service (DDoS), to achieve high security from various attacks. The client-server application reduces fees and runs the elite registration gadget, while the notable part assumes that it is manufactured using a grant application. Analysis of the traceback data is difficult in the previous system, and then it containsa common DDoS attack *** DDoS attacks involving different network layers such as (SIDDoS (SQL (Structured Query Language) injection), HTTP (HyperText Transfer Protocol) flood, TCP (Transmission Control Protocol) are recommended here as there are signs that new datasets of common datasets are not being collected in the proposed system. The DDoS attack classification consists of three main steps: preprocessing, trace out the source IP address, Backpropagation Shuffled Leaping Neural Network (BSLNN) based on maintaining the specifications’ integrity. The preprocessing is the first step based on the Gabor Filter used to remove the data’s noise and the second Adaboost Random Optimal Selection method to analyzethe packet flow’s relative frequency. The third step is the Backpropagation Shuffled Leaping Neural Network (BSLNN) classification of traceback data maintaining the specifications analysis’s integrity. The proposed system achieves good periodic study of packet flow accuracy and time complexity. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adaboost random optimal selection Backpropagation Shuffled Leaping Neural Network (BSLNN) Gabor filter Preprocessing Trace out the source IP address

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Leveraging XGBoost based GBM for Proactive Detection of Man-in-the-Middle Cyber Attacks

Leveraging XGBoost based GBM for Proactive Detection of Man-...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Manjaragi, Shivanand V. Asrani, Deepak Kumar, Arvind Sandesh, R. Maindola, Meenakshi Sai, Aarush Department of Computer Science and Engineering Hirasugar Institute of Technology Karnataka Nidasoshi591236 India Department of Computer Science and Engineering MIET Uttar Pradesh Greater Noida India SCA MRIIRS Haryana Faridabad India Department of Computer Science and Engineering ATME College of Engineering Karnataka Mysuru India Department of Computer Science Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India Department of CSBS Gyan Ganga Institute of Technology and Sciences Madhya Pradesh Jabalpur India

ISBN: (纸本)9798350374933

Man-in-the-Middle (MitM) cyber-attacks provide a substantial risk to network security, since they allow hostile individuals to intercept and alter communications between two parties. Conventional detection approaches frequently fail to recognize these complex threats, requiring the creation of more resilient and precise solutions. This study introduces a model called XGBoost-based Gradient Boosting Machine (GBM) to identify MitM attacks in advance. The system employs extensive datasets from Kaggle, namely CICIDS 2017 and UNSW-NB15, for the purpose of training and evaluating the model. By carefully preparing the data, selecting relevant features, and doing thorough cross-validation with hyperparameter optimization, the suggested model attains exceptional performance metrics. The assessment findings indicate an accuracy of 0.98, precision of 0.97, recall of 0.96, F1-Score of 0.965, and a remarkable ROC-AUC score of 0.99. Our model regularly outperforms existing technologies like as Random Forest and Traditional GBM in identifying MitM attacks, achieving higher performance metrics across the board. This research demonstrates the effectiveness of the XGBoost-based GBM model in improving cybersecurity defenses and suggests that it might serve as a standard for future advancements in cyber-attack detection systems. The suggested system's exceptional accuracy and resilience make it an invaluable tool for proactively detecting and reducing MitM attacks. © 2024 IEEE.

关键词： Cyber attacks

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An Approach to Disambiguate the Innate Sense of a Message with the Help of Emoji

An Approach to Disambiguate the Innate Sense of a Message wi...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Bhattacharyya, Saikat Maity, Surela Maiti, Suraj Pal, Alok Ranjan Pal, Antara College of Engineering and Management Kolaghat Department of Computer Sc and Engineering Kolaghat India National Institute of Technology Durgapur Department of Computer Sc and Engineering Durgapur India

ISBN: (纸本)9781665462631

This project aims to understand the tonality of a text using emoticons as the predictor. The emoticons have transpired to be an increasingly significant and standardized part of modern textual inputs. The combination of visual and textual content in the same message builds a compact form of communication, sometimes which becomes tough to portray by the words only. Emoticon prediction and word-emoticon embedding could be used for any kind of Sentiment Analysis based works like Opinion Mining, User Profiling etc. This work focuses on identifying the tone of emotion behind a body of text which is used to analyze the sentiment of a user according to h(is)er comments or messages in online media. This experiment is carried out on the comments collected from some Twitter handles, Facebook walls and Whatsapp conversations. During the experiment, it is observed that in a few cases the senses of different comments are not possible to retrieve only from the words, but the texts which are combined with some emoticons are evaluated correctly. Challenges faced in this work are explained at the end of this report. © 2023 IEEE.

关键词： Sentiment analysis

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Optimizing Gene Expression Analysis Using Clustering Algorithms 1

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Bikku, Thulasi Ramu, J. Sekhar, J. Chandra Pratap, V. Krishna Pujari, Jeevana Jyothi Computer Science and Engineering Amrita School of Computing Amaravati Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Database Systems School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

Gene expression analysis plays a crucial role in understanding biological processes and diseases. However, the high-dimensional nature of gene expression data poses challenges for its analysis and interpretation. Clustering algorithms have been widely used for identifying patterns in gene expression data, but their effectiveness is limited by the choice of algorithm and parameters used. In this study, we propose an approach for optimizing gene expression analysis using clustering algorithms. Initially, a range of clustering techniques were assessed, encompassing K-means, hierarchical clustering, and DBSCAN. This assessment was conducted using gene expression information sourced from a publicly accessible database. Subsequently, various techniques for feature selection were employed. These techniques encompassed principal component analysis (PCA) as well as correlation-based feature selection (CFS). Their objective was twofold: to decrease the data's dimensionality and to enhance the effectiveness of the clustering process. From the study, we came to know that the combination of K-means clustering with PCA feature selection outperformed other methods in terms of clustering accuracy and stability. We also identified a set of genes that were consistently associated with different clusters, providing insights into the underlying biological processes. Overall, our study demonstrates the importance of careful algorithm and parameter selection for gene expression analysis and highlights the potential of clustering algorithms for identifying meaningful patterns in gene expression data. The suggested methodology entails merging DBSCAN with K-means clustering, which could be extended to diverse high-dimensional datasets for the enhancement of their analysis and comprehension. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Principal component analysis

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A Comparative Analysis of Advanced Machine Learning Algorithms to diagnose Parkinson's Disease 2

A Comparative Analysis of Advanced Machine Learning Algorith...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Roy, Sourabarna Pal, Tannistha Debbarma, Swapan Department of Computer Science and Engineering National Institute of Technology Tripura Barjala Jirania Agartala799046 India

Parkinson's disease (PD) is a prevalent and debilitating neurological disorder affecting millions worldwide, necessitating early and precise diagnosis for effective management and therapy. Recent breakthroughs in artificial intelligence (AI) applications within the medical field have showcased promising potential in disease identification and diagnosis. This study presents a comprehensive analysis of cutting-edge AI-based methods for the identification of Parkinson's disease. This paper encompasses extensive testing on substantial datasets containing both PD patients and healthy individuals, aiming to assess the efficacy of AI models. Performance metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC-ROC) are employed to evaluate model performance rigorously. This paper is not only reviewing the state-of-the-art AI techniques for Parkinson's disease identification but also introduces novel contributions in the field. Our results demonstrate the potential of AI in achieving early and accurate PD diagnosis, offering hope for improved patient care and treatment outcomes. Furthermore, this study paves the way for future research and development in the domain of AI-driven healthcare, with the ultimate goal of enhancing the quality of life for individuals suffering from Parkinson's disease and similar neurodegenerative conditions. © 2024 Elsevier B.V.. All rights reserved.

关键词： Magnetic resonance imaging

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Automatic Detection and Classification of Wild Animal Species Using YOLO Models 3

Automatic Detection and Classification of Wild Animal Specie...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Thangaraj, Rajasekaran Charly Jerome, J. Sanjith, M. Rithick Saran, K. Sasikumar, Sudev Vijayakumar, S. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of English Coimbatore India

ISBN: (纸本)9798350322842

Using a camera trapping framework, which employs cameras activated by sensors to record a burst of pictures of animals in their habitat, it is feasible to monitor animals in the open. This paper explores the effectiveness of YOLOv3 and YOLOv5 object detection algorithms in automated animal tracking through the use of computer vision. A dataset of six animal classes was utilized to train and test both models. The results demonstrate that YOLOv5-m achieves an 81% detection accuracy rate, surpassing that of YOLOv3. In addition, the two models were evaluated based on detection speed and model size, with YOLOv5-m providing higher accuracy and comparable speed and model size to YOLOv3. Overall, these findings highlight the potential of computer vision and deep learning in automated animal tracking, and emphasize the importance of selecting appropriate algorithms for various use cases. © 2023 IEEE.

关键词： Animals

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