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Emerging Research in Computational science (ICERCS), International Conference on

作者： Anju A T Binu P Chacko Mohamed Basheer K P Department of Computer Science Sullamussalam Science College Malappuram India Department of Computer Science Prajyoti Niketan College Trisssur India Department of Computer Science Amal College of Advanced Studies Malappuram India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

The article introduces TrA-MHWR a unique methodology transformer-based model designed to facilitate the recognition of handwritten Malayalam words in an offline setting. The model features remarkable ability to transform raw pixel data into meaningful linguistic units. Utilizing the power of Vision transformers and attention units, the model offers a sophisticated framework for robust and precise identification of handwritten Malayalam words in offline contexts. This study presents a comprehensive analysis of the architecture, training methodology, and evaluation results of TrA-MHWR, demonstrating its efficacy in pushing the boundaries related to the recognition of handwritten words in relation to Malayalam language. The proposed study uses DeiT model with a multi-head self-attention layer to improve feature sensitivity and achieved 98.82% accuracy.

关键词： Training Handwriting recognition computer vision Vocabulary Accuracy computer architecture Transformers Feature extraction Data models Character recognition

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Tracking Evolving Labels using Cone based Oracles

arXiv

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arXiv 2023年

作者： Acharya, Aditya Mount, David M. Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD United States

The evolving data framework was first proposed by Anagnostopoulos et al.,where an evolver makes small changes to a structure behind the scenes. Insteadof taking a single input and producing a single output, an algorithmjudiciously probes the current state of the structure and attempts tocontinuously maintain a sketch of the structure that is as close as possible toits actual state. There have been a number of problems that have been studiedin the evolving framework including our own work on labeled trees. We weremotivated by the problem of maintaining a labeling in the plane, where updatingthe labels require physically moving them. Applications involve trackingevolving disease hot-spots via mobile testing units , and tracking unmannedaerial vehicles. To be specific, we consider the problem of tracking labelednodes in the plane, where an evolver continuously swaps labels of any twonearby nodes in the background unknown to us. We are tasked with maintaining ahypothesis, an approximate sketch of the locations of these labels, which wecan only update by physically moving them over a sparse graph. We assume theexistence of an Oracle, which when suitably probed, guides us in fixing ourhypothesis. © 2023, CC BY.

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Intelligent Malicious URL Detection Using Kernel PCA-SVM-GA Model with Feature Analysis 2

Intelligent Malicious URL Detection Using Kernel PCA-SVM-GA ...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Ariawan, Sandy Kumar, Arvind Swamy, Chinthala Kumara Divya, V. Manikandan, V. Devi, S. Rukmani Iakn Tarutung Indonesia Manav Rachna International Institute of Research and Studies Sca Faridabad India Mallareddy College of Engineering CSE-DS Hyderabad India Department of Information Technology Chennai India Jain University Department of Computer Science and Engineering Bangalore India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Chennai India

ISBN: (纸本)9798350373110

Dangerous websites, or rogue URLs, pose a serious threat to cybersecurity. Malicious URLs cost businesses and consumers alike billions of dollars annually because the system lure unsuspecting users into scams that steal personal information, cause financial loss, or install malware. Immediate action is required to detect and eliminate these threats. This form of detection has traditionally relied on blacklists. A major drawback of blacklists is that the system are not comprehensive and do not detect newly established harmful URLs. The procedure consists of three stages, which are preprocessing, feature extraction, and training the model. Important parts of data preprocessing when dealing with URLs are standardization and parsing. When it comes to detecting dangerous URLs using machine learning, feature extraction is a major concern. The proposed approach used a Kernal PCA-SVM-GA to train the model. The proposed approach beats both SVM and GA, with an average accuracy of 93.52%. © 2024 IEEE.

关键词： Support vector machines

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Ornithopter- IoT Based Kamikaze Drone 11

Ornithopter- IoT Based Kamikaze Drone

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11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Singh, Sugandha China Ramu, S. Kumar, Pawan Chinni, Pathlavath Vivekananda Institute of Professional Studies- TC Computer Science and Engineering Department Delhi India Chaitanya Bharathi Institute of Technology Computer Science and Engineering Department Hyderabad India Hyderabad Institute of Technology and Management Mechanical Engineering Department Hyderabad India Hyderabad Institute of Technology and Management Electronics and Communication Engineering Department Hyderabad India

ISBN: (纸本)9798350326147

The ornithopter drone's major objective is to address the issue of border monitoring in particular without being discovered by enemies. Many insect and bird wing variations were researched in attempt to comprehend how natural fliers generate lift by flapping their wings. Following extensive testing on many wing forms and materials, a design for the drone was created. The drone's tail is constructed in a manner reminiscent of an elevator on an airplane. The two distinct parts of the tail are controlled by two servo. Different lift and thrust computation methods are examined and evaluated. The drone is designed to seem like a bird and to hover or glide like a bird, making it more challenging for attackers to find it. The ornithopter is equipped with a variety of parts, such as battery packs, brushless motors, folding propellers, 30amp ESCs, three HS-55 servos, accelerometers, humidity sensors, GPS receivers, thermal imagers, electro-optical day cameras, and Arduino mega boards, It also has a transmitter and receiver. Primary objective is surveillance. It has the capacity to transport flying bombs as well. © 2023 IEEE.

关键词： Drones

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Exploring ML Methods for Sentiment Analysis in Text Data

Exploring ML Methods for Sentiment Analysis in Text Data

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Agarwal, Vishu Tayal, Ashmit Dixit, Rajani Chugh, Bharti Jain, Shikha Kiet Group of Institutions Department of Computer Science and Engineering Ghaziabad India School of Engineering and Technology Vivekananda Institute of Professional Studies- Technical Campus New Delhi India

ISBN: (纸本)9798350375237

Data Analysis has become an important part of our life, as it helps us to draw useful information, decision-making conclusions on any particular raw data. One component of digital data analysis is sentiment analysis. Sentiment analysis evaluates the emotional tone of textual data and classifies it as neutral, negative, or positive. In business, this analysis is frequently utilized for market social media monitoring, market research, consumer feedback and many more. In order to automate the procedure, machine learning models that recognize patterns in labeled data are frequently used. The objective is to derive meaningful insights from textual data so that decisions can be made with knowledge, effectively. In order to provide important insights into public opinion and behavior, this study uses machine learning techniques to investigate the sentiments conveyed in Twitter data. A wide range of Twitter posts, including ones about trending hashtags, events, and subjects, are gathered as part of the process. Sentiment analysis is done using machine learning algorithms. Sentiment analysis and examples of human language are used to train machine learning models. Following this training, machine learning is used by sentiment analysis software to evaluate and rank human language according to its previous training. The current study proposes a technique that best analyzes the sentiments of the text used in social media is gradient boosting classifier. With this method, an additive model can be built in a step-by-step, forward fashion and any differentiable loss function can be optimized. There are n-class regression trees that are fitted on the negative gradient of the loss function, which could be the multiclass log loss or the binary loss. This process is repeated in each step. In the particular case of binary classification, just one regression tree is induced. The computational results demonstrates that gradient boosting approach with an accuracy of 93.62% which outperform

关键词： Sentiment analysis

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Heterogeneous Acceleration System for Scene Text Recognition 4

Heterogeneous Acceleration System for Scene Text Recognition

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4th International Conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Feng, Hua Wu, Junmin Department of Computer Science and Technology University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China

ISBN: (纸本)9798400708831

Scene text recognition has a wide range of applications. Its deep learning algorithm model is mainly CNN and RNN hybrid neural network, which can effectively identify text information in natural scenes. However, complex network models lead to longer inference time. This paper proposes a heterogeneous acceleration system for the scene text recognition neural network model CRNN. Mainly performed hardware deployment and optimization on CNN and LSTM. For CNN, blocking computation and a new data access pattern are proposed to reuse data and reduce on-chip storage. For LSTM, it is implemented in modules, and a method to overcome data dependency is proposed to improve processing efficiency. We implemented this accelerator on the Xilinx ZCU104 platform, which has good performance and low resource utilization. © 2023 ACM.

关键词： Field programmable gate arrays (FPGA)

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Integration of NIST-Aligned Secure IoT Data Management Framework Tinkercad, Flask, and ThingSpeak 1

Integration of NIST-Aligned Secure IoT Data Management Frame...

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Ganga Shirisha, M.S. Shiva Prasad, M.S. Rao, B. Bhaskar Chetti, Sanjeevkumar Naikodi, Chandrakant Davangere University Department of Studies in Computer Science 577007 India Cambridge Institute of Technology Bengaluru56003 India Government of India Ministry of Msme Maharashtra Mumbai India

ISBN: (纸本)9798350376517

This research paper presents a secure IoT data management framework that integrates Tinkercad, Flask, and ThingSpeak, while adhering to the NIST guidelines for data security. The Flask server plays a central role in implementing encryption, hashing, and decryption techniques to ensure the confidentiality, integrity, and authenticity of IoT data. By leveraging NIST-approved algorithms, such as AES for encryption and SHA-256 for hashing, and following secure key management practices, the framework provides a robust and standardized approach to the security of IoT data. The proposed solution demonstrates the effectiveness of using Flask for secure data processing in IoT environments while aligning with industry best practices. © 2024 IEEE.

关键词： Data integrity

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Advancements in Skin Disease Diagnosis Through Machine Learning-Driven Lesion Segmentation Techniques

Advancements in Skin Disease Diagnosis Through Machine Learn...

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International Conference on Inventive Computation Technologies (ICICT)

作者： M. Megala T.R. Nisha Dayana Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced-Studies (VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Skin diseases pose a significant global health challenge, demanding precise diagnosis and effective treatment. This survey paper delves into the realm of enhancing skin disease diagnosis via a pioneering machine learning algorithm focused on lesion segmentation. By amalgamating cutting-edge techniques like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), this algorithm strives to elevate accuracy and efficiency in this pivotal task. The results unequivocally establish the algorithm's superiority in accurately delineating skin lesions. Timely identification and meticulous characterization of skin ailments substantially influence patient outcomes. This contribution augments the corpus of knowledge in machine learning-based medical image analysis. The amalgamation of sophisticated techniques within this algorithm unfurls new horizons for subsequent exploration and innovation in skin disease analysis. The continuous evolution of this field harbors the potential to revolutionize dermatological practice, thereby enhancing the global management of skin disorders. The machine learning algorithm for skin lesion segmentation unveils a propitious avenue for accurate and efficient skin disease diagnosis. The far-reaching impact on patient care and the broader dermatological sphere underscores the imperative of further investigation and validation of this approach.

关键词： Surveys Technological innovation Machine learning algorithms Accuracy Generative adversarial networks Skin Lesions Medical diagnosis Convolutional neural networks Diseases

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Optimizing Real- Time Threat Detection with Feature Engineering Strategies for Intelligent NIDS 6

Optimizing Real- Time Threat Detection with Feature Engineer...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Verma, Jyoti Sethi, Monika Snehi, Manish Sharma, Ishu Kaushik, Keshav Punjabi University Department of Computer Science and Engineering Punjab Patiala India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9798350304480

The escalation of cloud-based network attacks has been observed to be in tandem with the rapid proliferation of cloud-based services worldwide. It has highlighted current network intrusion detection systems'(NIDS) inadequacies in adequately safeguarding against distributed security incidents, particularly within the framework of Internet of Things (loT) networks. The significance of intrusion detection systems in safeguarding computer security cannot be overstated, and considerable research endeavours have been directed toward enhancing NIDS methodologies to counteract network interruptions. Learning-based methods to improve detection accuracy by eliminating correlated, recurring, and irrelevant features are a significant area of emphasis in advancing NIDS. Prior studies have investigated the integration of Machine Learning (ML), Dimensionality Reduction (DR), and Deep Learning (DL) methodologies to enhance the accuracy of classification outcomes on Network Intrusion Detection System (NIDS) datasets. The variability in selected features, design, and kinds of attacks among NIDS datasets requires thoroughly investigating these techniques across diverse datasets. The objective of this article is to furnish an overview of prevailing frameworks through the utilization of benchmark datasets to detect atypical attacks and obtain a comprehensive understanding of the present status of Network Intrusion Detection Systems. The efficacy of current Network Intrusion Detection Systems is assessed by categorizing feature characteristics and applying diverse machine learning methodologies. The study delves into the topic of feature selection techniques as a means of augmenting anomaly detection. Additionally, they investigate the pre-processing of Internet traffic data. Furthermore, the article explores the potential of conceptualizing virtualized real-time Intelligent NIDS using feature engineering techniques. This paper enhances comprehension of NIDS by consolidating varied resea

关键词： Deep Learning Dimensionality reduction Feature Engineering Feature Selection Machine Learning

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Big Data Analytics for Pattern Recognition and Technological Decision-Making

Big Data Analytics for Pattern Recognition and Technological...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Vidhya. N C. Meenakshi Department of Computer Applications Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

The purpose of this study's analysis of big data is to clarify the following ideas and classifications regarding big data, sorts of big data, what they are, and what characteristics they have as determined by the literature review. The discussion continues through the effective result it might produce thereby promising a bright future. The terminology and concepts of big data intellectuals and the information technology industry that pay closer attention to big data are to be discussed. Data is generated and retrieved at a rate that quickly exceeds the threshold range in the digital and computing world. The amount of data and pace of market expansion both rise when information is shared and transferred at the speed of light through wireless and broadband networks. When compared to conventional database systems, high-performance analytics of big data is the effective framework for the analysis and pattern development of processed data.

关键词： Wireless communication Industries Terminology Big Data Prediction algorithms Software Sustainable development Information technology Optimization Systematic literature review

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