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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Singh, Akhilendra Pratap Prabhu, T. Mehta, Deepak Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350306927

The rapid proliferation of Internet of Things (IoT) devices has engendered a complex and diverse ecosystem, demanding innovative solutions to ensure seamless connectivity. This research introduces a novel "Next-Generation Protocols for Enhanced Connectivity in Heterogeneous IoT Ecosystems"method, which addresses the critical challenges posed by this heterogeneity, scalability, security, and energy efficiency. This method leverages adaptability, advanced encryption, and energy-efficient communication mechanisms. The proposed methodology comprises three key algorithms. The Scalability-Optimized Routing Algorithm dynamically adapts routing paths, optimizing data transmission. The Secure Data Exchange Algorithm ensures data integrity and privacy through advanced encryption. The Energy-Efficient Communication Algorithm enhances the longevity of battery-powered IoT devices. Comparative performance evaluations with six original methods, including Lightweight M2M (LwM2M), CoAP, 6LoWPAN, MQTT, DDS, and Zigbee, clearly demonstrate the superiority of the proposed method across security, scalability, reliability, and energy efficiency parameters. This innovative approach paves the way for a more connected, secure, energy-efficient, and adaptive IoT ecosystem. © 2023 IEEE.

关键词： Energy efficiency

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An Evaluation of Hashing and Other Applied Cryptography Algorithms 5th

An Evaluation of Hashing and Other Applied Cryptography Algo...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Haripriya Sharma, Apurva Thomas, Jobin S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

In the modern digital world, cryptography is critical in protecting personal statistics. Most of the cryptography algorithms used to ease data, hashing, and other implemented cryptography algorithms are extensively used. Hashing refers to transforming plaintext or every other form of statistics into unreadable ciphertext. This type of encryption is used primarily to store passwords, authentication records, and other touchy records. Other applied cryptography algorithms include symmetric encryption, uneven encryption, and key exchanges. In this paper, an assessment of hashing and different implemented cryptography algorithms is conducted. The evaluation will be conscious of each of those algorithms’ distinctive traits, strengths, weaknesses, and safety concerns. The review also appears in the scenarios wherein one-of-a-kind algorithms can be carried out. It is followed by comparing the one-of-a-kind algorithms about their overall performance, protection, and comfort. Numerous pointers are also discussed regarding the application of these algorithms. Finally, the paper concludes with a standard assessment of the multiple algorithms and their suitability in one-of-a-kind contexts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Enhancing Remote Oversight of Plants with a Compact IIoT System 3

Enhancing Remote Oversight of Plants with a Compact IIoT Sys...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Singh, Shweta Ganorkar, Akshaya M Anujhna, B. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350343274

It is impossible to overestimate the importance of remotely monitoring industrial facilities in the context of the Industrial Internet of Things. The goal of this line of research is to improve remote monitoring by using real-time data analytics, predictive maintenance, and energy optimization with a tiny Industrial Internet of Things (IIoT) system. The three most important algorithms are an energy optimization method using a proportional-integral-derivative (PID) controller, a predictive maintenance strategy based on machine learning models, and a real-time data streaming and analysis tool. The proposed method is said to be able to use these algorithms in real time, which enables it to schedule preventive maintenance, identify anomalies, and use energy effectively. Effective, sustainable, and data-driven plant management support should be expected to result in minimal downtime, lower operating costs, and ecologically conscious operations. © 2023 IEEE.

关键词： Proportional control systems

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AI-Driven Advanced Text Matching for Improved information Capture and Retrieval 3

AI-Driven Advanced Text Matching for Improved Information Ca...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Agrawal, Trapty Kulhar, Kuldeep Singh Singh, K. Ranjith Indira, G. Shelke, Priya M. Kannagi, A. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Civil Engineering Jaipur India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

This paper presents an AI-pushed advanced text matching technique to improve facts capture and retrieval. The proposed approach makes use of deep getting to know and natural language processing algorithms to successfully compare texts and discover similarities and dissimilarities among them. The deep gaining knowledge of algorithm is trained on a textual content corpus that serves as enter statistics for the model. This corpus may be made from diverse styles of textual information depending at the goal of the assessment. To make certain accuracy, the model is outfitted with parameters consisting of word embedding, semantic similarity, and fuzzy string matching. Once skilled, the version is tested to discover false positives and fake negatives. Finally, the model is carried out to a hard and fast of statistics to quantify fits among texts and retrieve useful statistics. Consequences show a discount in false positives and a higher precision of the version in retrieving applicable statistics from given facts. © 2023 IEEE.

关键词： dissimilarities presents similarities statistics version

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Script independent text segmentation of document images using graph network based shortest path scheme

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International Journal of information technology (Singapore) 2023年第4期15卷 2247-2261页

作者： Sahare, Parul Tembhurne, Jitendra V. Parate, Mayur R. Diwan, Tausif Dhok, Sanjay B. Department of Electronics and Communication Engineering Indian Institute of Information Technology Maharashtra Nagpur India Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur India Centre for VLSI and Nanotechnology Visvesvaraya National Institute of Technology Maharashtra Nagpur India

Document image processing is one of the growing research fields in the digital world for applications like data base indexing, text recognition, signature verification, web-searching engines, etc. Segmenting intermixed texts (handwritten and machine-printed) from documents is a difficult task. In this paper, script independent text-line and word segmentation techniques are proposed. For text-line segmentation, Dijkstra’s algorithm is employed, whereas for segmenting words, wavelet transform is used. Text-line segmentation is modeled as a general image segmentation task. Dijkstra’s algorithm is a shortest path planning method, which is utilized for boundary growing process. This forms potential text-line boundary regions. For word segmentation, energy map is calculated first using wavelet transform and further, Gaussian filter is used for text-blocks creation. Proposed techniques are evaluated on different databases contain documents of different scripts. Benchmarking analysis is performed with other approaches where highest segmentation accuracies of 97.6% and 98.1% are obtained by text-line and word segmentation techniques, respectively. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Dijkstra’s algorithm Document image analysis Script independent Text processing Text segmentation

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A Paradigm Shift in Ethereum Network Analysis Through Google BigQuery,Portation, and Visualization 6

A Paradigm Shift in Ethereum Network Analysis Through Google...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Suresh Kumar, N. Mirdula, S. Singh, Pushpa Gayathri, T. Jayanthi, S. Anandraj, M. School of Computer Science and Engineering Jain Deemed-to-be University Ramanagara India SRM TRP Engineering College Department of Electronic and Communication Engineering Trichy India IILM University Department of Computer Science and Engineering Greater Noida India MVJ College of Engineering Department of Computer Science and Engineering Bangalore India Guru Nanak Institute of Technology Department of Information Technology Ibrahimpatnam India Mahendra College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350306613

The primary objective of this research is to comprehensively explore and analyze the dynamics of the Ethereum network using innovative methodologies and system architectures. The study aims to extract meaningful statistics from the Ethereum blockchain, focusing on account activity, popularity trends, and the distribution of transactions. Through rigorous data collection, robust query construction, and advanced analytical techniques, the research seeks to provide valuable insights into how the Ethereum network has evolved, particularly in the aftermath of the 'crypto bubble explosion.' The overarching goal is to contribute to the understanding of Ethereum's structural patterns, user behaviors, and the impact of external factors on the network. The research has yielded significant results, unveiling key insights into Ethereum network dynamics. The data collection phase, facilitated by Google BigQuery, successfully captured and filtered relevant information from a specific block range post the 'crypto bubble explosion.' The SQL queries, strategically designed for active account identification and popularity assessment, demonstrated efficiency and accuracy in handling the vast Ethereum dataset. The proposed system, introducing the novel methodology of 'portation,' showcased its efficiency in extracting and interpreting Ethereum blockchain data using Google BigQuery. The system architecture, as illustrated in the diagram, proved to be a well-coordinated and dynamic framework, emphasizing the seamless flow of data and processes. © 2024 IEEE.

关键词： Crypto Bubble Explosion Decentralized Data Transactions Ethereum Blockchain Google BigQuery Power-law Analysis

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The Synergy of Advanced Language Models, Sequential Skeleton Features, and GAN-Enhanced Data Augmentation for Unprecedented Precision

The Synergy of Advanced Language Models, Sequential Skeleton...

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

作者： Sahoo, Girija Shankar Dass, Sandhya Prakash, Febin Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350315127

The precision comparison between the proposed method and traditional methods is a crucial evaluation of their performance in machine learning and information retrieval tasks. Precision, a fundamental metric, measures the accuracy of positive predictions. This study presents the 'Proposed Method,' an innovative approach, alongside 'Traditional Methods,' representing conventional approaches. The comparison, visualized in a precision-focused diagram, demonstrates how effectively each method identifies relevant instances. A higher precision value in the Proposed Method implies its proficiency in minimizing false positives, particularly vital in precision-critical applications like medical diagnosis and spam email classification. This analysis aids stakeholders and researchers in assessing the precision-related trade-offs between innovation and convention, guiding informed decisions for specific contexts. © 2023 IEEE.

关键词： information retrieval

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An Analysis of Identification of Plant Leaf Diseases and Classification Using Machine Learning and computer Vision 3rd

An Analysis of Identification of Plant Leaf Diseases and Cla...

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3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Sethy, Abhisek Surya Prakash, Y. Archana, U. Nayak, Soumya Ranjan Kumar, Raghvendra Department of Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar India Department of Computer Science and Engineering MVGR College of Engineering Andhra Pradesh Vizianagaram India Department of Information Technology GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of CSE GIET University Gunupur India

ISBN: (纸本)9789819705726

Agriculture is a significant source of revenue for Indians. Farmers can grow a wide range of crops. However, crop growth is hampered by illnesses. Plant pests are one of the reasons for the damage to plant crops. Different diseases affect different plants. The proposed work discussed a detailed investigation of disease recognition and classification. Implementing reliable methods with high recognition rates for diagnosing plant diseases is still on hold. Supervised learning techniques are mostly employed for the identification of diseases in plants. Unsupervised learning is an alternative, though. This suggested illness detection system includes image acquisition, pre-processing, segmenting the images, extracting features, and classification. As a result, image processing and feature extraction technologies have mainly been used to detect plant illnesses. This paper was classified based on two important concepts: leaf feature identification and classification. A detailed in-depth disclosure of disease identification and classification efficiency is depicted here, considering and evaluating certain well-defined suggested techniques by various authors from the last few decades. This paper also focuses on the state-of-the-art methodologies that came into the picture the in the present day in the boom of AI. Finally, examine and categorize the obstacles and potential future improvements in this domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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IIMH: Intention Identification in Multimodal Human Utterances 2023

IIMH: Intention Identification in Multimodal Human Utterance...

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15th International Conference on Contemporary Computing, IC3 2023

作者： Keerthan Kumar, T.G. Dhakate, Himanshu Koolagudi, Shashidhar G Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India Department of Information Science and Engineering Siddaganga Institute of Technology Tumkur India

ISBN: (纸本)9798400700224

Intention identification is a challenging problem in the field of natural language processing, speech processing, and computer vision. People often use contradictory or ambiguous words in different contexts, which can sometimes be very confusing to identify the intention behind an utterance. Intention identification has many practical applications in the fields of natural language processing, sentiment analysis, social media analysis, robotics, and human-computer interaction, where valuable insights into user behavior can be achieved by identifying intention. In this work, we propose a model to determine whether an utterance made by a person is intentional or not intentional. To achieve this, we collected a multimodal dataset containing text, video, and speech from various TV shows, movies, and YouTube videos and labeled them with their corresponding intention. Feature extraction is done at both utterance and word levels to get useful information from all three modalities. We trained the baseline model using SVM to set a benchmark performance. We designed an architecture to detect the contradiction between positive spoken words with negative facial expressions or speech to identify an utterance as non-intentional. Along with the architecture, we used different approaches for classification and got the best results with the Support vector machine (SVM) classifier using RBF kernel, with an accuracy of 78.83% and proven to be better compared to the baseline approach. © 2023 ACM.

关键词： Support vector machines

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Smart EV Charging to Mitigate Range Anxiety in VANET Backbone Guided by Named Data Networking and Block-Chain 2

Smart EV Charging to Mitigate Range Anxiety in VANET Backbon...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Vendan, Vetri Chaudhary, Anamika Galgotias University Department of Computer Science and Engineering Greater Noida India Noida Institute of Engineering and Technology Department of Information Technology Greater Noida India

ISBN: (纸本)9798350347456

A interesting area of research for future connected car application is the development of an effective charging management system for on-the-go EVs, which is especially important given the inherent unpredictability in EV mobility. The main technological hurdles here are the necessity for intelligent decision making when choosing a CS and the related communication infrastructure for exchanging data between the grid and mobile EVs. Switching to electric vehicles has the potential to reduce pollution, vehicle maintenance expenses, and increasing gas prices (EVs). The range anxiety issue is a significant impediment to the rapid adoption of EV. Range anxiety can be alleviated by guaranteeing that a charging point will be found within the vehicle's driving range, subject to other constraints such as waiting time, reliability, cost, and so on. In this research, we suggest building a P2P infrastructure on top of a VANET so that users may trade power with one another in a secure and reliable manner. Although the labour itself is decentralised, secure, and reliable, the associated financial transactions are not. The proposed framework for exchanging electric charge is based on the intersection of three concepts from the realm of automotive social networks: data connectivity, the bitcoin, and the anxiety of having insufficient battery power. We address reliability using an established collaborative paradigm rooted in the field of automotive social networks. Organizational verification previous to trading and encrypted parameter exchange provide safety. With the help of named data networking, reliable charging points may be located using a set of criteria. The simulation results demonstrate that the proposed technique decreases the amount of time it takes to complete an energy transaction by 17%, boosts coverage for picking charging providers by 13%, and decreases the amount of erroneous provider filtering by 18%. © 2023 IEEE.

关键词： Commerce

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