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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Amasala, Likhitha Ponnuru, Mahesh Srideviponmalar, P. School of Computer Science and Engineering Vellore Institute of Technology Amaravathi522237 India School of Computing SRM Institute of Science and Technology Potheri603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri603203 India

At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors. © 2024 The Authors. Published by Elsevier B.V.

关键词： Smart contract

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Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private States 30

Opinion Mining Using Pre-Trained Large Language Models: Iden...

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Joint 30th International Conference on computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ahmadnia, Saeed Jordehi, Arash Yousefi Heyran, Mahsa Hosseini Khasheh Mirroshandel, Seyed Abolghasem Rambow, Owen Department of Computer Engineering Department of Linguistics United States Institute for Advanced Computational Science United States Stony Brook University Stony BrookNY United States University of Guilan Guilan Rasht Iran

ISBN: (纸本)9782493814104

Opinion mining is an important task in natural language processing. The MPQA Opinion Corpus is a fine-grained and comprehensive dataset of private states (i.e., the condition of a source who has an attitude which may be directed toward a target) based on context. Although this dataset was released years ago, because of its complex definition of annotations and hard-to-read data format, almost all existing research works have only focused on a small subset of the dataset. In this paper, we present a comprehensive study of the entire MPQA 2.0 dataset. In order to achieve this goal, we first provide a clean version of MPQA 2.0 in a more interpretable format. Then, we propose two novel approaches for opinion mining, establishing new high baselines for future work. We use two pre-trained large language models, BERT and T5, to automatically identify the type, polarity, and intensity of private states expressed in phrases, and we use T5 to detect opinion expressions and their agents (i.e., sources). © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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Hybrid architecture CNN-BLSTM for automatic speech recognition 3

Hybrid architecture CNN-BLSTM for automatic speech recogniti...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Dar, Muzaffar Ahmad Pushparaj, Jagalingam Vellore Institute of Technology School of Advanced Sciences Department of Mathematics Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Computational Intelligence Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

With several deep learning approaches, the domain of automatic speech recognition (ASR) has seen notable advancements in recent times. The domains of intelligent human- computer interaction and machine translation greatly benefited from accurate speech recognition. The present study introduces a hybrid architecture amalgamating a convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) for speech recognition. For aligning speech input sequences with corresponding text output sequences, it uses the connectionist temporal classification (CTC) technique. The experiments were done on the LJ speech dataset, and the results showed with an increased number of training samples, the performance of the speech recognition algorithm tended to be increased but the time taken for training gradually increased over time. Moreover, a trained speech recognition algorithm exhibits a longer training time when the recognition accuracy is lower. In this study, we implemented a hybrid deep learning model CNN-BLSTM, in conjunction with the CTC loss function, which attains a word error rate (WER) of 36.97% on the testing dataset. © 2024 IEEE.

关键词： Speech recognition

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Scheduling Multi-Mode Resource-Constrained Projects Using Heuristic Rules Under Uncertainty Environment

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computers, Materials & Continua 2022年第1期70卷 847-874页

作者： Mohamed Abdel-Basset Ahmed Sleem Asmaa Atef Yunyoung Nam Mohamed Abouhawwash Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA

Project scheduling is a key objective of many models and is the proposed method for project planning and *** scheduling problems depend on precedence relationships and resource constraints,in addition to some other limitations for achieving a subset of *** scheduling problems are dependent on many limitations,including limitations of precedence relationships,resource constraints,and some other limitations for achieving a subset of *** project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required,which are known and stable during the implementation *** concept of deterministic project scheduling conflicts with real situations,in which in many cases,some data on the activity’s durations of the project and the degree of availability of resources change or may have different modes and strategies during the process of project implementation for dealing with multi-mode conditions surrounded by projects and their activity *** the multi-mode resource-constrained project problem is an optimization problem whose minimum project duration subject to the availability of resources is of particular interest to *** use the multi-mode resource allocation and schedulingmodel that takes into account the dynamicity features of all parameters,that is,the scheduling process must be flexible to dynamic environment *** this paper,we propose five priority heuristic rules for scheduling multi-mode resource-constrained projects under dynamicity features for more realistic situations,in which we apply the proposed heuristic rules(PHR)for scheduling multi-mode resource-constrained *** projects are considered test problems for the *** obtained results rendered by these priority rules for the test problems are compared by the results obtained from 10 well-known heuristics rules rendered for

关键词： Constrained resource scheduling multi-mode durations dynamic scheduling resource allocation heuristic rules

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Batch-Parallel Compressed Sparse Row: A Locality-Optimized Dynamic-Graph Representation

Batch-Parallel Compressed Sparse Row: A Locality-Optimized D...

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2024 IEEE High Performance Extreme Computing Conference, HPEC 2024

作者： Wheatman, Brian Burns, Randal Xu, Helen University of Chicago ChicagoIL United States Johns Hopkins University Department of Computer Science BaltimoreMD United States School of Computational Science and Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798350387131

The default data structure for storing sparse graphs is Compressed Sparse Row (CSR), which enables efficient algorithms but is not designed to accommodate changes to the graph. Since many real-world graphs are dynamic (i.e., they change over time), there has been significant work towards developing dynamic-graph data structures that can support fast algorithms as well as updates to the graph. This paper introduces Batch-Parallel Compressed Sparse Row (BP-CSR), a batch-parallel data structure optimized for storing and processing dynamic graphs based on the Packed Memory Array (PMA). At a high level, Batch-Parallel Compressed Sparse Row extends Packed Compressed Sparse Row (PCSR, HPEC '18), a serial dynamic-graph data structure built on a PMA. However, since the original PCSR runs only on one thread, it cannot take advantage of the parallelism available in multithreaded machines. In contrast, Batch-Parallel Compressed Sparse Row is built on the batch-parallel Packed Memory Array data structure (PPoPP '24) and can support fast parallel algorithms and updates. The empirical evaluation demonstrates that Batch-Parallel Compressed Sparse Row supports fast parallel updates with minimal cost to algorithm performance. Specifically, BatchParallel Compressed Sparse Row performs up to 420 million inserts per second. Across a suite of 10 graph algorithms and 10 input graphs, Batch-Parallel Compressed Sparse Row incurs 1.05 × slowdown on average and about 1.5 × slowdown at most compared to Compressed Sparse Row (CSR), a classical static graph representation. Furthermore, the empirical results show that Batch-Parallel Compressed Sparse Row outperforms existing tree-based and PMA-based dynamic-graph data structures on both algorithms and updates. © 2024 IEEE.

关键词： Graph algorithms

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Age Classification through Handwriting Analysis using Convolutional Neural Networks and Pre-Segmented Offline Handwritten Gurumukhi Characters 7

Age Classification through Handwriting Analysis using Convol...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Singla, Chinu Maini, Raman Kumar, Munish Punjabi University Department of Computer Science And Engineering Punjab Patiala India Maharaja Ranjit Singh Punjab Technical University Department of Computational Sciences Punjab Bathinda India

ISBN: (纸本)9798350371406

Handwriting analysis is increasingly gaining prominence across various domains, including forensics, education, and biometrics, owing to its invaluable insights. This article introduces an innovative approach to age classification based on offline handwriting in Gurumukhi script, employing a Convolutional Neural Network (CNN) architecture, specifically the Inception-V3 model. The method utilizes pre-segmented offline Gurumukhi characters, commonly employed in the Punjabi language, as input features. This pre-segmentation process ensures precise and efficient extraction of individual characters from handwriting samples. The CNN model undergoes training on a substantial dataset of annotated Gurumukhi characters, encompassing a diverse range of age groups. The dataset comprises 74,760 Gurumukhi characters contributed by 267 writers, with 137 males and 130 females, each providing 280 characters. By inputting the extracted features into the CNN model, our approach accurately predicts individuals' age groups based on their handwriting samples. Experimental results underscore the efficacy of the proposed method, achieving an impressive accuracy rate of 92.7% in age classification. This high classification accuracy unequivocally validates the potential of our method for age classification in handwriting analysis. It underscores its applicability in forensic investigations and document verification scenarios, highlighting the promising role of handwriting analysis in various fields that necessitate age-related information. © 2023 IEEE.

关键词： Deep neural networks

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A Detailed Analysis of Cloud Storage and Key Management Techniques in IoT Driven Smart Grids 7

A Detailed Analysis of Cloud Storage and Key Management Tech...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Sivaprasad Manivannan, I. Roobert, A. Andrew Muthukumaran, N. Rohini College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli627003 India Sri Eshwar College of Engineering Centre for Computational Imaging and Machine Vision Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641202 India

ISBN: (纸本)9798350372816

The Internet of Things (IoT) technology has revolutionized smart grid settings, enhancing efficiency and enabling advanced data analytics. However, the integration of IoT devices within smart grids introduces significant challenges related to data storage and security. This survey provides a comprehensive review of the various classifiers and optimization algorithms employed to enhance cloud storage solutions and key management systems within smart grid environments. Moreover, it symmetrically examines different classifiers, like Machine Learning (ML) and Deep Learning (DL) methods, which are designed for managing cryptographic keys, focusing on their ability to secure communications, authenticate devices, and ensure data integrity in the presence of cyber threats. Additionally, it categorizes and evaluates a wide range of optimization methods, including evolutionary algorithms, particle swarm optimization (PSO), gray wolf optimization (GWO) and firefly algorithm (FFA) based techniques for optimizing the classifiers, highlighting their applicability in optimizing storage capacities, data retrieval speeds, and cost-effectiveness. By synthesizing recent advancements and comparing the efficacy of these techniques, the survey identifies the best individuals and emerging trends that promise to strengthen the resilience and efficiency of IoT-enabled smart grids. The findings promote the critical role of integrated classifiers and optimization techniques in achieving scalable, secure, and reliable smart grid infrastructures. © 2024 IEEE.

关键词： Data integrity

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Analyzing the Evolution of Modern Tamil Script for Natural Language Processing 1

Analyzing the Evolution of Modern Tamil Script for Natural L...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Kaladevi, R. Revathi, A. Manju, A. Department of Computer Science and Engineering Saveetha Engineering College Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9781607685395

History is essential for understanding the society's former state. It is beneficial to understand people's historical status, culture, and how we view the present. The society benefits from the society's diversified culture and unparalleled civilization. The limited sources available to learn about our history are books, epigraphs, and inscriptions. Almost 60% of the Indian inscriptions are in Tamil and only 50% among them are published.. The name India is derived from Indus River and the most vibrant and progressive Indus Valley Civilization is paramount of ancient world. Hinduism, Buddhism, Sikhism, and Jainism are four well-known religions that emerged from this culture. Epigraphs are the most reliable source of information about ancient India's life, culture, religion, and politics. This study examines the evolution of India's various scripts and languages. Discovering the incremental changes in scripts and languages that demonstrate the linguistic relationship between distinct populations is critical. The ability to recognize letters in a variety of scripts, languages, and tools is a boon for natural language processing. © The Electrochemical Society

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Advance Public Bus Transport Management System: An Innovative Smart Bus Concept

Advance Public Bus Transport Management System: An Innovativ...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Patel, Keyurkumar Patel, Divya Chaudhary, Nishit Thakkar, Darsh Savaliya, Pratham Mistry, Armaan Rashtriya Raksha University Artificial Intelligence and Cyber Security Department of IT Gujarat Gandhinagar382305 India Karnavati University Unitedworld School of Computational Intelligence Department of Computer Science and Engineering Gujarat Gandhinagar382422 India

ISBN: (纸本)9798350324136

In the present era, public bus transportation services often lag behind in adopting modern technologies, resulting in numerous operational complexities. These issues encompass challenges such as poor passenger density control, operational inefficiencies, inadequate live tracking, limited real-Time capabilities, suboptimal resource management, route ambiguities, safety concerns, and more. This research paper introduces a comprehensive solution an Advance Public Bus Transport Management System (APBTMS) that aims to address these challenges for the benefit of both passengers and transport management authority. Our proposed system leverages various sensors and IoT (Internet of Things) modules deployed within buses to monitor various onboard activities and physical environmental factors. These enable the making of data-driven decisions in real-Time, Facilitating improved operational control. Key components of the system include GPS (Global Positioning System) tracking for real-Time bus location updates, passenger density/count through CV (computer Vision), and continuous monitoring of physical environmental factors such as temperature, humidity, sound magnitude, and vibration magnitude which facilitate accident/abnormalities detection, Fire detection and helps in continuously monitoring road quality. The application of CV technology, along with passenger count, can detect harm-causing objects inside a bus and can compare the faces of passengers with black-listed passengers (involved in previously reported crimes such as theft, and nuisance). Passengers benefit from these advancements through a dedicated mobile application. A User-friendly website is also offered to public bus transport management authority providing various useful functionalities. With the addition of safety features like real-Time speed detection and many other capabilities in the future, our proposed system can expand its benefits to school bus services and other public transportation services. © 2024

关键词： API (Application Programming Interface) AWS (Amazon Web Services) Cloud Computing CV (computer Vision) GPS (Global Positioning System) GSM (Global System for Mobile communication) IoT (Internet of Things) ML (Machine Learning)

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Integrating Digital Twins with AI for Real-Time Intrusion Detection in Smart Infrastructure Networks

Integrating Digital Twins with AI for Real-Time Intrusion De...

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2024 International Conference on Intelligent Algorithms for computational Intelligence Systems, IACIS 2024

作者： Sasikala, M. Mahaboob John, Y.M. Jothi, B. Nandhini, S. Senthil Kumar, S. Sona College of Technology Department of Information Technology Salem India Mahendra College of Engineering Ece Department Salem India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Karpagam Academy of Higher Education Computer Science and Engineering Coimbatore India R.M.K. College of Engineering and Technology Department of Mechanical Engineering Thiruvallur India

ISBN: (纸本)9798350360660

The integration of Digital Twins with Artificial Intelligence (AI) represents a revolutionary approach to enhancing real-time intrusion detection in smart infrastructure networks. Digital Twins, as virtual replicas of physical assets, enable continuous monitoring and predictive analysis, while AI provides advanced analytical capabilities for identifying and mitigating potential security threats. This paper presents a comprehensive framework that leverages Digital Twins in conjunction with AI algorithms to detect and respond to intrusions in smart infrastructure networks. By simulating network behavior and employing machine learning models to analyze data patterns, the proposed system ensures robust security measures. One of the proposed techniques involves the use of a hybrid anomaly detection algorithm combining Autoencoders and Recurrent Neural Networks (RNN). The Autoencoder is utilized to learn a compact representation of the normal network behavior by reducing dimensionality and reconstructing inputs, while the RNN captures temporal dependencies and sequences within the network data. This hybrid model enhances the detection of both known and unknown intrusion patterns by focusing on deviations from the learned normal behavior. The integration of these AI components with Digital Twins enables a more nuanced and accurate understanding of network anomalies, thereby improving the system's ability to detect and respond to intrusions promptly. Experimental results demonstrate the efficacy of this approach in real-time threat detection, showcasing significant improvements in response times and accuracy. This integration not only enhances the resilience of smart infrastructure but also paves the way for future advancements in secure and intelligent network management. © 2024 IEEE.

关键词： Network intrusion

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