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2021 International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021

作者： Gupta, Hritvik Patel, Mayank Geetanjali Institute of Technical Studies 5th Sem B. Tech Computer Science and Engineering Udaipur Rajasthan India

ISBN: (纸本)9781728195377

Document summarization is one such task of the natural language processing which deals with the long textual data to make its concise and fluent summaries that contains all of document relevant information. The branch of NLP that deals with it, is automatic text summarizer. Automatic text summarizer does the task of converting the long textual document into short fluent summaries. There are generally two ways of summarizing text using automatic text summarizer, first is using extractive text summarizer and another abstractive text summarizer. This paper has demonstrated an experiment in contrast with the extractive text summarizer for summarizing the text. On the other hand topic modelling is a NLP task that extracts the relevant topic from the textual document. One such method is Latent semantic Analysis (LSA) using truncated SVD which extracts all the relevant topics from the text. This paper has demonstrated the experiment in which the proposed research work will be summarizing the long textual document using LSA topic modelling along with TFIDF keyword extractor for each sentence in a text document and also using bERT encoder model for encoding the sentences from textual document in order to retrieve the positional embedding of topics word vectors. The algorithm proposed algorithm in this paper is able to achieve the score greater than that of text summarization using Latent Dirichlet Allocation (LDA) topic modelling. © 2021 IEEE.

关键词： Natural language processing systems

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A Statistical Analysis of Impact of COVID19 on the Global Economy and Stock Index Returns

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SN computer science 2021年第1期2卷 27页

作者： Verma, Parag Dumka, Ankur bhardwaj, Anuj Ashok, Alaknanda Kestwal, Mukesh Chandra Kumar, Praveen Computer Science and Engineering Uttaranchal University Dehradun India Computer Science and Engineering Women Institute of Technology (Govt.) Dehradun India Computer Science and Engineering Chandigarh University Punjab India Computer Science and Engineering College of Technology G.B. Pant University of Agriculture and Technology Pantnagar India Atal Incubation Center EMPI New Delhi India Yuvayana Tech and Craft (P) Ltd. Uttar Pradesh Bilaspur India

The outbreak of pandemic COVID-19 across the world has completely disrupted the political, social, economic, religious, and financial structures of the world. According to the data of April 22nd, 2020, more than 4.6 million people have been screened, in which the infection has made more than 2.7 million people positive, in which 182,740 people have died due to infection. More than 80 countries have closed their borders from transitioning countries, ordered businesses to close, instructed their populations to self-quarantine, and closed schools to an estimated 1.5 billion children. The world’s top ten economies such as the United States, China, Japan, Germany, United Kingdom, France, India, Italy, brazil, and Canada stand on the verge of complete collapse. In addition, stock markets around the world have been pounded, and tax revenue sources have fallen off a cliff. The epidemic due to infection is having a noticeable impact on global economic development. It is estimated that by now the virus could exceed global economic growth by more than 2.0% per month if the current situation persists. Global trade may also fall from 13 to 32% depending on the depth and extent of the global economic slowdown. The full impact will not be known until the effects of the epidemic occurred. This research analyses the impact of COVID-19 on the economic growth and stock market as well. The aim of this research is to present how well COVID-19 correlated with economic growth through gross domestic products (GDP). In addition, the research considers the top five other tax revenue sources like S&P500 (GPSC), Crude oil (CL = F), Gold (GC = F), Silver (SI = F), Natural Gas (NG = F), iShares 20 + Year Treasury bond (TLT), and correlate with the COVID-19. To fulfill the statistical analysis purpose this research uses publically available data from yahoo finance, IMF, and John Hopkins COVID-19 map with regression models that revealed a moderated positive correlation between them. The model was

关键词： COVID19 Crude oil revenue Gold revenue Gross domestic products (GDP) iShares 20 + year treasury bond revenue Natural gas revenue Regression model S&P500 revenue Silver revenue World economy growth

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A Secure Data Encryption Mechanism in Cloud Using Elliptic Curve Cryptography

A Secure Data Encryption Mechanism in Cloud Using Elliptic C...

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Intelligent and Innovative technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： I. Sudha Cecil Donald S. Navya G. Nithya Manivannan balamurugan S. Saravanan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences – SIMATS Chennai India Department of Computer Science CHRIST (Deemed to be University) Bengaluru India Department of Computer Science Engineering Raghu Engineering College Visakhapatnam India Department of CSE V. S. B College of Engineering Technical Campus Coimbatore India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu India

Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote server infrastructure known as the cloud. Cloud service providers, such Amazon, Rackspace, VMware, iCloud, Dropbox, Google's Application, and Microsoft Azure, provide customers the opportunity to create and deploy their own applications inside a cloud-based environment. These providers also grant users the ability to access and use these applications from any location worldwide. The subject of security poses significant challenges in contemporary times. The primary objective of cloud security is to establish a sense of confidence between cloud service providers and data owners inside the cloud environment. The cloud service provider is responsible for ensuring user data's security and integrity. Therefore, the use of several encryption techniques may effectively ensure cloud security. Data encryption is a commonly used procedure utilised to ensure the security of data. This study analyses the Elliptic Curve Cryptography method, focusing on its implementation in the context of encryption and digital signature processes. The objective is to enhance the security of cloud applications. Elliptic curve cryptography is a very effective and robust encryption system due to its ability to provide reduced key sizes, decreased CPU time requirements, and lower memory utilisation.

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Digital Cottage Hub for Empowering Artisans, Fostering Connectivity, and Promoting ‘Make in India’

Digital Cottage Hub for Empowering Artisans, Fostering Conne...

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International Conference on Advances in Information technology (ICAIT)

作者： Vani Hiremani Praveen Gujjar J Sumit Shinde Raghavendra M Devadas Anser Pasha C A Lubna Ambreen Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) (SIU) Lavale Pune Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India B. Tech Computer Science and Engineering ITM Skills University Navi Mumbai Department of Computer Science and Engineering Gitam School of Technology Gitam (Deemed-to-be University) Bengaluru India Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Aims to redefine the landscape of the cottage industry with an innovative social web application designed to simplify the exchange of handcrafted items. At its heart, the platform aims to bridge the gap between producers and customers by providing a thriving marketplace for one-of-a-kind, handcrafted things. With a focus on encouraging job possibilities, particularly for women, the application corresponds with the wider purpose of supporting “women empowerment” and contributing to national efforts such as “Make in India.”The system study highlights the existing holes in present systems, emphasizing the necessity for a complete platform tailored exclusively to the cottage sector. An architecture diagram and extensive use case overviews for customers, producers, and administrators are included in the system design. Implementation specifics include login, identity, purchasing, and administrative operations, among others. The initiative aims to promote not just economic activity in the cottage industry, but also social issues like as empowering women and inclusive employment. The dedication to ongoing development is obvious in the plans for the future, which include tightening security measures, notably for online financial transactions. Finally, the social web application aspires to build a dynamic and safe digital area for artisans, producers, and customers, therefore leaving a lasting impression on the cottage industry in the age of technology. The chatbot is implemented using the rasa open-source framework along with the digital platform. The results of the chatbot are discussed in detail in the results and discussion section.

关键词： Industries Economics Heart Employment Chatbots Remote working Security Information technology System analysis and design business

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IDOL: IoT based intrusion detection using Conv- biLSTM

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Australian Journal of Electrical and Electronics engineering 2025年

作者： C, Vijayalakshmi V, Padmajothi R, Suguna Devi Whitin, Priscilla S, Rajakumar Rajaretnam, Subraja Department of Computer Science and Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India

Internet of Things (IoT) networks have produced copious amounts of data that can be used to identify intrusions. Network Intrusion Detection Systems are crucial for spotting attacks before they cause any damage as part of a layered defence strategy. In this paper, a novel Intrusion Detection using Optimized deep Learning (IDOL) has been proposed to detect the intrusions like DDoS attacks, botnet attacks, Convergence attacks, and Ransomware attacks. Initially the IXIA PerfectStorm tool display data with malicious attack from the Network Environment and the data are converted in the data Pre-Processing method. Then the needed data are extracted in the feature extraction process by using boW technique. From the Extracted data the Feature selection process will select the essential data using Ant Colony Optimization. Finally, the Conv-biLSTM model will classify the attack into four classes namely DDoS attack, botnet attack, Convergence attack, and Ransomware attack. The proposed IDOL method achieves the highest accuracy rate of 98.93%. Using the UNSW-Nb15 and NSL-KDD datasets, the suggested model achieves an accuracy rate of 98.92% and 98.94% which is greater than the existing techniques such as ML-IMID, IDS-SLoEL, and POS-LightGRM respectively. © 2025 Engineers Australia.

关键词： Network intrusion

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VLSI implementation using fully connected neural networks for energy consumption over neurons

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SUSTAINAbLE ENERGY techNOLOGIES AND ASSESSMENTS 2022年第PartA期52卷

作者： Mehbodniya, Abolfazl Kumar, Ravi Mohanty, Sachi Nandan Geetha, A. bedi, Pradeep Tripathi, Rohit [a]Department of Electronics and Communications Engineering Kuwait College of Science and Technology (KCST) Doha Area 7th Ring Road Kuwait [b]Department of Electronics and Communication Engineering Jaypee University of Engineering and Technology Guna Madhya Pradesh India [c]Department of ECE Saveetha School of Engineering SIMATS Thadalam Chennai - 602105 Tamil Nadu India [d]Department of Computer Science & Engineering Vardhaman College of Engineering (Autonomous) Hyderabad India [e]B.Tech Department of ECE Indian Institute of Space Science and Technology Thiruvananthapuram 695547 Kerala India [f]Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu 603203 India

Increasingly, the capacity of artificial neural networks to identify and express top-level concepts in sets of data has become quite significant. Deep learning models like fully connected layer machine learning have demonstrated outstanding results in range of identification and verification applications. It's technology deployments suffered as from complexity and huge silicone areas with energy consumption. Memory accesses, the bulk from which happens in fully connected systems, comprise the energy usage of machine learning. It has many of the depths of neural network. Throughout this work, researchers suggest densely connected networks by demonstrating that there is indeed a drop in the number of links inside fully connected systems of up to 90% enhancing the quality of three popular sets of data. To decrease the memory needs have suggested low-connected grid networks, researchers present perhaps an effective hardware implementation based on the linear response registers. In comparison to the traditional design of fully connected layer neural network models, the suggested design could save up to 90 percent of storage. Findings of execution further reveal that artificial neurons of suggested weak network connections have a power consumption decrease up to 84 percent compared to one single atom of fully connected layer machine learning.

关键词： Neural Network Deep learning Energy consumption VLSI

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Monitoring state of mind using Natural Language Processing

Monitoring state of mind using Natural Language Processing

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International Conference on Advances in Computing, Communication and Control (ICAC3)

作者： Vemula Sai Saketh Srinivas Khatravath Yashvanth Kumar Guntupalli B.Tech Computer Science PES University Bangalore India M.Tech Electronics and Electrical Communication IIT Kharagpur Kharagpur India

ISBN: (纸本)9781665426350

State of mind is essential in every person’s life to keep their life stable, calm and peaceful. The key factors that contribute towards them are mood, emotion and thoughts. Monitoring them is key to a person's overall development and allows family and friends to support them to a greater extent. One aspect of getting a report on this is using the words being typed by the person on their computer or mobile, browser history, songs and movies played. This paper proposes a sentiment analysis system where information about mood and thoughts are conveyed by means of natural language processing. These logs captured during each time session are classified to gain insights about the person. This reduces the likelihood of any drastic decisions taken by the person and provides a better means of conveying the information and maintaining communication between them and their relations.

关键词： Sentiment analysis Mood Motion pictures browsers History Monitoring

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Challenges in Cyber Physical Social Systems and Internet of Things

Challenges in Cyber Physical Social Systems and Internet of ...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： S. Revathi J. Raja M Mohanraj K. Malathi balasubbareddy Mallala R.G. Vidhya Department of Computer Science Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Business Systems Sri Eshwar college of engineering Coimbatore India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of ECE HKBK College of Engineering Bangalore India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

This research analyses the complex dynamics of Cyber-Physical-Social Systems (CPSS), encompassing cyber-physical systems, cybersecurity, the Internet of Things (IoT), and social media. by exploring the interactions among these elements, this study analyses the vulnerabilities, risks, and potential benefits within the CPSS framework. This research study analyses the evolving landscape of cybersecurity by considering various aspects such as data integrity, privacy in the IoT era, and the influence of social media on user behaviour and influence dynamics. The study also introduces innovative strategies to enhance CPSS resilience, including methods to mitigate cyber threats and enhance overall system integrity. Additionally, we investigate how social media shapes CPSS behaviours, offering valuable insights into influence dynamics. This research advances the comprehensive understanding of CPSS to support future studies and advancements in the protection and improvement of interconnected systems.

关键词： Data privacy Social networking (online) Shape Media Real-time systems Internet of Things Protection Predictive analytics Monitoring Resilience

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Particle Swarm Optimization for Efficient brain Tumor Classification Using InceptionV3 Deep Learning Model

Particle Swarm Optimization for Efficient Brain Tumor Classi...

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International Conference on Communication and Electronics Systems (ICCES)

作者： P. Akhila G. Prabaharan K. Pandiiyan S. L. Swarna Hussain. A U. Sakthivelu Department of Artificial Intelligence and Machine Learning B V Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science with Artificial Intelligence SRM Arts and Science College Chengalpattu India Department of Computer Science Science and Engineering Sona College of Technology Salem Tamil Nadu India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

This research proposes a novel framework for brain tumor classification utilizing a combination of deep learning and optimization techniques. The framework employs an autoencoder network for image preprocessing, followed by SegNet for accurate tumor segmentation. The core classification is performed using the InceptionV3 model, further optimized through Particle Swarm Optimization (PSO) to enhance predictive performance. The proposed system achieved a classification accuracy of 98.75% on a dataset of 7,023 MRI images, demonstrating its effectiveness in accurately classifying brain tumors. This research contributes to the advancement of medical image analysis and provides a valuable tool for supporting clinicians in the early diagnosis and treatment of brain tumors.

关键词： Deep learning Accuracy brain tumors Predictive models brain modeling Planning Reliability Particle swarm optimization Optimization Residual neural networks

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Energy Proficient and Dependable Cluster Routing in Wireless Sensor Network

Energy Proficient and Dependable Cluster Routing in Wireless...

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Intelligent and Innovative technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： S. Hemavathi Jeya Ganesan J G Geetha Kishore Kumar K R. Kohila Department of Computer Science Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Computer Science Engineering B V Raju Institute of Technology Hyderabad Telangana India Department of Computer Science Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Engineering (Cyber Security) Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

Huge energy utilization information of structures is created with the improving information technology. The energy utilization information is processed by an energy depletion observing system through the wireless sensor network (WSN). Conventional security approaches can face external attacks. Nevertheless, they cannot proficiently solve interior attacks triggered by the captured nodes. Energy-efficient and Dependable Cluster Routing (EPDC) in WSN. This approach aims to reduce energy expenditure and enhance reliable routing in WSNs. The highest energy and highest available bandwidth are selected as a cluster head (CH). In every cluster, the selected CH has received the observed information from the cluster member and then forwarded it to the bS through a relay node. The forwarder relay node is selected based on the average energy faith and transmission faith during data transmission. The whole faith is greater than the average value that node is selected as a data forwarder from cluster to bS in the WSN. Furthermore, the Path Optimization Algorithm (POA) is used to detect the obstacle and build the obstacle-aware route efficiently. The simulation results explain why the proposed method has better residual energy. In addition, it minimized both the network delay and the packet loss rate in the WSN.

关键词： Wireless sensor networks Simulation Packet loss bandwidth Routing Energy efficiency Delays

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