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8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Das, Priyanka Ray, Sangram Dasgupta, Mou Govil, Mahesh Chandra Department of Computer Science and Engineering National Institute of Technology Sikkim Sikkim Ravangla737139 India Department of Computer Application National Institute of Technology Raipur Chhattisgarh Raipur492010 India

ISBN: (纸本)9789819757855

Internet of Things (IoT) plays a crucial role in automating systems, offering effectual and reliable services with minimal human intervention. To be precise, IoT-enabled smart agriculture is a vital factor in the modern digital world which ensures to provide various functionalities such as—water level monitoring, temperature monitoring, moisture sensing, and plant growth tracking. These functionalities of smart agriculture significantly simplify the work of farmers. But on the contrary, several security and privacy-related concerns were identified such as critical agriculture field data storage and management. Therefore, the evolution of integration of the IoT and blockchain (BIoT) came into the limelight. BIoT-based smart agriculture can enhance efficiency, sustainability, etc., leading to increased productivity, improved resource management, and secure data storing management in a decentralized manner. In this paper, we propose an architecture for a smart agriculture ecosystem based on the fusion of both IoT and blockchain. Primarily, our proposed architecture consists of three stages, PEGASIS-based chain construction phase, mutual authentication and key agreement phase, and blockchain-based data storage in base-station phase. Finally, this paper highlights on security analysis of our proposed architecture and found that it is well-secured against broadly known security attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Applying Keyword Extraction Techniques on Customer's Feedback of a Software Product to Accelerate Agile Based Development 7

Applying Keyword Extraction Techniques on Customer's Feedbac...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Bhatia, Nidhi Singh, Jitendra Vandana Srm Institute of Science & Technology Department of Computer Science and Engineering Delhi NCR Campus UttarPradesh Ghaziabad India Chaudhary Charan Singh University Scriet Department of Computer Application UttarPradesh Meerut India

ISBN: (纸本)9798350350067

Keywords or key-phrase extraction is an important task in almost all the scenarios where natural language processing is required. Present paper analyses various keyword extraction techniques for improving a softwareproduct, taking into consideration an agile based development approach. Feedback, reviews given by customers are always written in an unrestricted natural language. If we are able to extract keywords from this feedback, at predevelopment stage, quality of a software can be improved, and it would be saving efforts and time of developers. Keywords provide a quick review about sentiments, prioritize customer needs, enhance user experience, provide marketing insights and also analyse the trends. Change-request submitted in last stages of development process require a lot of reworks. Hence this work can be beneficial in agile based development process to develop an end product which incorporates suggestions given by users. © 2024 IEEE.

关键词： Sales

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Study of Adaptive Nonlinear Signal Processing Structures for Underwater Acoustic Systems

Study of Adaptive Nonlinear Signal Processing Structures for...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kaushik, Neeraj Prabhu, A. Kaushik, Harshita College of Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

This takes a look at and explores the use of adaptive nonlinear sign processing structures for underwater acoustic systems. The focus of the research is on how such structures can improve the accuracy of acoustic alerts underwater by adapting to various environmental conditions. Particularly, the research will analyze various acoustic sign distortion additives caused by nonlinear distortion, as well as the effectiveness of diverse adaptive nonlinear signal processing systems for reducing these effects. Sign processing strategies will include both adaptive thresholding and adaptive nonlinear time-frequency transformations. The studies will examine the overall performance of these systems in phrases of signal fidelity, in addition to the impact on cognitive complexity. The effects of the studies will inform capability enhancements and packages of nonlinear sign processing systems for underwater acoustic structures. © 2024 IEEE.

关键词： Performance Research Thresholding Transformations Underwater

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Understanding Social Networks Using Machine Learning Sentimental Analysis Algorithms

Understanding Social Networks Using Machine Learning Sentime...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Goswami, Garima Varun Somanna, M.N. Inzimam College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

Social networks are dynamic and complex structures that depend closely on human interplay. Machine learning and nostalgic evaluation algorithms can be used to gain insights into the shape and dynamics of these networks. Through those techniques, patterns of connections between people may be exposed, in addition to sentiment associated with subjects or entities. This know-how can help understand higher social networks over more than a few subjects, including fitness, politics, and the stock market. Moreover, such techniques may be implemented to leverage community effects to optimize the overall performance of advertising and marketing techniques. © 2024 IEEE.

关键词： advertising associated evaluation nostalgic techniques

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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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Optimizing Stroke Diagnosis: application of Machine Learning Algorithms for Early Detection

Optimizing Stroke Diagnosis: Application of Machine Learning...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sharma, Chetan Anooja, A. Kishore, Jaydeep Bhardwaj, Vivek UpGrad Education Private Limited Bangalore India School of Computer Application Jecrc University Rajasthan Jaipur India School of Computer Science and Engineering Manipal University Jaipur Jaipur India

ISBN: (纸本)9798331509828

Being a top cause of mortality and serious disability, stroke demands urgent diagnosis and impactful prevention. Current innovations in machine learning (ML) show a great capacity for predicting the likelihood of a stroke. This study reports on applying ML algorithms to improve the timely and early identification of stroke, leading to more effective interventions and lowering morbidity and mortality numbers. This work employs a complete dataset that integrates demographic information, medical history, data on lifestyle, and physical measurements. engineering techniques of a high level were utilized in pre-processing the data to reduce the assumptions that lay underneath. This research applied five machine learning models for the prompt identification of stroke: Decision Tree, Support Vector Machine (SVM), Gradient Boosting, K-Nearest Neighbours (KNN), and XG Boost. The number of datasets used for training was 80%, and 20% went to testing. The accuracy of the Gradient Boosting model stood at 96%, outperforming XG Boost at 92% and Decision Tree at 91%. The model with Support Vector Machines (SVM) gave 84% accuracy, but all three models illustrated high precision and recall. These results indicate that machine learning models could significantly augment stroke prediction and enhance clinical decision processes. © 2025 IEEE.

关键词： Support vector machines

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IOT-based Smart Ground water Recharge System 1

IOT-based Smart Ground water Recharge System

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Uniyal, Manas Bhatt, Chandradeep Mohd, Noor Kumar, Devendra Graphic Era Hill University Computer Science Department Dehradun India Abes Engineering College Computer Application Department Ghaziabad India

ISBN: (纸本)9798350338027

Water is the most important commodity for the existence of life. In India, the main sources of water are rainwater(monsoon) that fills the rivers with water and underground water. However, we have seen a steady decline in underground water levels across India at an alarming rate that affects the agriculture and daily water needs of the citizens. This paper suggests a smart ground water recharge system that automatically recharges the ground water after rain falls. The system uses smart IOT devices, solar panels, and a filter to filter out unwanted materials that come along the rainwater. In this paper a fully automatic rainwater harvesting is introduced that collects rainwater and filters out dirt and unwanted materials(trash) that comes along the rain. It also senses the acidic content of rain and filters it out when needed. Later on, the filtered water can be directly recharged in ground, or it can also be collected in artificial pools for irrigation purposes. Polluted water can be very harmful for our environment and food cycle. © 2023 IEEE.

关键词： Aquifers

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A Survey of Dynamic Exploration Symmetric Encryption with Backward & Forward Secrecy 3

A Survey of Dynamic Exploration Symmetric Encryption with Ba...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Singh, Satendra Bhagat, Samriti Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9798350399264

A sophisticated type of symmetric encryption is called Multiuser Dynamic Searchable Symmetric Encryption. It expands the capabilities of conventional symmetric encryption to enable actions such as adding and removing encrypted data as well as the ability for a verified set of information users to access their preferred encrypted data within the dynamic database. The latter can use using erased data and search results to comprehend the content, while the former allows attackers to discover by performing file injection attacks, keywords from the injected data were exposed. can do. To our knowledge, the available literature has not completely addressed these privacy problems for M-DSSE. In light of this, our attention is directed toward multiuser dynamic searchable symmetric encryption that satisfies the requirements for forwarding and backward security. It uses Inverted indexes are created and updated using random number generator Functions (RNG) and Authenticator primitives search tokens to suggest specific solutions. In the accidental model, the suggested approach is shown to be secure. Additionally, the performance study demonstrates that the suggested approach provides increased security assurances at a fair cost to efficiency. © 2023 IEEE.

关键词： Cryptography

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An Edge Computing Algorithm Based on Multi-Level Star Sensor Cloud

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computer Modeling in engineering & sciences 2023年第8期136卷 1643-1659页

作者： Siyu Ren Shi Qiu Keyang Cheng The Open University of Chengdu Major in Computer ApplicationChengdu610000China Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences.Xi’an710119China School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212013China

Star sensors are an important means of autonomous navigation and access to space information for *** have been widely deployed in the aerospace *** satisfy the requirements for high resolution,timeliness,and confidentiality of star images,we propose an edge computing algorithm based on the star sensor *** sensors cooperate with each other to forma sensor cloud,which in turn extends the performance of a single *** research on the data obtained by the star sensor has very important research and application ***,a star point extraction model is proposed based on the fuzzy set model by analyzing the star image composition,which can reduce the amount of data ***,a mappingmodel between content and space is constructed to achieve low-rank image representation and efficient ***,the data collected by the wireless sensor is delivered to the edge server,and a differentmethod is used to achieve privacy *** a small amount of core data is stored in edge servers and local servers,and other data is transmitted to the *** show that the proposed algorithm can effectively reduce the cost of communication and storage,and has strong privacy.

关键词： Star-sensing sensor cloud fuzzy set edge computing mapping

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Comparative Evaluation of Machine Learning Algorithms for Detecting Credit Card Fraud 8

Comparative Evaluation of Machine Learning Algorithms for De...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Prashanth, Banagani Gangadhar, Suddapalli Bhardwaj, Vivek Reddy, Kovvuru Diwakar Reddy, Puli Shiva Mehra, Manik Lovely Professional University Department of Computer Science and Engineering Phagwara India Manipal University Jaipur Department of Computer Science and Engineering Jaipur India Lovely Professional University School of Computer Application Phagwara India

ISBN: (纸本)9798350396638

Detecting credit card fraud is an extremely important issue in the financial sector, as it results in significant financial losses and negatively impacts customer trust. The main motive of CCFD is to develop methods to identify fraudulent transactions accurately and efficiently. This research typically involves the use of statistical and machine-learning algorithms to analyze large amounts of transaction data. Utilizing machine learning is a potential solution for addressing credit card fraud. Studies suggest that machine learning techniques can help overcome the challenges of identifying fraudulent transactions with high detection rates, both directly and indirectly. While supervised ML algorithm and unsupervised ML algorithms have been proposed, the limitations of each approach underscore the need for hybrid methods. From the results, it is evident that Logistic Regression has the highest accuracy of 94.86%, highest recall value of 98.97% and highest F1-score of 95.09%. © 2023 IEEE.

关键词： Feature extraction

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