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An Efficient Detection and Classification of Sarcastic by Using CNN Model 1

World Conference on Information Systems for Business Management, ISBM 2022

作者： Parveen, Summia Saradha, S. Krishnaraj, N. Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore641202 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (数字)9789811974472

ISBN: (纸本)9789811974465

There have been a lot of new ways to figure out how people feel about things on Twitter and other microblogging platforms in the last few years. Using both types of semantics, experiments are carried out to see how they perform in three popular tasks of sentiment investigation on Twitter: sentiment analysis at the level of entities, sentiment analysis at the level of tweets, and context-sensitive sentiment lexicon adaptation. These three tasks include as follows: Find and categorize sarcastic texts were the objectives of this module. After the initial sentiment classification, texts that were initially classified as positive will be classified as moreover positive sarcastic or true positive. Texts classified as negative by the primary sentiment classification module can be confidential in two ways. Negative sarcasm and true negativity are two different types of negativity. In this research, a CNN model integrates the implicit as well as explicit representations of short text for sarcastic classification and is abbreviated as CNN-SC model. The proposed classification model incorporates three main processes, namely feature engineering, FS, and classification. Using data from Amazon and Twitter, it was tested to see if it worked. Detailed information on the dataset, experiments, and findings is presented in the section on experimental setup and results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Neural Network-Based Automated Soil Salinity Mapping and Remediation Using Wireless Sensor and Cloud Computing 10

Neural Network-Based Automated Soil Salinity Mapping and Rem...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Shadaksharappa, B. Sriram, S. Swathy, R. Mishra, Nilamadhab Meenakshi, R. Suganya, J. Sri Sairam College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chenagalpattu Chennai Tamil Nadu Kattankulathur India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This paper uses neural networks, wireless sensor networks, and cloud computing to automate soil salinity mapping and treatment. Soil salinity affects agricultural output, but conventional techniques of monitoring and managing it are laborious. This system collects real-time soil salinity data from a specific agricultural region using a wireless sensor network. These sensor nodes provide data to a cloud computing platform for automatic soil salinity mapping using a neural network model. The neural network is trained on a large soil property and salinity dataset to accurately predict field salinity distribution. Farmers and land managers may make quick decisions using the cloud-based data storage, processing, and analysis solution. The suggested system incorporates remediation options to inform sustainable soil management. Through field testing, the methodology proves its accuracy and efficiency above older approaches. Precision agriculture producers may monitor soil salinity in real time and take targeted repair actions using the described method. Due to rising agricultural needs and environmental concerns, neural networks, wireless sensor networks, and cloud computing provide a solid foundation for sustainable and technology-driven soil management. © 2024 IEEE.

关键词： Data integration

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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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Prediction of Breast Cancer Risk Using Microarrays and Deep Learning 1

Prediction of Breast Cancer Risk Using Microarrays and Deep ...

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1st IEEE International Conference on networking and Communications, ICNWC 2023

作者： Shanmu, R. Madhubala Brundha, P. Swaminathan, G. Aravind Merlin, R. Tino Hemamalini, V. Ramnath, M. Francis Xavier Engineering College Department of CSE Tamil Nadu Tirunelveli India School of Computing SRMIST KTR Department of Networking and Communications Tamil Nadu Chennai India Thamirabharani Engineering College Department of Computer Science and Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350336009

More than 1.15 million new instances of breast cancer are identified each year. In the clinic, only a few reliable prognostic and predictive indicators are utilized to make decisions about the treatment of breast cancer patients. The mortality rate of breast cancer patients may be lowered and their survival time extended by early identification. Analysis and processing of Microarray images, the principal test used for screening and early diagnosis, are the keys to improving breast cancer prognosis and are at the heart of this study. The Fuzzy C-means (FCM) approach is used for image segmentation in microarray for the detection of breast cancer. After features are retrieved from the segmented areas and the system is fully trained, the efficient classifier is used to assign microarrays to their respective classes. Techniques such as Multi-level Discrete Wavelet Transform, Principal Component Analysis (PCA), and Gray-level Co-occurrence Matrix (GLCM) are used to extract texture information. Differentiating masses and microarray image calcifications from the surrounding tissue is achieved with the aid of morphological operators and the classification of these features is handled by the Deep Convolution Neural Network (DCNN) algorithm. In a microarray, the tumor's borders are highlighted and exhibited to the doctor, who may then assess the extent of the growth. © 2023 IEEE.

关键词： Textures

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Privacy Preserving Quantum Search Mechanism Using Grover's Algorithm 1

Privacy Preserving Quantum Search Mechanism Using Grover's A...

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1st International Conference on Quantum Communications, networking, and Computing, QCNC 2024

作者： Ju, Keyi Qin, Xiaoqi Zhong, Hui Zhang, Xinyue Pan, Miao Liu, Baoling State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Kennesaw State University Department of Computer Science MariettaGA30060 United States

ISBN: (纸本)9798350366778

Quantum computing changes the way of solving complex problems and handling vast datasets. The introduction of quantum search algorithms, notably Grover's algorithm, addresses the inefficiencies of classical search methods in handling vast datasets by offering substantial speed improvements. However, an inherent aspect of quantum searches is their failure probability. This paper aims to explore the relationship between the failure probability in Grover's algorithm and the privacy guarantees of differential privacy (DP). We further investigate the impact of several quantum circuit parameters, including the number of shots, the number of solutions within the dataset, and circuit noise levels, on this failure probability. Our analysis demonstrates the indirect connection between these quantum circuit elements and the privacy guarantees of DP. Through Monte Carlo simulations, we offer an in-depth analysis that paves the way for secure and faster data querying methods. © 2024 IEEE.

关键词： Differential privacy

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An Intelligent Tender Management System using Block Chain and IPFS

An Intelligent Tender Management System using Block Chain an...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Sai Tarun Kumar Reddy, E. Sathvik, Madarapu Rajaram, V. Rao, Chilka Prithvi Srmist School of Computing Department of Networking and Communications Kattankulathur Campus Chennai India Vellore Institute of Technology School of Computer Science and Engineering Andhra Pradesh Amaravati India

ISBN: (纸本)9798350333602

Tenders must be included in the procurement process for companies and government organizations. The primary purpose of these bids is to get the products and services necessary to effectively complete operations. Poor management of these bids, on the other hand, might have serious financial consequences. Contractor favoritism, inadequate record-keeping systems, a lack of transparency, hacking, data manipulation, and other obstacles may be among these issues. Improper bid and contract management may cause substantial financial loses and harm to the organisation's reputation. When deficient record-keeping practices result in the loss of critical information, it can be difficult to trace the progress of tenders and contracts and evaluate performance. Additionally, the possibility of hacking and data tampering can lead to the theft of sensitive information, resulting in financial losses and reputational harm. To solve these issues and eliminate the dangers connected with them, this study has used secure blockchain technology as well as encryption to ensure the safety and security of transactions during the tendering process. Blockchain technology is a secure and decentralized ledger system that allows for secure and tamper-proof transaction recording. Encryption, on the other hand, is a means of transforming plain text into unreadable code that requires a decryption key to decrypt. Tender specifications, bid submissions, business profiles, historical performance records, approval authority details, and rejection information are all protected by its cutting-edge technology. The proposed method assures a completely transparent and safe tendering process by utilizing this technology, lowering the risks associated with tender and contract mismanagement. © 2023 IEEE.

关键词： Smart contract

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IoT Based Soil Moisture Monitoring and Nutrients Suggestion Based on the Soil PH Value 4

IoT Based Soil Moisture Monitoring and Nutrients Suggestion ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Prasanna, R. Saravanan, P. Jenath, M. Saravanan, G. Prathaban, Banu Priya Rajesh Kanna, R. Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur Chennai India Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computer and Science Engineering kattankulathur Campus Chennai India

ISBN: (纸本)9798331508845

The Internet of Things (IoT) has a massive influence on machine-to-machine (M2M) and machine-to-human (M2H) technologies, which in turn have caused major changes in society. Country's ability to provide its citizens with food is largely dependent on its agriculture industry. Farmers continue to lack access to technological innovations and advancements in their industry, which causes them to suffer financial losses so we need soil monitoring system. Many chemical and physical possessions of the soil, such as temperature, moisture, and concentrations of nitrogen, phosphorus, and potassium, have a significant impact on crop yield. In order to preserve soil fertility and productivity, farmers can benefit from the increased awareness of continuous cropping and minimum fertilizer use provided by technology and digital gadgets. In order to help farmers in the future with affordable and equable automated irrigation system, a budget friendly prototype for quantifying soil moisture, temperature, pH level and we plan to measure the soil NPK values using sensors. Our proposed work suggests the farmers what nutrient to be added to get better yield using a microcontroller, sensor in conjunction with GSM technology and IoT for continuous analyses of the soil. It will support the efficient cultivation of the land by encouraging moderate pesticide application and soil fertilization. © 2024 IEEE.

关键词： Budget control

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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Goal-Oriented Communication, Estimation, and Control Over Bidirectional Wireless Links

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IEEE Transactions on Communications 2025年第5期73卷 3031-3045页

作者： Cao, Jie Kurniawan, Ernest Boonkajay, Amnart Pappas, Nikolaos Sun, Sumei Popovski, Petar School of Electronic and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Institute of Infocomm Research Agency for Science Technology and Research Fusionopolis 138632 Singapore Linköping University Department of Computer and Information Science Linköping58183 Sweden Aalborg University Department of Electronic Systems Aalborg9220 Denmark

We consider a wireless networked control system (WNCS) with imperfect bidirectional links for real-time applications such as smart grids. To maintain the stability of WNCS, captured by the probability that plant state violates preset values, at minimal cost, heterogeneous physical processes are monitored by multiple sensors. This status information, such as dynamic plant state and Markov Process-based context information, is then received/estimated by the controller for remote control. However, scheduling multiple sensors and designing the controller with limited resources is challenging due to their coupling, delay, and transmission loss. We formulate a Constrained Markov Decision Problem (CMDP) to minimize violation probability with cost constraints. We reveal the relationship between the goal and different updating actions by analyzing the significance of information that incorporates goal-related usefulness and contextual importance. Subsequently, a goal-oriented deterministic scheduling policy is proposed. Two sensing-assisted control strategies and a control-aware estimation policy are proposed to improve the violation probability-cost tradeoff, integrated with the scheduling policy to form a goal-oriented co-design framework. Additionally, we explore retransmission in downlink transmission and qualitatively analyze its preference scenario. Simulation results demonstrate that the proposed goal-oriented co-design policy outperforms previous work in simultaneously reducing violation probability and cost. © 1972-2012 IEEE.

关键词： Markov processes

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IoT-Based Smart Irrigation System in Aquaponics Using Ensemble Machine Learning 1

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Singh, Aishani Bajaj, Dhruv Safa, M. Arulmurugan, A. John, A. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan

ISBN: (数字)9789819984510

ISBN: (纸本)9789819984503

Aquaponics is a sustainable farming method that combines aquaculture and hydroponics to grow plants and fish in a closed-loop system. In this research paper, an irrigation system based on aquaponics is proposed, which uses real-time sensor data from the fish tank and crop soil to improve the efficiency of the system. The system is designed to make informed decisions about crop irrigation needs by visualizing the data for analytics. The study compares the accuracy of three classification algorithms, KNN, Naive Bayes, and ANN, to decide when to irrigate the soil based on real-time sensor data. The proposed irrigation system includes two sets of sensors, one for the fish tank and the other for the crop soil, which is processed by an Arduino board and sent to Adafruit’s cloud platform for visualization and analytics. This cloud-based platform allows easy access to real-time data, enabling efficient monitoring and control of the irrigation system. Additionally, the study visualizes the results obtained from using regular water and lake water in the aquaponics system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Hydroponics

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