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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Sengottaiyan, N. Sathesh, m. Gurusamy, Ravikumar Brindha, m. Kalyanasundaram, P. Rajasekar, m. Alliance College of Engineering and Design Alliance University Computer Science Faculty of Science and Technology Karnataka Anekal562106 India Department of ECE Vel Tech Rangarajan Dr. Sagunthala Rand D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India M.P.Nachimuthu M.Jaganathan Engineering College Department of ECE Tamilnadu Erode India K S R Institute for Engineering and Technology Department of BME Tamilnadu Tiruchengode637215 India K S R Institute for Engineering and Technology Department of IT Tamilnadu Tiruchengode637215 India Institute of CSE SIMATS School of Engineering Saveetha Institute for Medical and Technical Sciences Tamilnadu Chennai602105 India

ISBN: (纸本)9798350340600

This research study's objective is to develop a unique, high gain triangle-shaped Sierpinski fractal antenna utilizing a FR4 epoxy substrate at frequencies between 1.0 GHz and 3.0 GHz, and to evaluate its performance in comparison to a square-shaped microstrip patch antenna (mPA). There are 2 groups in this study. Group 1 refers to the triangle-shaped Sierpinski fractal antenna with 26 samples and Group 2 refers to square shaped microstrip patch antenna with 26 samples. The confidence interval is 95%, the threshold is 0.05%, and the G Power value is 80%. The observed gain from the novel triangle-shaped Sierpinski fractal antenna has 27.3709 dB to 68.6663 dB and the gain of the square shaped mP A have -21.0381 dB to - 0.1775 dB. The novel triangle-shaped Sierpinski fractal antenna's best frequency for maximum gain was 1.5 GHz, with a significance level of around 0.000423 (p 0.05). In this work, it is observed that the gain of novel triangle-shaped Sierpinski fractal antenna is significantly better than square shaped microstrip patch antenna for implantable biomedical applications. © 2023 IEEE.

关键词： microstrip antennas

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Optimizing Food Image Classification Using Black Widow Algorithm and Deep Learning techniques

Optimizing Food Image Classification Using Black Widow Algor...

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International Conference on Advances in Information technology (ICAIT)

作者： Bhavana Jamalpur Sakthi Priya G S. Venkat Aarthi R S. Kavitha G. Charles Babu Department of Computer Science and Artificial Intelligence S R University Hanamkonda Telangana Computer science and engineering Ramco Institute of Technology Rajapalayam Department of ECE S.A. Engineering College Chennai Department of ECE R.M.D Engineering College Department of CSE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad Telangana

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Food image classification is a key application of computer vision and machine Learning (mL) devoted to mechanically classifying and labelling different food items depending on visual content seized in images. Using sophisticated Convolutional Neural Networks (CNNs) and deep learning (DL) models, this skill permits detection of different dishes, culinary creations, ingredients and raises many practical uses. From supporting nutritional choices and recipe recommendations to menu analysis and food quality control, food image detection updates procedures of food identification, providing valuable insights for users, food experts as well as food industry. Its high potential is to power detection and classification of food items provide benefits in upholding better eating habits, cooking improvement, and enhancing food creation and management procedures. This research proposed Food Image Classification Using Black Widow Algorithm and Deep Learning (FIC-BWODL) model. The developed FIC-BWODL method combines a multifaceted model starting with Contrast Limited Adaptive Histogram Equalization (CLAHE) for image preprocessing, certifying improved image quality and optimum feature extraction. Extraction of features executed by utilizing Capsule Networks (CapsNet) which is famous for their capability to capture difficult classified image patterns. Classification performed via Convolutional Autoencoders (CAE), exactly classifying food images. moreover, model controls Black Widow Optimization Algorithm (BWO) for hyperparameter tuning that enhances the act and supports it with exclusive features of a dataset. FIC-BWODL methodology provides an important leap in food image detection that provides a strong and exact solution for automated food detection.

关键词： Deep learning Image quality Adaptation models Visualization Quality control Feature extraction Classification algorithms

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Efficient Weather Detection Using Lightweight Convolutional Neural Networks suitable for Implementing and Deploying on Edge Devices

Efficient Weather Detection Using Lightweight Convolutional ...

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Integration of Emerging technologies for the Digital World (ICIETDW), International Conference on

作者： R Arumuga Arun K Saravanan J manju Suresh A Priyanka N Islabudeen m School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Information Technology R.M.D. Engineering College Chennai India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

Weather detection is vital in many fields, including disaster recovery, farming, transportation, and climate studies. making educated decisions and reducing potential dangers requires the quick and precise observation of weather patterns. As CNN-based deep learning models automatically train and extract significant features from raw image data, they often achieve higher accuracy in a variety of image classification tasks and exhibit good data generalization. In addition to the accurate detection, it should be lightweight to deploy to edge locations. To address this, we have developed a Smart lightweight deep-learning model to detect the weather accurately and it can be conveniently deployable in resource-constrained devices. This study uses several deep lightweight learning models, including mobileNetV2, mobileNetV3_Small, and NasNetmobile, where mobileNetV3_Small is the most efficient regarding weather detection and computational complexity reduction. The proposed approach utilized a multi-class Weather Dataset for Image Classification for building Smart lightweight deep-learning models. Besides the weather classification, the proposed approach also gives the confidence score for the predicted climate, which provides a numerical indicator to evaluate how likely the predicted climate is to occur.

关键词： Deep learning Accuracy Computational modeling Image edge detection Feature extraction Numerical models Convolutional neural networks Computational complexity meteorology Image classification

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IoT-Based Environmental Control System for Fish Farms with Sensor Integration and machine Learning Decision Support

arXiv

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arXiv 2023年

作者： Dhinakaran, D. Gopalakrishnan, S. manigandan, m.D. Anish, T.P. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering R.M.K College of Engineering and Technology Chennai India

In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT-based environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth;Support Vector machines (SVms) function as an early warning system, promptly detecting diseases and parasites in fish;Gradient Boosting machines (GBms) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity;Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming. © 2023, CC BY.

关键词： Support vector machines

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Enhancing Accessibility for Visually Impaired on Integrating Object, Distance and Face Detection

Enhancing Accessibility for Visually Impaired on Integrating...

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International Conference on I-SmAC (IoT in Social, mobile, Analytics and Cloud) (I-SmAC)

作者： C. Selvarathi R. Kannan muthumanickam K P. Pandiaraja T. S. Venkateswaran R. Arunraj Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Business Systems Saranathan College of Engineering Tiruchirapalli Tamilnadu India Department of Information Technology Kongunadu College of Engineering and Technology Trichy Tamil Nadu India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur Tamilnadu India Department of Information Technology K. Ramakrishnan college of Engineering Samayapuram Tamilnadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This research presents a novel assistive technology system designed to empower visually impaired individuals by providing enhanced object, facial, and distance detection capabilities. The system aims to replace traditional electronic travel aids with a more comprehensive and cost-effective solution. By combining object and facial recognition with distance detection, the system enables users to navigate their surroundings independently and confidently. Additionally, the system incorporates threat detection capabilities, alerting users to potential dangers and triggering SOS alerts to contacts or authorities. This innovative system offers a more reliable and accessible solution for visually impaired individuals, providing them with the freedom and independence to navigate their daily lives with greater confidence and security.

关键词： Performance evaluation Navigation Face recognition Blindness Object detection User experience Threat assessment Face detection Security Reliability

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Enabling Energy Efficient Communication between Entities Using 5G Assisted Wireless Sensor Networks

Enabling Energy Efficient Communication between Entities Usi...

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

作者： Vijayalakshmi, S. Charaan, R m Dilip Bala Sundara Ganapathy, N. Nivedha, C.S. Senthilkumar, C. Kumar, G V S manoj R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mba Tamil Nadu Chennai India Saveetha School of Engineering Simats University Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350315127

most of the sensor nodes in WSNs rely on power sources, which have a finite lifespan and can adversely influence the network's functionality, excellence and longevity because of the amount of energy they place on each node. Thus, several clustering approaches are proposed to enhance WSNs' energy efficiency. mobile communication technology has completely altered the way individuals interact with one another. Providers of internet services now face difficulties due to the exponential growth in the amount of users, the speed of data transfer, the need for more bandwidth, and the volume of data being sent. The expectation is that the contemporary communication system will fulfill all these needs. Next-generation communication technologies will prioritize the creation of user terminals that mix several flows from different technologies and enable access to those technologies. The researchers of this work have offered a comprehensive summary of the current state of wireless communication technologies and their potential for future improvement. This transmission is energy intensive. There has been a rise in the number of users necessitating constant connection due to the transition from 4G, which is transformed to 4G with LTE in wireless communication. Because of this, battery life on mobile devices is shortened. Therefore, a strategy should be created to reduce consumption of energy, which will lengthen the life of portable packs of batteries. A fifth-Generation (5G) technological advance, which is discussed in this article, is discussed, and it is both intelligent and interconnected with the rest of the world. In addition, this paper addresses the limitations of current wireless technology, the problems with current cellular networks, and the necessity for the next-generation communication technologies. In addition, 5G wireless communication has been the subject of extensive research and discussion. The major concern of this paper is to prove the efficiency of the propos

关键词： Energy efficiency

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PSO Pretrained ADP Based Control Scheme for Energy Storage System 3

PSO Pretrained ADP Based Control Scheme for Energy Storage S...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Kavitha, P. Parige, Leela Santi Kumar, D. Sateesh Gowrishankar, P. maranan, Ramya Vinoth, K. R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Kavaraipettai601206 India Bhavan's Vivekananda College of Science Humanities and Commerce Department of Mathematics Telangana Secunderabad500094 India College of Engineering Koneru Lakshmaiah Education Foundation Department of Engineering Mathematics Andhra Pradesh Guntur522302 India Kongu Engineering College Department of Electrical and Electronic Engineering Tamil Nadu Perundurai638060 India Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798350322842

The ADP-based controller is optimized using the PSO method so that it can respond appropriately to the changing dynamics of the system. The PSO-pre trained ADP-based control system is evaluated against the 'Proportional-Integral (PI)' and 'Fuzzy Logic Control (FLC)' strategies in terms of performance. The simulation results show that the PSO-pretrained ADP-based control scheme has the best tracking error, settling time, and steady-state error compared to the other control schemes. In terms of real-world applications, the suggested control approach may help save costs, increase efficiency, and facilitate the incorporation of renewable energy sources into existing power grids. Additional work might investigate the possibility of integrating PSO with other optimization methods to create more sophisticated and efficient control strategies for ESSs. © 2023 IEEE.

关键词： Energy storage

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Revolutionizing Cardiovascular Attack Prediction: A Comprehensive machine Learning Approach for Accurate and Timely Detection

Revolutionizing Cardiovascular Attack Prediction: A Comprehe...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： S. Durai D. Jaganathan Prabhu V R. Harini Dept. of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Artificial Intelligence Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Thiruvallur India Artificial Intelligence & Data Science vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Chennai India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Cardiac attacks are a leading cause of mortality worldwide, underscoring the critical need for timely and accurate healthcare detection. Predicting cardiovascular attacks is a formidable challenge in clinical data analysis. machine Learning (mL) proves to be a valuable tool for analyzing vast healthcare data. This innovative approach focuses on employing mL techniques to identify significant features, thereby improving the accuracy of cardiovascular attack prediction. A variety of characteristics and well-known classifiers, including Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector machine (SVm), Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR), are used to create the final prediction model. Through collaborative efforts, these algorithms pave the way for binary classification, effectively distinguishing between true and false outcomes. By analyzing their accuracies, this approach enables the possibilities for successful predictions.

关键词： Support vector machines Logistic regression Accuracy Cardiac arrest Nearest neighbor methods Prediction algorithms Bayes methods

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Sustainable management Practices for Enhancing Employee Recruitment and Retention

Sustainable Management Practices for Enhancing Employee Recr...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： D. Raja Jeyakarthika K A. Gowridurga Prem Kumar R J. Divya Nalini. m Dept of EEE Thiagarajar college of engineering India Dept of CSE Ramco Institute of Technology India Computer Science and Business Systems R.M.D Engineering college India Dept of CSE R.M.K College of Engg & Tech India Dept of CSD R.M.K.Engineering College India Dept of CSBS S.A Engineering College India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

The long-term organizational goals require sustainable practices in the management of organizations, especially with regards to hiring and retaining employees. To this effect, this paper aims at exploring how environmental, social and economic sustainability frameworks help in improving talent attraction and retention strategies in human resource. Recruitment management strategies including employer branding via CSR, technical hiring, and flex-work are assessed for their success. For retention, the contribution of ethical leadership, the impact of the organizational culture, and, the part that can be played by the encouragement of staff development and work-life balance in enhancing satisfaction and hence loyalty are discussed. Examples of best practices within each competency making up of the above organizational practices include case studies. It also presents further strategies for practice, emerging issues such as measurement of effects and realization difficulties, and proposes directions for future scholarly work.

关键词： Training Surveys Productivity Leadership Ethics Brand management market research Sustainable development Informatics Recruitment

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Retraction Notice: A machine Learning method for Detecting Sarcasm in Negative Sentiment Tweets using Hyperboles

Retraction Notice: A Machine Learning Method for Detecting S...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： V. Preetha P. Sathishkumar m. Janani B. manohari S. Jhansi Ida S. Suganyadevi Department of CSE Sri Ramakrishna Institute of Technology Coimbatore India Department of CSE Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Chennai India Department of CSE KPR Institute of Engineering and Technology Coimbatore India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Business Systems R.M.K Engineering Colleg Kavaraipettai India Department of ECE KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9781665453424

ISBN: (纸本)9781665453431

The use of sarcasm as an expression is a common literary device that involves someone purposefully expressing the opposite of what is being meant. The accurate identification of sarcasm in a text may boost other natural language processing activities, particularly ones involving sentiment analysis, and make it easier to comprehend the genuine intents of the speakers in the text. It is difficult to determine if someone is being sarcastic only based on their written words since sarcasm is a kind of implicitly expressing one's feelings and sarcastic speakers purposefully confound their audiences. Existing methods based on machine learning and deep learning obtained an insufficient level of performance when processing sarcastic material that included complicated expressions or required a certain set of prior knowledge to comprehend. The purpose of this research is to examine how sarcasm may be detected in tweets that have a negative mood and include hyperboles. For the purpose of sarcasm identification, the hashtags #Chinesevirus, #Kungflu, #COVID19, #Hantavirus, and #Coronavirus were included in the collection of 6,600 pre-processed negative sentiment tweets. The three well-known machine learning algorithms known as Support Vector machine, Random Forest, and Random Forest with Bagging were utilised in order to conduct an analysis of the five hyperbole characteristics known as interjection, intensifier, capital letter, punctuation mark, and elongated word. The suggested model with the extended term attained an accuracy of 78.74% and an F-score of 71%, respectively, despite the inclusion of hyperbolic words in the tweets included in an unbiased dataset. It was discovered that intensifier was the exaggeration with the highest level of significance (p. 0001). The results of the experiments and analyses c

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