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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Brindha, K. Maruthi, R. Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350397253

The act of obscuring the existence of data in images is commonly referred to as steganography. Contrary to cryptography, there is no data scrambling. The original and the cover data carrying the hidden confidential data are so similar that human perceptions cannot differentiate. Steganography is an ancient and efficient way to hide and communicate confidential information. The effectiveness is measurable using various parameters. The steganography analysis method examines the captured data with various parameters in an attempt to identify the steganography. This research study presents an appropriate way to perform image steganography using a combination of techniques like the least significant bit, discrete cosine transforms, and a combined model of convolutional neural network (CNN)-Convolutional Autoencoder with Encoder-Decoder based Latent Features (CAEDLF). LSB-based steganography is an in-demand technique for concealing confidential information in the LSBs of pixel values without creating any noticeable deformity. DCT helps provide robustness by changing the structure of the secret image. The Convolution Neural Network (CNN) based convolutional autoencoder with encoder-decoder-based latent features (CAEDLF) autoencoder is used for optimizing steganography. The proposed steganography shows the best results in terms of robustness, security, and imperceptibility capabilities and also helps optimize steganography analysis. © 2023 IEEE.

关键词： Steganography

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Enhancing Educational Outcomes Through Natural Language Processing Using Sentiment Analysis to Understand Course Feedback 13

Enhancing Educational Outcomes Through Natural Language Proc...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Deepanchakkaravarthy, K. Umarani, S. Srm Institute of Science and Technology Department of Computer Science Ramapuram Chennai India A.M. Jain College Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Applications Ramapuram Chennai India

ISBN: (纸本)9798350380583

For robots, artificial intelligence includes a subset called natural language processing, which employs text analysis to analyse details and improve decision-making capacity by means of text interpretation based on criteria. Sentiment analysis seeks the nuances among a set of words. It is absolutely necessary for the evolution of natural language processing applications. It is often used to measure public or customer opinions. Setting sentiment analysis might be used to assess comments on courses providing schools with an understanding of how students see their learning environments. By use of data analysis, educators may identify areas of challenge for their respective pupils. Expect trends in performance that will then guide policies to improve learning capacity progressively over time. Furthermore, comments reveal how to improve teaching strategies. Customise strategies to better meet the personal requirements of students. By extracting information from the text using machine learning techniques such as Aspect-Based Sentiment Analysis and Rule-based Approaches, sentiment analysis sorts emotions as either positive or negative. Schools that use language processing tools and examine student emotions may allow them to better understand their feelings and foster a flexible classroom environment. © 2024 IEEE.

关键词： Students

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Evaluation and Enhancement of Standard Classifier Performance by Resolving Class Imbalance Issue Using Smote-Variants Over Multiple Medical Datasets

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SN computer science 2025年第3期6卷 1-30页

作者： Kumar, Vinod Kumar, Ravi Kant Singh, Sunil Kumar School of Computer Applications and Information Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering SRM University AP Andhra Pradesh Guntur India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India karnataka Bengaluru India

In the era of machine learning we are solving the classification problems by training the labeled classes. But sometimes due to insufficient data in some of the training classes, the system training is inadequate for these minority classes. In this case the output for the classes obtained from the less amount of trained data are miserably inappropriate and biased towards the classes having more data. This problem is known as a class imbalance problem. In such cases, standard classifiers tend to be overpowered by the expansive classes and disregard the little ones. As a result, the performance of machine learning and the deep learning algorithms are also reducing and sometimes highly unacceptable too, mainly if it is related to crucial data like medical and health related. Though various researchers provided some methods to solve this problem but mostly they are problem specific and suitable with the specific classifier only. To find a generalized and effective solution to this problem, we have applied various smote variants for solving the imbalanced factors in dataset and finally improved the performance of the various machine learning and deep learning algorithms. We have experimented and analyzed the effects of SMOTE variants on various machine learning techniques over six standard medical datasets. We have found that SMOTE variants are very effective, and they improve the standard performance measures (Accuracy, Precision, Recall and F1-Score). Additionally, based on our research, it is feasible to determine which smote variation works best with machine learning methods and datasets. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalanced Classifiers Machine learning Medical datasets Smote variants

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Replica controlled sensor enabled architecture for management of electronic health records

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International Journal of Information Technology (Singapore) 2023年第7期15卷 3643-3653页

作者： Franklin, I. Benjamin Bhuvaneswari, R. Vasanthi, V. Jerald, M. Paul Arokiadass PG Department of Computer Applications St. Joseph’s College of Arts and Science (Autonomous) Cuddalore India Department of Computer Science Periyar Arts College Cuddalore India Department of Computer Science Dharmapuram Gnanambigai Govt. Arts College for Women Mayiladuthurai India

Electronic health records (EHR) are awfully substantial for patients in managing their records in a distributed environment and endorse their status of rising or fall of health on a time-to-time basis. The replicas can be prevented through logic programming which cannot be managed on a time-to-time basis. The major objective of this paper is to design and propose a replica controlled and body area sensor enabled architecture model that would be capable of managing the EHR records based on life-threatening problems like irregular heartbeat, arrhythmia, biomedical problems, bio fabrication and organ-on-a-chip process. The proposed model assisted in managing the biomedical problems related to EHR data using a sensor-enabled environment where the patient can be tracked continuously. Also, the model conveys a distributed system to control the replica through consistent monitoring of redundant data that comes through the sensor device refresh process in a distributed system. The scope of the model included implementation in health centers, hospitals where data segregation and analysis on a time-to-time basis is recommended. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Biomedical problems Body area sensor Distributed system Electronic health records Replica management

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Fake News Detection Using ARO and LSTM Algorithms

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SN computer science 2025年第1期6卷 1-11页

作者： Bhagat, Amit Dangi, Dharmendra Suman, Vaibhav Dixit, Dheeraj Kumar Sharma, Suvarna Department of Computer Applications MANIT Madhya Pradesh Bhopal India Department of Information Technology IIIT Madhya Pradesh Bhopal India Department of Computer Science & Engineering LNCT University Madhya Pradesh Bhopal India Department of Computer Science & Engineering MITS Madhya Pradesh Gwalior India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

There has been an exponential increase in social media content over the past few years due to the popularity of social media platforms. However, this has also resulted in the dissemination of intentionally false information, or fake news, which can have significant negative effects on society. As a result, detecting fake news has become an important issue that requires attention. Promoting trust in the global community is crucial to preventing the spread of false information in mass media. A study report suggests a reliable technique for identifying false news in order to remedy this. The author has thoughtfully chosen two distinct datasets: the Buzzfeed dataset, which is an assortment of news articles from Buzzfeed News, and the ISOT dataset, which is a collection of social media postings and articles from other sources. The author thoroughly analyzed a variety of content kinds and assessed the efficacy of various approaches and methods for processing and categorizing them using the datasets. The study found that these datasets have accuracy rates of 99% and 97.8% respectively. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： ARO algorithm Fake news detection LSTM Pre-processing

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Leveraging Web applications for Improved Dining Experience and Data-Driven Decision Making 3

Leveraging Web Applications for Improved Dining Experience a...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Subhashini, S. Maruthi, R. Nagarajan, Srideivanai Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350342338

The objective of the project is to design a feedback system for Hostel Mess management that will aid the organization and students who are residing in the hostel. As they are facing many issues with hostel mess, it is utmost important to collect the feedback from the students and provide necessary solutions and suggestions. This web application handles the hostel mess management functionalities such as quick access to menu options available for each day and lets the users vote on a weekly basis. Furthermore, this system enables to maintain track of the hostel mess activity's performance and outcomes in order to assess how well it is meeting the organization's measurable and pertinent goals. It gives management access to timely, safe, and confidential reports from the users, which helps the preparation and decision-making process go more smoothly. The system's primary goal is to get user input regarding the mess of the hostel by addressing them about their experiences there. The collected data is then analysed to make informed decisions using data analysis strategies. The report that the system generates addresses the majority of management's concerns regarding hostel mess records. © 2023 IEEE.

关键词： data analysis feedback Hostel mess mess management web application

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Sensor and Microcontroller based 360 Degree Car Assistance System 4

Sensor and Microcontroller based 360 Degree Car Assistance S...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Jenit Elavarasi, J. Franklin, Abilash Nagarajan, Srideivanai Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

The 360 Degree Car Assistance System automatically applies emergency brakes when necessary and regulates a vehicle's speed based on the proximity of an object. Damages that may be made while driving will be avoided by this approach. The car's undercarriage is vulnerable to hit from the ground and vice versa, and the region is also hidden from the driver's view. Thus, in order to avoid this, a survey was conducted, and this project was built in such a manner that it would identify the item and alert the driver if there were any impediments beneath the car that are close to the Chassis and the Wheels. A prototype of the car is created and used in this project. The system uses an Ultrasonic Sensor and AT Mega to detect obstructions, and it allows the execution of emergency braking in accordance with pre-burnt code in the AT Mega. Consequently, a cost-effective automatic collision avoidance system is suggested. In order to avoid and prevent accidents all automobile models can use the 360 Degree Car Assistance System that has been designed. © 2023 IEEE.

关键词： Advanced driver assistance systems

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Automated Motorcycle Helmet Detection Using The Combination of YOLO AND CNN 3

Automated Motorcycle Helmet Detection Using The Combination ...

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

作者： Anne Sanchana, Mary Eliyas, Sherin Hindustan Institute of Technology and Science Department of Computer Applications Padur603 103 India

ISBN: (纸本)9798350399264

Motorcycle accidents are a significant public concern worldwide. Accidents have been increasing rapidly which leads to the loss of several lives. One of the main contributing factors to the severity of injuries in these accidents is the lack of helmet use. Though the usage of helmets is necessary, it is also considered a piece of mandatory safety equipment among drivers, but they do not make use of it which causes unfortunate incidents to take place. Traffic police do try to bring awareness among the people but most of the population refuses to follow them. To avoid such incidents and to detect people from breaking the rules, this research was introduced. Here, we present a study based on an automated helmet detection system for motorcyclists using the combined techniques of YOLO and CNN. The combination of these image processing techniques such as You Only Look Once (YOLO) and Convolutional Neural Network (CNN) helps in the identification of helmets from image datasets. Because of this combination, the model can accurately identify helmets with a high degree of reliability due to the use of image datasets. And due to this combination, our system can accurately detect helmets with an accuracy of 94.29%. © 2023 IEEE.

关键词： Object detection

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An Effective Multi-exposure Fusion Approach Using Exposure Correction and Recursive Filter 7th

An Effective Multi-exposure Fusion Approach Using Exposure C...

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7th International Conference on Inventive Systems and Control, ICISC 2023

作者： Jishnu, C.R. Vishnukumar, S. Department of Computer Applications Cochin University of Science and Technology Kerala Kochi682022 India

ISBN: (纸本)9789819916238

The visual quality of a natural scene is constantly degraded when using conventional imaging sensors. The dynamic range is the factor that limits the sensors to capture more information as it is limited to a specific range during capturing. Multi-exposure image fusion (MEF) is a technique for creating a well-exposed, high dynamic range (HDR)-like image from a collection of inconsistently exposed, low dynamic range (LDR) images. The fused image may still have image artifacts and lose information, nevertheless. To overcome this, we propose an MEF approach using an effective exposure correction mechanism. The technical strategy for the proposed method is to initially improve the source images using an exposure correction strategy, followed by merging the images using pyramidal decomposition to produce HDR-like images. Experimental comparison with existing methods demonstrates that the proposed procedure produces positive statistical and visual outcomes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image fusion

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Monitoring Perishable Foods using Machine Learning and Internet of Things 2

Monitoring Perishable Foods using Machine Learning and Inter...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Maruthi, R. Lakshmi, I. Devi, V. Durga Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India Dhanapalan College of Science & Management Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

The perishable food items are needed to be monitored in order to ensure food safety and quality before it is to be consumed. The perishable food items were preserved and packed to maintain the quality as well as safety in an appropriate environment. The environment has to be monitored using feasible technologies. The Internet of Things (IoT) is a proven technology to monitor and control anything in the remote place. The Machine Learning (ML) approach, an emerging field can be combined with IoT to monitor the perishable food items effectively. The proposed study focuses on a framework with IoT and ML for the smart monitoring of food items to maintain its safety and quality to avoid wastage of food items. © 2023 IEEE.

关键词： Internet of things

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