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Sentimental Analysis with Various Classification Techniques—A Comprehensive Survey 5th

Sentimental Analysis with Various Classification Techniques...

引用

5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Sharma, Rahul Kumar Dagur, Arvind School of Computing Science & Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819771776

Sentiment analysis is a subset of NLP and has encountered an outstanding change with the introduction of new approaches to increase its precision and performance. Neural networks and transformers in deep learning are very widely used techniques in sentiment analysis. This paper suggests the most recent research techniques that are currently used in sentiment analysis and also examines the difficulty and limitations of recently used techniques in sentiment analysis. This paper also provides directions for future studies. The increasing effectiveness of deep learning algorithms in sentiment analysis is an important finding from this survey. To analyze text data and determine the underlying sentiment, neural networks, and transformers have shown extraordinary potential. They are especially helpful in traversing the nuances of human language, which present difficulties for traditional machine learning techniques. Despite these developments, there are still difficulties with reliably identifying irony and sarcasm in sentiment analysis tools. To mitigate these issues, future research should concentrate on devising more robust sentiment analysis strategies capable of accurately interpreting sarcasm and irony. Additionally, efforts should be directed toward developing diverse datasets to ensure sentiment analysis techniques do not favor certain demographics or viewpoints. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Virtual Assistant with Facial Expession Recognition 5

Virtual Assistant with Facial Expession Recognition

引用

5th IEEE International Conference on Advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Saxena, Kanish Janarthanan, S. Goyal, Himanshi Galgotias University School Of Computing Science And Engineering India

ISBN: (纸本)9798350330861

In Today's world, Intelligent Virtual Assistants are used everywhere, and their base working principle is voice commands. And there are numerous technologies available for reading human facial expressions. So, in this paper, we will learn what happens if we combine both technologies. Does it better or create chaos? Artificial intelligence is gaining strength in the modern world. Every industry will soon use AI technology. AI has made great progress in recent years in identifying human emotions through voice, body language, facial expressions, etc. I's significance stems from the fact that traditional human capabilities can now be studied inexpensively and at scale in the software. Every sector can use me to provide new possibilities and efficiencies. It is significant because it successfully overcomes a well-defined set of technological challenges. The technology enables us to complete competitive tasks with basic controls quickly. Every new voice-interactive technology we adopt deepens our reliance on artificial intelligence (AI) and machine learning from our smartphones, computers, televisions, and refrigerators. The connection between machines and humans that we had been seeing and dreaming about a few decades ago has been real thanks to voice assistants like Amazon Echo, Siri, Google Home, and Amazon Lexa. But today, the fact is undeniable and real. Voice search technology is the upcoming digital trend on the market;in certain cases, touch screens have already been replaced. © 2023 IEEE.

关键词： Face recognition

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A cascade classifier-based system for identifying And counting the different kinds of vehicles using Open CV 1

A cascade classifier-based system for identifying And counti...

引用

1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Amit Yadav, Sunita Galgotias University Department of Computer Science and Engineering Greater Noida India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

Vehicle identification and counting are becoming more important tools for highway management. Anyhow, because cars come in a variety of sizes, identifying them remains a test that directly affects the accuracy of vehicle counts. The advancements in vehicle detection technology have significantly progressed alongside the development of autonomous driving and intelligent transportation research. This study focuses on video-based algorithms for vehicle detection and counting, utilizing OpenCV technology as the foundation for these techniques. The suggested solution use the background subtraction method to identify foreground elements in a subsequent movie. Using OpenCV, Numpy, and object detection techniques, from those moving objects, automobiles are spotted, categorised, and counted for traffic estimation. The results of an experiment reveal that the proposed counting techniques are about 84.60% percent accurate. The method is able to provide good vehicle counting accuracy with quick calculation when combined with quick motion detection and tracking techniques. © 2024 IEEE.

关键词： Highway administration

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Lossless Image Compression using K-Means Clustering in Color Pixel Domain 5

Lossless Image Compression using K-Means Clustering in Color...

引用

5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumari, Rimjhim Sriramulu, Srinivasan Galgotias University Department of Computer Science and Engineering Greater Noida India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Compressing images is a method for shrinking the image's dimensions using a particular algorithm. Image compression is a solution associated with transmitting and storing large amounts of data for digital images. While image storage is necessary for medical images, satellite images, documents, and pictures, image transmission includes a variety of applications like television broadcasting, remote sensing via satellite, and other long-distance communication. These kinds of applications deal with image compression. Lossy compression and lossless compression are two separate methods for compressing images. While unneeded metadata is deleted in lossless compression to minimize file size, lossy compression permanently removes part of the original data to reduce file size. The terms 'supervised learning' and 'unsupervised learning' refer to two different machine learning techniques. As opposed to a non-supervised learning algorithm, which uses unlabeled input data, supervised learning uses labeled data. While clustering is a subtype of unsupervised learning, classification, and regression are additional subcategories of supervised learning. This paper discusses the application of clustering using K-means. This methodology explores the concept of lossless picture compression is the process of reducing the size of an image without sacrificing its quality which is achieved using k-mean clustering. The proposed methodology achieves higher compression ratios than other existing approaches to image compression algorithms. © 2024 IEEE.

关键词： clustering compression image k-mean lossless unsupervised

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Feature Extraction in Image Processing using Medical Scans: Detailed Survey 2

Feature Extraction in Image Processing using Medical Scans: ...

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Singh, Tanu Singh, Manu Sugandh, Urvashi Panwar, Arvind UPES School of Computer Science Uttarakhand Dehardun India Galgotias University School of Computing Science and Engineering Uttar Pradesh Greater Noida India Galgotias University School of Computing Science and Engineering Uttarakhand Greater Noida India

ISBN: (纸本)9798350368109

An important area of research in computer vision is feature extraction algorithms for image processing, which is the focal point of this paper's analysis. In the field of visual management, feature extraction and its representation serve an important role. It can focus on desirable details that may represent the image's inherent meaning. The examined image has undergone a variety of image pre-processing techniques, such as Data Cleaning, Histogram Equalization, Skull Striping, Binarization, Feature Scaling, etc. before acquiring features about the particular medical scans. After that, feature extraction techniques are applied to obtain specific features that will be useful for the analysis of image scans. Thus, the approaches utilized for shape, texture, color and appearance-based feature extraction are the main subjects of our investigation in this paper. © 2024 IEEE.

关键词： Medical image processing

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MAPIFY: Map Your Day-to-Day Activities using Web Application 5

MAPIFY: Map Your Day-to-Day Activities using Web Application

引用

5th IEEE International Conference on Advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Singh, Abhishek Kumar Janarthanan, S. Upadhyay, Sushant Verma, Siddhant Galgotias University School Of Computing Science And Engineering India

ISBN: (纸本)9798350330861

Nowadays, everyone has become so busy in their life that they sometimes forget their meetings, events, and many day-to-day activities. We are dependent on others for information to get somewhere which we often forget. Many of the people face problem of depending on others to get anywhere on the planet for their crucial activities such as important meetings, sport activities, or kind of event that is to be happen in future. Some of them also don't remember all the activities and places to visit on. So, we have come up with the solution. The proposed solution to the existing problem is MAPIFY, a front-end web application developed by us which is an excellent example that one should include in their lifestyle. This application will keep the record of your various events in the local storage API of the web browser. This will make sure their location to be mapped on a map so that one can easily track it down, making life easier. It will keep record of all the activities that one should want to schedule along with location of that venue on the map. Also, there is no limit to these records, so one should not fear of storage run out, Since, we have used local storage Web API to solve the problem users face in case of weak internet connection which often leads to refresh of the web page which in turn leads to data losing. This mapify project will make sure to keep the record of the events with the location so that one can easily follow this to keep their day-to-day activities well-arranged and manage their time easily. © 2023 IEEE.

关键词： Location

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Credit Card Fraud Detection Using Machine Learning 1

Credit Card Fraud Detection Using Machine Learning

引用

1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Nishad, Alok Kumar Pandey, Nitesh Pandey, Vinay Kumar Galgotias University School of Computing Science and Engineering U.P. India

ISBN: (纸本)9798350356816

Credit card fraud continues to pose significant financial risk to consumers and any financial institution. To meet this task, this approach looks at machines that are particularly active in credit card fraud detection techniques, mainly focusing on random forest version Using a comprehensive dataset of credit card transactions we see how customized technologies can improve the accuracy of fraud detection. Our research aims to increase our technical understanding of techniques like decision trees, neural networks, and logistic regression. High accuracy deceptive interest detection was demonstrated by the random forest model, which had outstanding power. Beyond only comparing the different models in our study, this example shows how adaptable and trustworthy the model is when handling credit card theft. To investigate the models employed and emphasize the benefits and drawbacks of each technique for identifying fraudulent messages, we offer a thorough study of the system. Our results demonstrate the potential of machine recognition to improve fraud detection and establish random forest versions as predictors in this regard. This information has more concrete implications because it provides a reliable and powerful means of reducing economic losses and increasing the security of the financial system and of every individual. This study also suggests future directions, and it says that a combination of similar developments and models could also lead to more robust fraud detection systems. Credit card fraud continues, and is a constant *** conclusion, our research offers valuable information about credit card fraud detection, and the Random Forest version of the tool is effective, accurate, and gives better protection against fraud even in the face of ongoing attacks. © 2024 IEEE.

关键词： Neural networks

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Advanced Driving Assistant System: An Approach of Edge Detection Algorithm 5

Advanced Driving Assistant System: An Approach of Edge Detec...

引用

5th IEEE International Conference on Advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Dev, Mrinal Kumar, Gautam Kumar, Abhishek Saini, Kavita Galgotias University School Of Computing Science & Engineering India

ISBN: (纸本)9798350330861

Improving road safety and reducing road accidents, hence saving lives, is a major objective of Advanced Driver Assistance Systems. Detection of road lanes and/or road limits appears to be one of the most difficult and complex tasks of future road vehicles. This is built on vehicle detection, which includes identification of the road, assessment of the car's position in relation to the road, and analysis of the car's driving direction. Using the vehicle's visual system for identifying road limits and lanes is among the most popular ways. Yet, lane recognition is a challenging problem because of the variety of traffic conditions drivers can experience. A vision-based lane recognition approach that can function in real time and is resistant to lighting changes and shadows is presented in this research study. The device acquires the front image with a vehicle-mounted camera and then performs a few steps to recognise the lanes. This device captures the front image of the vehicle using a mounted camera and employs a variety of methods to identify the lanes. Using two fit plots to the lane edges, the Hough transform recovers the lanes. Its proposed lane detection method is adaptable to straight and curved roads, paint and unpainted roadways, and different weather situations. No more information is needed for the suggested method, including lane width, the length of time between lane crossings, or the offset between the centres of the lanes. No coordinate transformation or camera calibration is also necessary. The system was evaluated under a range of conditions, including varying illumination, shadow effects, and varied road kinds, with no speed restrictions. Ultimately, a comprehensive assessment of the methodologies and their prospective deployment prospects were addressed. © 2023 IEEE.

关键词： Advanced driver assistance systems

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A Study and Analysis of Timetable Generation using a Genetic Algorithm 5

A Study and Analysis of Timetable Generation using a Genetic...

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5th IEEE International Conference on Advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Choudhary, Sagar Janarthanan, S. Maurya, Pranjal Galgotias University School Of Computing Science And Engineering India

ISBN: (纸本)9798350330861

A system of manually creating timetables is tedious in terms of time and resources for educators in universities with large numbers of students, and usually results in conflicting classes and multiple lectures assigned to the same professor. It will result in supplying an automated timetable maker can help you create exams and timetables. the system helps auto-generate and saves time. Avoid complex manual processes to set up and manage timetables. Here, the goal is to create a user-friendly, effective, and straightforward application that facilitates the process of creating and distributing timetables. The main algorithm used to generate timetables is the genetic algorithm. Organizing all the rules will help you create the optimal schedule. Teachers don't have to worry about time conflicts. The system's user interface is simple, interactive, and allows for inputs such the teacher's name, the date of the classroom, the date of the lab, and the date of the topic. the programme features a well-designed database to keep track of all the data submitted. The system uses algorithms to handle all of the data in the database while taking both hard and soft limitations into consideration. A schedule generating method that use a genetic algorithm to find the best option. Instead of cumbersome paperwork, students and instructors may examine timetables immediately. The technique is simple to use and will assist you in creating an effective timetable. © 2023 IEEE.

关键词： Soft computing

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Leaf Detection: Techniques and Applications

Leaf Detection: Techniques and Applications

引用

2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Kumar, Rahul Pal, Arpit Singh, Tejendra Chauhan, Alok Singh School of Computing Science and Engineering Galgotias University Department of Computer Science and Engineering Greater Noida India School of Computing Science & Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350329186

Leaf detection is a critical task in various fields, including agriculture, ecology, and botany, with applications ranging from plant species identification to disease diagnosis. This research paper explores the techniques used for leaf detection, the applications where it is employed, and the challenges faced in this domain. Traditional image processing techniques, machine learning approaches, deep learning-based methods, and hybrid approaches are examined and compared. The applications of leaf detection include agriculture and crop monitoring, plant species identification, disease diagnosis and management, ecological studies, and remote sensing. The challenges in leaf detection arise from the variability in leaf shapes and textures, occlusion and complex backgrounds, realtime processing requirements, and robustness to environmental factors. Future directions and emerging trends in multi-modal leaf detection, domain adaptation, integration of remote sensing technologies, and advanced image analysis techniques are discussed. The findings of this research have implications for further research and practical applications, highlighting the need for more robust and accurate leaf detection techniques and the utilization of advanced technologies. The research contributes to a deeper understanding of plant ecosystems, optimization of agricultural practices, and improved management of natural resources. © 2023 IEEE.

关键词： Remote sensing

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