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Insect Classification Framework based on a Novel Fusion of High-level and Shallow Features

Insect Classification Framework based on a Novel Fusion of H...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Haarika, Raye Babu, Tina Nair, Rekha R. Department of Computer Science and Engineering Dayananda Sagar University School of Engineering Bengaluru India Department of Computer Applications Dayananda Sagar University School of Engineering Bengaluru India

The largest and most diversified group of organisms are insects. Since insects play a crucial role in many ecosystems, it must be precisely identified for efficient management. However, it is difficult and labor-intensive to identify insect species. Due to advancements in deep learning, computer vision, and sensor technologies, there is rising interest in image-based systems for quick, accurate identification. This study investigates a novel hybrid feature set of shallow features from the tiger beetle dataset, such as texture and wavelet features and high-level features from SqueezeNet. The tiger beetle insect is classified into the cicindelini and collyridini classes with a random forest classifier with 97.65 percent accuracy using this hybrid feature set, which considers the texture and structural characteristics of the insect. Thus the technique provides insight into various features and indicates promising future directions for image-based insect identification and species classification relevant to computer science, Agriculture, and Ecology research. © 2022 Elsevier B.V.. All rights reserved.

关键词： Ecology

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Ad Service Detection - A Comparative Study Using Machine Learning Techniques 13

Ad Service Detection - A Comparative Study Using Machine Lea...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Yadhu Krishna, M. Sanjana, S. Thushara, M.G. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science and Engineering Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science and Applications Amritapuri India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

The use of web-based services has been soaring since the internet has come up. Hence, traditional forms of advertisements have moved on to the web, leading to a rising number of ad services and trackers affecting user privacy. Ad blockers were developed in order to prevent these. However, the currently available ad blockers are purely based on a predefined set of blacklist domains. This study proposes a machine learning-based approach to detect web-based ad services and trackers. In order to help us explore the area using machine learning techniques, a dataset was created. The developed dataset contained 74,000 entries comprising both domains that served advertisements and normal domains. In addition, we have also compared the scores that were obtained by various supervised machine learning models, and the best model was identified. The best model offered up to 88% accuracy in classifying ad services and normal websites. The model contributed by this paper could be further developed into a machine learning-based advertisement blocker system. © 2022 IEEE.

关键词： Websites

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Sentiment classification of reviews using combination of Firefly Algorithm and ANN 21

Sentiment classification of reviews using combination of Fir...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Tripathy, Abinash De, Utpal Chandra Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Rao, Ch. Chakradhara Pandey, Trilok Nath KIIT Deemed to Be University School of Computer Applications Odisha Bhubaneswar India Raghu Engineering College Department of Computer Sc. & Engg AP Viskahapatnam India Vellore Institute of Technology School of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350358230

With the availability of many social media sites, the reviews and comments by the customers are freely available for analysis. Most of these reviews are textual as per the content ***, proper processing need to be done for future use of the reviews. As reviews are in text, the amount of words need to be processed for any analysis work is very large in amount. So, an approach needs to be adopted to minimize the amount of word without affecting the result of the analysis. Thus, in this paper the Firefly algorithm is used to select the words / features that can act as a representative of the whole set of words. The output of Firefly algorithm i.e., selected features act as input of ANN. In this paper, a hybridization of Firefly algorithm and ANN is carried out for better accuracy result. The result of the proposed approach is analyzed by using confusion matrix and various evaluation parameters. © 2023 IEEE.

关键词： Sentiment analysis

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Sentiment analysis of reviews using Nature Inspired PSO and ANN

Sentiment analysis of reviews using Nature Inspired PSO and ...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Tripathy, Abinash De, Utpal Chandra Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Rao, Ch. Chakradhara Pandey, Trilok Nath Kiit Deemed to Be University School of Computer Applications Odisha Bhubaneswar India Raghu Engineering College Department of Computer Sc. & Engg AP Viskahapatnam India Vellore Institute of Technology School of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350308167

Social media now plays an important role is sharing the views or comments about a topic. These comments are available to the users for their processing and future decision making process. As the reviews contains mostly text or emojis, thus, processing is required to obtained the required information. Nature is always a source for inspiration and *** activities of the nature can act as a source for optimization and classification. In this paper, two nature inspired algorithms are considered for analysis. Firstly, Particle Swarm Optimization (PSO) act as an optimization technique for feature selection i.e., best features are selected and these best features act as input to Artificial Neural Network (ANN) for further classification purpose. The reviews are processed using the proposed algorithms and finally, the confusion matrix is generated, which helps to generate the evaluation parameters to evaluate the results. © 2023 IEEE.

关键词： Artificial Neural Network (ANN) Confusion matrix Particle Swarm Optimization(PSO) Performance evaluation parameter Sentiment analysis

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A Smart Approach to Coconut Leaf Spot Disease Classification using computer Vision and Deep Learning Technique

A Smart Approach to Coconut Leaf Spot Disease Classification...

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2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Brar, Simrat Kaur Sharma, Rishabh Vats, Satvik Kukreja, Vinay Chandigarh Group of Colleges Department of Computer Applications Landran India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350311204

Coconut leaf spot (CLS) disease is a major threat to coconut production and can cause severe economic losses. In this study, we propose a deep learning (DL)-based ResNext50 model for automated detection and severity classification of CLS disease. Our model leverages a ResNext50 mode;and is trained and tested on a dataset of coconut leaf images with six severity levels, ranging from healthy leaves to critical severity. The proposed approach achieves high accuracy in detecting and classifying the severity levels of the disease. Our findings suggest that the proposed method is successful in properly identifying and categorizing the illness severity levels, with an accuracy rate of 91.77% overall. The strategy that has been presented has the possibility to significantly improve the efficiency and accuracy of CLS disease detection and monitoring, ultimately leading to better management strategies and increased productivity in the coconut industry. © 2023 IEEE.

关键词： computer vision

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Performance Comparison of Deep Learning Models for computer Generated Image Detection 5

Performance Comparison of Deep Learning Models for Computer ...

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5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Sychandran, C.S. Shreelekshmi, R. APJ Abdul Kalam Technological University Department of Computer Science and Engineering College of Engineering Trivandrum Kerala Thiruvananthapuram695016 India APJ Abdul Kalam Technological University Department of Computer Applications College of Engineering Trivandrum Kerala Thiruvananthapuram695016 India

ISBN: (纸本)9798350334128

With the advancement in digital image processing technology, there is a huge proliferation of computer-generated images. They are widely used in interactive games, film industry and cartoons. computer-generated images are photo-realistic because they are created using complex algorithms and high-end graphics processing power to accurately mimic real-world objects and environments' lighting, textures, and colors. Since computer-generated images are more realistic, even human eyes can barely distinguish them. So verifying image authenticity and differentiating computer-generated images from photographic images are inevitable. This paper investigates the performance of different deep learning models for detecting computer-generated images. We experiment on three datasets: DSTok, Columbia PRCG and Rahmouni. Experimental results show that the hybrid Xception ensemble outperforms the state-of-the-art methods and achieves the highest accuracy, precision, recall, F1 score and specificity on three benchmark datasets. © 2023 IEEE.

关键词： Deep learning

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Enhancing Industrial applications with LSTM-Based Predictive Analytics 15

Enhancing Industrial Applications with LSTM-Based Predictive...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ramachandran, Lalitha Girinath, S. Padmavathi, N. Subramaniam, G. Chitra, M. Siva Sathya, V. R.M.K. Engineering college Department of Science and Humanities Chennai India Department of Computer Applications Tirupati India R.M.D. Engineering College Department of Electronics and Instrumentation Engineering Tamilnadu India M. Kumarasamy College of Engineering Department of EEE Tamilnadu Karur India Kongu Engineering College Department of EEE Tamilnadu Perundurai India VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350370249

Predictive analytics in industrial applications has the potential to significantly boost productivity and enhance operational efficiency. The research intends to solve is the difficulty of traditional models to capture the detailed temporal correlations and complex non-linear patterns that are common in industrial data. Long Short-Term Memory (LSTM)-based predictive analytics are the primary focus of this research, with the primary objective being the enhancement of industrial processes. Predicting complex and ever-changing patterns within operational data is a challenge in the modern industrial environment. The allocation of resources and decision-making processes are not as ideal as they may be. The LSTM architecture enables to use of the temporal correlations of the data in order to improve ability to predict the future. LSTM-based predictive analytics are significantly more accurate than conventional models when it comes to predicting the events that will occur in industrial processes. The findings shows LSTM-based predictive analytics have the potential to revolutionize industrial decision-making, which in turn leads to improved resource management and operational efficiency. © 2024 IEEE.

关键词： Industrial applications LSTM Operational efficiency Predictive analytics Temporal dependencies

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Audio to Sign Language conversion using Natural Language Processing 3

Audio to Sign Language conversion using Natural Language Pro...

引用

3rd International Conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Nayana, J. Bhat, Suparna Nair, Rekha R Babu, Tina School of Engineering Department of Computer Applications Dayananda Sagar University Bengaluru India School of Engineering Department of Computer Science and Engineering Dayananda Sagar University Bengaluru India

ISBN: (纸本)9781665474979

People who are deaf never get to enjoy things that hearing folks do, whether it's conversation, playing the game, going to seminars, or participating in virtual meetings, for example. The major challenge they have while interacting among regular people is connection, because every regular person has no concept what sign language is or how it works.. Our project's objective is to help those who have hearing loss develop a system for communication. The spoken message is converted into signs. It converts the spoken word into signs. This system receives voice as inputs, converts the soundtrack into a text, and displays the key symbols used in Indian language using well before graphics or GIFs. The use of this technology makes communication between those who have normal hearing and those who are difficult to hear easier This takes into account how your arms, body, and appearance are combined. Around the universe, there really are 135 different varieties of hand signals. There are numerous different sign languages, including American Sign Language (ASL), Indian Sign Language (ISL), British Sign Language (BSL), Australian Sign Language (Auslan), and many more In this initiative, Indian Sign Language is being used. The deaf community may take advantage of all the activities that hearing people engage in, from social engagement to accessing information, thanks to this technology. NLP is used to pre-process the text (Natural Language Processing). With the help of this technology, hearing-impaired people may participate in a wide range of activities, from socialising to accessing information NLP is used to post the text (Natural Language Processing). This programme converts audio into text, displays Indian sign language, and then accepts speech as input. Accuracy rate for the planned project work is 99%, and Validation accuracy is 96%. © 2022 IEEE.

关键词： component formatting insert style styling

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DLIoMT: Deep Learning Approaches for IoMT-Overview, challenges and the future

DLIoMT: Deep Learning Approaches for IoMT-Overview, challeng...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Harshni, V. Singh, Satyendr Indhumathi, R. Kumar, Neelam Sanjeev Chowdhury, Subrata Patra, Sudhansu Shekhar Srm Institute of Science and Technology Department of Bme Ramapuram campus Chennai India Bml Munjal University Computer Science & Engg Department Gurugram India Vadapalani Campus Chennai India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India Deemed to be University School of Compter Applications Odisha Bhubaneswar India

ISBN: (纸本)9798331513894

An artificial intelligence (AI) system works by combining a computer program and algorithms to make a device more efficient and intelligent for tasks that are typically performed by humans. Deep learning, machine learning, conventional neural networks, recognition, and fuzzy logic are a few of the subsets of AI with unique capabilities and functions that can enhance modern medical science. Such innovations simplify human interference in clinical diagnostic procedures, decision making, and medical imaging. To improve human health, connected biomedical network devices and software applications are interconnected to form the Internet of Medical Things (IoMT), the next generation of bioanalytical tools. This review paper provides in-depth analysis of the IoMT encasing its definition, technologies, applications, challenges, and future prospects. © 2025 IEEE.

关键词： Electronic health record

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Adaptive Hybrid Fraud Detection System with Personalized Transaction Profiling 5

Adaptive Hybrid Fraud Detection System with Personalized Tra...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Musthafa, A. Syed Sree, Pokkuluri Kiran Fathima, H. Jayalakshmi.r, Angu Saranya, P. Murugesh, L. M.Kumarasamy College of Engineering Department of Information Technology Tamil Nadu Karur639113 India Department of Computer science and Engineering Vishnupur Andhra Pradesh Bhimavaram534202 India KSR College of Arts and Science Department of Computer Applications Tamil Nadu Tiruchengode637 215 India M. Kumarasamy College of Engineering Department of Information Technology Tamil Nadu Karur639113 India

ISBN: (纸本)9798331530013

This research study presents an Adaptive Fraud Detection System that uses personalized user behavior profiles and multiple layers of detection methods to spot fraud in real time. By clustering similar transaction patterns and applying machine learning techniques like ensemble models and autoencoders, the system can continuously improve its ability to detect unusual activity. Frequency of getting false alarm is reduced. Improved functional clarity in the system. © 2024 IEEE.

关键词： Adversarial machine learning

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