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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Jadhav, Ranjana Damre, Prajwal Hire, Atharva Gosavi, Priti Deshmukh, Sanskruti Vishwakarma Institiute of Technology Department of Multidisciplinary Engineering Pune India Vishwakarma Institiute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350396157

The idea and features of the proposed You Tube video summarizer tool created especially for regional languages is presented in this research study. Generally, it is a challenging task for viewers, who prefer regional languages to access and understand videos that are primarily available in a different language in YouTube. The proposed You Tube video summarizer in regional language attempts to overcome this challenge by offering a service to summarize videos in regional languages, making them easier for viewers who might not understand the original language of the video to access and comprehend. The developed program analyses and extracts important information from the video's audio or subtitles using natural language processing (NLP) and machine learning algorithms. This YouTube video summarizer in local languages has a wide range of potential uses. It can improve non-English speakers' access to information, improve video browsing, and promote effective content consumption in localized language groups. © 2024 IEEE.

关键词： Machine learning

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Enhanced Vision Transformer with Lion Optimizer for Accurate Classification of Monkeypox Skin Images 4

Enhanced Vision Transformer with Lion Optimizer for Accurate...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Bhatnagar, Roheet Hassan, Esraa Saber, Abeer Shams, Mahmoud Y. Elbedwehy, Samar Manipal University Jaipur Department of Computer Science and Engineering Rajasthan India Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial Intelligence Information Technology Dept. Damietta34517 Egypt Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Data Science Kafrelsheikh33511 Egypt

ISBN: (纸本)9798350387490

Monkeypox is a contagious viral infection caused by the monkeypox virus (Mpoxvirus). Characterized by skin lesions, fever, respiratory difficulties, swollen lymph nodes, and neurological complications, MPX can be fatal. Recent outbreaks have spread the disease across Europe, Australia, the United States, and Africa. Traditionally diagnosed through PCR testing of skin lesions, this process poses significant risks to medical personnel due to potential exposure to the highly contagious virus. However, the usage artificial intelligence (AI) is revolutionizing diagnostics, providing a safer and more efficient approach to MPX detection. In this paper, a new AI technique based on vision transformer and lion optimizer for classifying Monkeypox Skin Images. The presented model used for classifying the dataset and results reached accuracy of 99.6%. © 2024 IEEE.

关键词： Classification Deep Learning Diagnosis Monkeypox

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Harnessing Long Short Term Memory Networks for Stock Market Forecasts

Harnessing Long Short Term Memory Networks for Stock Market ...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Faheem, Alishba Arshad, Usama Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Department of Artificial Intelligence Topi Pakistan

ISBN: (纸本)9798350349719

Stock market trend prediction is a classical re-gression problem with effects on stock exchange. This research is presenting a detailed exploration of how Long Short Term Memory (LSTM) networks may be used for predicting stock market trends. The LSTM model was employed instead of the conventional Recurrent Neural Networks (RNNs), to learn from historical financial data. The study examines the operations of the LSTM model and the optimization of hyper parameters that lead to accurate prediction outcomes from the given dataset. Our model gave encouraging results that underlined the LSTM model's prowess in capturing intricate temporal dependencies in data. The model's result were in a low Root Mean Square Error (RMSE) score and an impressively high accuracy level that were good indicators of the model's adaptability at trend identification. Our study amplifies the need for advanced machine learning algorithms like LSTM that increase prediction accuracy while offering insights into decision-making processes for both investors and financial analysts at the same time. Further, it also hints at the potential of deep learning within the stock market. The paper concludes by discussions on all possible improvements to the existing model, future directions, and how incorporating additional data types can increase our predictive accuracy. This research primarily underscores the significant role LSTM plays in deciphering complex financial data trends accurately. © 2024 IEEE.

关键词： Long short-term memory

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A Real TimeVehicle Detection Based on Modified Convolutional Neural Networks in Different Climate

A Real TimeVehicle Detection Based on Modified Convolutional...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Johnson, Sandra Laxmi, B Prathusha R.M.K. Engineering College Department of Artificial Intelligence and Data Science Chennai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9781665462631

In order to effectively manage and monitor traffic on the roads, intelligent technologies must be able to detect and tally the number of cars on the road. The technology may be used for controlling and monitoring traffic in both urban areas and on highways, especially in times of heavy congestion or bad weather. It has traditionally been accomplished using sensor data and the standard toolkit for image processing. Recently however, with the help of deep learning-based smart computer vision systems, this process has become highly scalable and dependable. For intelligent traffic monitoring and management of issues like traffic congestion, the information gathered from various surveillance cameras may be utilized to train models that can recognize and track on-road cars, even in low-light and blurry situations brought on by bad weather. Although they all aim to solve the same problem, vehicle identification algorithms all have to deal with one or two very distinct cases. Vehicle detection under haze, dust, sandstorms, snow, and rain, as well as daytime and nocturnal conditions, is the focus of this study. The suggested design is based on the CNN architecture as a starting point, but it adds a spatial pyramid pooling layer and removes several of the Batch Normalization levels. During training, the system achieved an average accuracy of 81% and was able to identify even the tiniest vehicles in the scene. © 2023 IEEE.

关键词： Traffic congestion

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Emotion-Based Song Recommendation System 11th

Emotion-Based Song Recommendation System

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Sathya, A.R. Varma, Alluri Raghav Department of Data Science and Artificial Intelligence Faculty of Science and Technology ICFAI Foundation for Higher Education Telangana Hyderabad India

ISBN: (纸本)9789819967056

In current scenario, many music applications are competing with each other to increase their customer base. The user chooses the app based on criteria like less latency, UI friendly, and so on. Existing song recommendation systems suggest songs based on the user’s previous music preferences, such as by examining at his previous song choices, the amount of time he spends listening to music, etc. But classifying the data and preparing separate playlists based on user’s history are time-consuming. But, song recommendation based on user emotions is another advancement where the application recommends songs based on user’s facial expressions. The user emotions can be determined by their facial expressions. Therefore, the need to read a person’s emotions and recommending songs accordingly is something new to explore. Thus, in this paper, we propose a machine learning approach that focuses on detecting human emotions from the input image and creating a music playlist based on the emotions detected. The machine learning model, Convolutional Neural Network (CNN) is used for image classification in this approach. The main goals of the suggested work are to analyze the user’s image, categorize their emotion, and offer songs depending on that feeling to increase customer satisfaction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image recognition

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Transfer Learning and Fine-tuned CNN Architecture for Dog Breed Classification 3

Transfer Learning and Fine-tuned CNN Architecture for Dog Br...

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3rd International Conference on artificial intelligence For Internet of Things, AIIoT 2024

作者： Gunaranjan, K. Vijay, K. Naveen, P. St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Chennai India SRM institute of science and technology Department of computational intelligence Chennai India

ISBN: (纸本)9798350372120

This study presents a comprehensive approach to dog breed classification, leveraging transfer learning algorithms and Convolutional Neural Networks (CNNs). The dataset utilized for training and evaluation is sourced from the Dog Breed Identification dataset, encompassing 120 distinct breeds. A pre-trained CNN model is employed, and through the application of transfer learning, the proposed models achieve an impressive accuracy rate of 99%. The primary objective is to enable users to submit images of dogs, with the model accurately identifying the specific breed from the diverse dataset. The research underscores the efficacy of transfer learning and pretrained models in achieving exceptional accuracy for the nuanced task of dog breed classification, contributing to the advancements in image recognition within the context of diverse canine breeds. © 2024 IEEE.

关键词： Convolutional neural networks

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Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 58-73页

作者： Qingsong Zhao Lei Shu Kailiang Li Mohamed Amine Ferrag Ximeng Liu Yanbin Li the College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031 the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023 the State Key Laboratory of Cryptology Beijing 100878China IEEE the School of Engineering University of LincolnLincoln LN67TSUK the Artificial Intelligence and Digital Science Research Center Technology Innovation InstituteMasdar City 9639UAE the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security *** issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

关键词： Challenges Internet of Things(IoT) privacy and security security requirements solar insecticidal lamps(SIL)

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Influencing Factors in Facial Recognition and Verification: A Siamese Neural Network Hyperparameter Study

Influencing Factors in Facial Recognition and Verification: ...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Maaz, Ahmad Arshad, Usama Ahmad, Tafreed Abbas, Shaheer Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Department of Artificial Intelligence Topi Pakistan

ISBN: (纸本)9798350349719

There are many practical uses for facial verification and recognition jobs. In this study, we look into the developments and difficulties faced in facial recognition technologies, especially in the application of Siamese neural networks. This study converges around optimizing hyperparameters to improve Siamese network accuracy and performance. We evaluated the impact of different optimizers, loss functions, and data preprocessing techniques on Siamese network performance through a chain of experiments. The results demonstrated the importance of choosing the right hyperparameters, demonstrating how decisions such as the Adam optimizer and Binary Cross-Entropy as the loss function may greatly enhance the system's capability to precisely identify and validate faces. This study also overlooks the importance of diverse and high-quality datasets and states how these elements affect the model's performance. With this work, we hope to give a thorough manual for fine-Tuning Siamese neural networks for use in facial recognition applications, along with helpful advice on how to overcome the difficulties posed by dataset variability and hyperparameter selection. In addition to adding to the theoretical knowledge of facial verification technologies, our research offers useful suggestions for enhancing the conception and application of these systems in actual settings. © 2024 IEEE.

关键词： Face recognition

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NeuralMango: Advanced Mango Classification and Price Prediction

NeuralMango: Advanced Mango Classification and Price Predict...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Peerzada, Shuhrahbeel Saud, Muhammad Ramiz Javed, Danish Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Department of Artificial Intelligence Topi Pakistan

ISBN: (纸本)9798350349719

The mislabeling of mangoes in local Pakistani markets unfairly impacts buyers, who frequently end up paying a premium for a cheaper type of mango fruit. To address this issue, our study employs advanced deep learning techniques, specifically leveraging the EfficientBNet2 model. This model aids in providing consumers with accurate information about the mangoes they purchase, including details on daily government-set prices for these varieties. We've developed a user-friendly Flutter app supported by the predictive capabilities of the EfficientBNet2 model, enabling buyers to make informed decisions. Our research aims to bridge the gap caused by mango mislabeling in Pakistani markets, ultimately preventing buyers from falling victim to price discrepancies in various types of mangoes. By utilizing EfficientBNet2 and TensorFlow, our goal is to furnish users with real-Time pricing data and comprehensive product information. The creation of the Flutter app serves to empower buyers by putting this predictive technology directly into their hands. The significance of this study lies in its potential to simplify the buying process for everyday consumers through the application of deep learning models. Furthermore, by providing accurate pricing information, we anticipate contributing to greater stability within the local mango market. © 2024 IEEE.

关键词： Costs

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Detecting Community Through User Similarity Analysis on Twitter 18

Detecting Community Through User Similarity Analysis on Twit...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Hasan, Md Ahsan Ul Bakar, Azuraliza Abu Yaakub, Mohd Ridzwan Universiti Kebangsaan Malaysia Sentiment Analysis Lab Center for Artificial Intelligence Technology Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Universiti Kebangsaan Malaysia Data Mining and Optimization Lab Center for Artificial Intelligence Technology Faculty of Information Science and Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350331011

Community detection in social relationship networks is a significant research domain within the field of social network analysis. However, Twitter sets itself apart from conventional friends' and followers' networks due to its users' distinct focus on sharing thoughts and ideas related to specific topics rather than prioritising personal connections. As a result, relying solely on friends' and followers' relations in Twitter networks for community detection would be insufficient and incomplete. In this paper, we proposed a Twitter community detection method that measures similarity using user profiles, tweet subjects, and tweet sentiment in this research. We constructed a similarity network using real-time Twitter API data and compared it to Zachary's Karate Network. The proposed approach enhances the quality function of modularity and conductance in the Louvain community detection algorithm, resulting in a more precise and meaningful community structure within the Twitter network. © 2024 IEEE.

关键词： Graph databases

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