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Fruit and Vegetable Grading with Transfer Learning and Convolutional Neural Networks for Better Productivity 3

Fruit and Vegetable Grading with Transfer Learning and Convo...

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3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Narvekar, Chhaya Rao, Madhuri Xavier Institute of Engineering Department of Information Technology Mumbai India Thadomal Shahani Engineering College Department of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9798350348347

Grading provides quality assurance by confirming that agricultural products match predetermined standards, which can boost consumer trust in the goods. Additionally, it aids in ensuring that consumers receive products that are risk-free and error-free. In this study, a fruit and vegetable grading system based on deep learning methods such as transfer learning and convolutional neural networks (CNN) is proposed. The suggested approach correctly categorizes various fruit and vegetable varieties according to their outward appearance. Objective is to ascertain the effect of various grading parameters, such as size, shape, and color, on the grading system's correctness. This study utilized labeled image dataset for training models. Transfer learning is utilized to enhance the performance and accuracy of the grading system by leveraging pre-trained CNN models. The experimental findings show that the suggested system outperforms existing fruit grading systems and achieves excellent accuracy to comprehend how grading and standardization affect farming's profitability. © 2023 IEEE.

关键词： Grading

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IoT Malware detection Technique Based on Deep Learning and Natural Language Processing 3

IoT Malware Detection Technique Based on Deep Learning and N...

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3rd international conference on Electronic and Electrical Engineering and Intelligent system, ICE3IS 2023

作者： Al Abbas, Marwa A. Khammas, Ban M. Collage of Information Engineering Al-Nahrain University Department of Computer Networks Engineering Jadriya Baghdad Iraq

ISBN: (纸本)9798350327762

The Internet of Things (IoT) has gained significant popularity and attention as a research topic due to its widespread use and associated concerns. Previous security measures, which focused on specific threats using limited and outdated datasets, need to be revised to address the growing size and diversity of IoT applications. Consequently, there is a pressing need to prioritize security in IoT systems. Various deep learning (DL)-based algorithms have been developed to address this challenge for IoT attack detection. In this study, we proposed two deep learning models for malware classification integrated with natural language processing (NLP) techniques, such as padding and word embedding. The first model utilizes a Recurrent Neural Network (RNN), and the second employs a one-dimensional Convolutional Neural Network (CNN). Both proposed systems were trained and evaluated on 3971 opcode instruction files representing attacks on nine CPU architectures. The proposed methods were evaluated based on accuracy, F1-score, recall, and precision and were compared with the latest approaches. The experimental results demonstrate the effectiveness of the proposed models in accurately identifying and categorizing IoT attacks. The CNN classifier achieved an accuracy of 96.7%, while the RNN classifier achieved an impressive accuracy of 99.3%. © 2023 IEEE.

关键词： Internet of things

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Findings of the Shared Task on Multimodal Abusive Language detection and Sentiment Analysis in Tamil and Malayalam 3

Findings of the Shared Task on Multimodal Abusive Language D...

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3rd Workshop on Speech and Language Technologies for Dravidian Languages, DravidianLangTech 2023

作者： Premjith, B. Jyothish Lal, G. Sowmya, V. Chakravarthi, Bharathi Raja Natarajan, Rajeswari Nandhini, K. Murugappan, Abirami Bharathi, B. Kaushik, M. Prasanth, S.N. Raj, R. Aswin Vijai Simmon, S. Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Ireland SASTRA Deemed to be University SRC Campus Tamilnadu Kumbakonam India School of Mathematics and Computer Sciences Central University of Tamil Nadu India Department of Information Science and Technology Anna University India SSN College of Engineering Tamilnadu India

ISBN: (纸本)9789544520854

This paper summarizes the shared task on multimodal abusive language detection and sentiment analysis in Dravidian languages as part of the third Workshop on Speech and Language Technologies for Dravidian Languages at RANLP 2023. This shared task provides a platform for researchers worldwide to submit their models on two crucial social media data analysis problems in Dravidian languages - abusive language detection and sentiment analysis. Abusive language detection identifies social media content with abusive information, whereas sentiment analysis refers to the problem of determining the sentiments expressed in a text. This task aims to build models for detecting abusive content and analyzing fine-grained sentiment from multimodal data in Tamil and Malayalam. The multimodal data consists of three modalities - video, audio and text. The datasets for both tasks were prepared by collecting videos from YouTube. Sixty teams participated in both tasks. However, only two teams submitted their results. The submissions were evaluated using macro F1-score. © DravidianLangTech 2023 - 3rd Workshop on Speech and Language Technologies for Dravidian Languages, associated with 14th international conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings.

关键词： Sentiment analysis

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Real Time Car Speed detection Using CNN Algorithm 3

Real Time Car Speed Detection Using CNN Algorithm

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Panigrahi, Jangyaseni France, K. Hindustan Institute of Technology and Science Department of Computer Applications India

ISBN: (纸本)9798350399264

In addition to determining car speed, the method can be used to track traffic patterns along a road or motorway. The speed of the car is rarely determined with the security cameras already in use. The majority of the currently used methods require ongoing maintenance and employ expensive sensors. As a result, we suggest a technique for using security cameras to monitor traffic and estimate car speed. To detect a car, pinpoint its location, determine the distance it has travelled over time, and ultimately determine its speed, various image processing techniques are applied. The proposed method's ability to predict speed even in videos with low resolutions or quality will be evaluated. The traffic surveillance system (TSS) is a crucial instrument for gathering important data (speed). Convolution neural networks (CNNs) with a single pipeline have been the focus of a significant amount of research up to this point. Even though this research produced highly accurate results, they needed a sizable dataset and an implementation on specialized hardware. © 2023 IEEE.

关键词： Target tracking

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Mobile Based Application for Tea Leaf Disease detection 3

Mobile Based Application for Tea Leaf Disease Detection

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3rd IEEE international conference on technology, Engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Manasa, J.P. Nethravathi VVIET Department of ECE Mysuru India

ISBN: (纸本)9798350397819

Tea plant leaf diseases are one of the grand challenges that face in agriculture sector worldwide. In India approximately 20% to 30% of the leaves are lost due to various diseases each year. Tea leaf disease identification is challenging for limited-resources farmers if performed through optical observation of plant leaves symptoms. Therefore, it is important to catch the spread of the disease can spread of the disease in its early stage before they reach epidemic proportion otherwise the disease can spread quickly throughout the entire plantation resulting in huge losses for the farmers. To aid the farmers in the crucial task of identifying tea leaf diseases in its early stage. it is practical to have an intelligent system for detection,identification and classification system in place as a preventive measure. This proposed model is a ML powered mobile based system to automate the plant leaf disease diagnosis process. The developed system uses CNN for feature reduction and classifying five diseases categories. The user interface is developed as a mobile application, allowing farmers to capture a photo of the infected Tea plant leaves. Captured image is then uploaded into the Database and then into the algorithm by specifying image *** then displays the disease category and symptoms of disease along with the confidence *** solutionwill be user friendly and will perform in an effective and efficient manner. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants. © 2023 IEEE.

关键词： Feature extraction

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CNN-Based Location detection of Static Eccentricity in 5-X Resolver 3

CNN-Based Location Detection of Static Eccentricity in 5-X R...

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3rd international conference on Electrical Machines and Drives, ICEMD 2023

作者： Emadaleslami, M. Tootoonchian, F. Tarbiat Modares University Faculty of Electrical and Computer Engineering Tehran Iran Iran University of Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350393743

According to the significant effects of faulty resolver condition and rotor angle error on Permanent Magnet Synchronous Motor (PMSM) drive system stability, the diagnosis of the fault occurrence and, more critically, the fault location is crucial. Among the resolver's faults, static eccentricity has more probability and severity. So, the following study focuses on proposing a Convolutional Neural Networks (CNN)-based static eccentricity location diagnosis under stator slots. Accordingly, using Finite Element Analysis (FEA), various locations and severity of static eccentricity are generated. The Fast Fourier Transform (FFT) and Frequency Occurrence Plot (FOP) are regarded as the data preprocessing stages, applying on resolver signal voltages. Then, in the training stage, various CNNs are trained to obtain a highly accurate network. The feasibility of the suggested CNN in fault location diagnosis is shown by the confusion matrix, accuracy metric, and F1-score criterion. © 2023 IEEE.

关键词： Convolution

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3rd international conference on Mechanical and Energy Technologies, ICMET 2023

3rd International Conference on Mechanical and Energy Techno...

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3rd international conference on Mechanical and Energy Technologies, ICMET 2023

ISBN: (纸本)9789819727155

The proceedings contain 50 papers. The special focus in this conference is on Mechanical and Energy Technologies. The topics include: A Numerical Investigation on Estimation and Validation of Power Law Constants for TiB2 Reinforced AA2024 Composite Material;Power Flow Control of the Grid-Integrated DG system Using Hybrid Aquila Optimizer‑Tangent Search Algorithm;Design and Analysis of Manual Pallet Stacker by Using FEM Method;Design and Development of an IoT Counter Using ESP8266 for 6 Axis MIG Welding Robot;In-Depth Examination of the Mechanical Properties of AA6061/MoS2/SiC Hybrid Composites;optimization of Microchannel Heat Sink Shapes to Enhancing Electronic Cooling Efficiency;empowering Next-Generation Energy Infrastructure Through IoT for Climate Resilience;safeguarding Cryptocurrency Transactions: Leveraging Blockchain and Machine Learning for Enhanced Financial Security;utilizing Machine Learning for Advanced Natural Language Processing and Sentiment Analysis in Social Media Platforms;improving Image Clarity with Artificial intelligence-Powered Super-Resolution Methods;revolutionising Tumour Diagnosis: How Clinical Application of Artificial intelligence and Machine Learning Enhances Accuracy and Efficiency;harnessing Medical Databases and Data Mining in the Big Data Era: Advancements and Applications in Healthcare;machine Learning-Powered Design and Implementation for Classification of Missing Data in IoT Applications;ioT-Based Security detection for Cloud Web Applications: Leveraging Internet of Things Approaches;Exploring Ethical Considerations: Privacy and Accountability in Conversational Agents like ChatGPT;navigating Cross-Lingual Natural Language Processing: Challenges, Strategies, and Applications;fostering Understanding: Bridging the Gap Between Black-Box Models and Human Interpretability with Explainable Artificial intelligence;semantic Analysis and Machine Learning Techniques for Enhancing Content-Based Image Retrieval.

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Detecting Vehicles using YOLOv8n in Edge Computing Dashcam 3

Detecting Vehicles using YOLOv8n in Edge Computing Dashcam

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3rd international conference on Intelligent Cybernetics technology and Applications, ICICyTA 2023

作者： Radim, Aura Syafa Aprillia Rusydi, Muchammad Irfan Chanif Nasution, Surya Michrandi Setianingsih, Casi Telkom University School of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350394559

Dashcam is a camera placed on the dashboard of a vehicle. This device's function is to capture footage of all events in front of the vehicle. Security and safety have become a significant concern in various sectors, including transportation and public roads. Traffic accidents caused by drivers' ignorance of objects around the vehicle are still a severe problem on the highway. In this study, a simple dashcam built from an edge computer was developed. By adding a camera, the dashcam is able to detect vehicles ahead. By the time, vehicles appear in the system, it will be detected using an object detection method called YOLOv8. This research is expected to be one step in a proof-of-concept of the development of an Intelligent Transportation system that is in accordance with traffic conditions in Indonesia. In this paper simulated and tested the usage of GPU from the edge computing device. Even though the YOLO8n has lower 6.29, 9.11, 6.05, and 0.24 points performances for its precision, recall, mAP50, and mAP50-95 respectively than YOLOv7-tiny, it only used half the computational cost than the YOLOv7-tiny. It shows the YOLOv8n is suitable as a detection method in an edge computing device. As the inference time testing, objects in an image can be detected from 65-500 ms based on the power supplied to the computer. It also means, in a second the system is able to infer objects for 2 to 15.38 frames. © 2023 IEEE.

关键词： Object detection

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Smart Grid vs. Intelligent Grid;Artificial intelligence Takes the Power system to the Next Level 3

Smart Grid vs. Intelligent Grid;Artificial Intelligence Take...

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3rd IEEE international conference on Digital Twins and Parallel intelligence, DTPI 2023

作者： Liasi, Sahand Ghiasi, Narges S. Hadidi, Ramtin Clemson University Holcombe Department of Electrical and Computer Engineering CharlestonSC United States

ISBN: (纸本)9798350318470

This paper discusses the concept of intelligent power systems, particularly the distinction between smart grids and intelligent grids. Smart grids represent a convergence of various energy technologies and communication systems, enabling multidomain interactions. However, in such a grid, humans are still the ultimate decision maker based on the gathered information. By adding artificial intelligence to the system, smart grids can be upgraded to intelligent grids. This article explores the cutting-edge methods and propositions of machine learning and artificial intelligence, and their influence on a plethora of smart grid applications, aiding their transition to truly intelligent grids. The piece highlights the surge in interest and rapid advancement in employing machine learning and AI techniques to effectively tackle technical obstacles across various facets of the smart grid. However, it notes unresolved issues meriting additional research, such as high-performance data processing for informed decision-making in large, intricate multi-energy systems, and lightweight solutions based on machine learning. Additionally, the article spotlights the potential of harnessing progressive computing and communication technologies such as edge computing, pervasive internet of things, and 5G wireless networks within the smart grid framework. Simulating smart grids requires overcoming challenges posed by the integration of diverse subsystems with varying time scales. Co-simulation is employed for these cases. This paper presents the state-of-the-art in simulating intelligent power systems and addresses challenges in applying co-simulation to test cases spanning various domains, including information and communication technology (ICT) and power systems. We anticipate that this study will serve as a launching pad for continued exploration and advancement in this burgeoning area of knowledge and practice. © 2023 IEEE.

关键词： Smart power grids

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Improved detection of Cyber-Attacks Using a Bi-Directional RNN with LSTM Deep Learning Model 3

Improved Detection of Cyber-Attacks Using a Bi-Directional R...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Tomar, Parveen Kumar Kumar, K. Suresh Krishna, Gopal Soumya, K. Ibrahim, Read Khalid Alazzam, Malik Bader Kurukshetra University Department of Electronics & Communication Haryana Thanesar136119 India Panimalar Engineering College Mba Department Tamil Nadu Chennai600123 India Uttaranchal University Uttaranchal Institute of Technology Uttarakhand Dehradun24800 India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Al-Farahidi University Medical Technical College Baghdad10021 Iraq Ajloun National University Information Technology College Ajloun Jordan

ISBN: (纸本)9798350399264

The threat of cyber-attacks is a major concern in today's world, and accurate and efficient detection of these attacks is crucial for ensuring the security of critical systems. Traditional approaches to cyber attack detection have limitations, and therefore, novel methods are necessary to overcome these challenges. However, predicting cyber attacks presents unique difficulties due to the long-range dependency and nonlinearity of attack data. In this study, we propose a deep learning model, called BRNN-LSTM, that uses bi-directional RNNs with LSTM to overcome these challenges and improve the accuracy of cyber attack detection. Our investigation shows that BRNN-LSTM achieves significantly higher prediction accuracy in detecting cyber-attack rates compared to statistical techniques commonly used in the field. The proposed model uses a bi-directional architecture that enables it to capture the dependencies in the input data from both past and future time steps. LSTM cells are employed to capture the long-term dependencies and nonlinearity of attack data. The results show that BRNN-LSTM is an effective model for detecting cyber attacks. The proposed model outperforms traditional statistical methods, and it can capture the complex dependencies in the data that are crucial for the accurate detection of cyber attacks. Therefore, we believe that BRNN-LSTM can be a valuable addition to the existing methods for cyber attack detection and can significantly improve the security of critical systems. © 2023 IEEE.

关键词： Crime

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