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A Novel Hybrid Deep Neural Network for Early Detection and Classification of Chicken Diseases 5

A Novel Hybrid Deep Neural Network for Early Detection and C...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Md Shihab Islam, Md Fakrul Rahman, Sharukh Foysal, Nasir Uddin Islam, Muhammad Nazrul Razzaque, Md. Abdur Al Faisal, Faiz Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka1216 Bangladesh University of Dhaka Dept. of Computer Science and Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350394290

Detection and classification of chicken diseases has been a longstanding concern for farmers. Relying solely on expert opinions could be a costly option. Moreover, there are certain chicken diseases that spread quickly, and necessary procedures are immediately needed to carryon. On the other hand, the lack of research activity or low availability of data on this topic leads to many misunderstandings and challenges so as to correctly detect a certain type of chicken disease. The existing techniques show low accuracy on disease classification, which leads to a complex engineering problem and critical analysis is required for sustainable development. In this paper, we develop a novel hybrid deep neural network, namely ChickenNet21, which combines a Convolutional Neural Network (CNN) and a fine-tuned Visual Geometry Group (VGG) model to address this challenge. Using a large dataset of 6,812 farm-labeled fecal images, we assess the effectiveness of ChickenNet21 and it achieves 98.83% accuracy in identifying chicken diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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A Deep Learning Approach to Analyze the Relationship Between Gender, Height, Weight, and Basal Metabolic Rate from Face Images 2

A Deep Learning Approach to Analyze the Relationship Between...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Siam, Shykul Islam Chowdhury, S.A.H. Ahmed, Nafi Biswas, Swapneel Rajshahi University of Engineering & Technology Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Ahsanullah University of Science & Technology Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798331517984

In response to existing limitations, a novel convolutional neural network (CNN) model tailored for facial attribute estimation is introduced in this study. The methodology encompasses meticulous data preprocessing techniques, including dataset splitting, resizing, cleaning, augmentation, and cropping, to ensure the integrity of the dataset. Through rigorous real-world testing, remarkable accuracy in predicting gender, height, weight, and basal metabolic rate (BMR) from facial photos is demonstrated by our CNN architecture. Notably, unlike conventional approaches where BMR is directly estimated from facial images, three output neurons are employed by our model to simultaneously predict gender, height, and weight, which are sub-sequently utilized for BMR calculation. A primary contribution is made through the development of a comprehensive methodology that advances the reliability of facial attribute estimation by achieving an outstanding accuracy of 98.50% on our dataset named Face-ete and a commendable accuracy of 88.29% on another dataset. Our model outperforms the second-best model by percentage relative improvement factors of 2.19 and 4.28 in terms of BMR prediction accuracy for the 'Faceere' and the other dataset, respectively. This work represents a significant step forward in the field of facial attribute estimation. © 2024 IEEE.

关键词： Convolutional neural networks

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Samarjini - Revolutionizing Household Cleaning with an Affordable IoT-Based Automatic Mopper 3

Samarjini - Revolutionizing Household Cleaning with an Affor...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Joshi, Siddharth Kate, Vandana Bhalerao, Shilpa Tripathi, Prasoon Sharma, Sudhanshu Acropolis Institute of Technology and Research Dept. of Computer Science and Information Technology Indore India

ISBN: (纸本)9798350343274

The title 'Samarjini' draws its inspiration from the Sanskrit word for a cleaning tool, and this research paper introduces the development of an automatic mopper machine designed to streamline household cleaning tasks while maintaining affordability. In a market filled with high-cost alternatives, this IoT-based device stands out by offering autonomous navigation, obstacle detection and avoidance, and effective floor cleaning at a lower price. The paper explores the machine's design, hardware and software components, evaluates its performance in various scenarios, and underscores its relevance in today's fast-paced lifestyles. Extensive experimentation was conducted to assess the machine's performance in diverse environments and scenarios. The outcomes affirm that 'Samarjini' excels in floor cleaning, adept.y avoiding obstacles and autonomously navigating through spaces. © 2023 IEEE.

关键词： Floors

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Foreign Exchange Market Analysis based on Novel Grey Wolf based Extreme Learning Machine Predictor 4

Foreign Exchange Market Analysis based on Novel Grey Wolf ba...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Dash, Swaty Sahu, Pradip Kumar Mishra, Debahuti Veer Surendra Sai University of Technology Burla Dept. of Information Technology Odisha Sambalpur India University Dept. of Computer Science & Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350349856

Foreign exchange trading basically bridges a gap between buyer and seller to transact at a set of prices of the currencies to make profit out of it by the traders and investors. In this paper, foreign exchange predictive models are explored based on basic Extreme Learning Machine (ELM) along with optimized version of ELM. Initially, this work tried to obtain the best optimized ELM network based upon the four nature-inspired optimization methodologies such as;particle swarm, ant colony, moth flame and grey wolf optimization (GWO). From this phase, it has been observed that the ELM network optimized with GWO outperforms rest of other experimented optimized ELM networks with respect to error graphs and performance metrics. In order to evaluate the performance, the ELM-GWO currency exchange predictor, the recognition performance metrics such as;EVS, MAE, MSE and R2 Score are computed, the error graphs are obtained and the average computational time taken are compared. © 2024 IEEE.

关键词： Ant colony optimization

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Unmasking the AI Hand: A Machine Learning Approach to Deciphering Authorship 3

Unmasking the AI Hand: A Machine Learning Approach to Deciph...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Bhandarkar, Abhay Mushtaq Ahmed, D.M. Vishwachetan, D. Mushtaq, Arfaat Kadam, Dhrithi Saxena, Salaj Ramaiah Institute of Technology Dept. of Computer Science and Engg Bangalore54 India Ramaiah Institute of Technology Dept. of Information Science and Engg Bangalore54 India

ISBN: (纸本)9798350381931

In an era of explosive data growth, accurate text analysis tools are paramount. The surge of AI-generated texts presents a pressing challenge: discerning human-written content from AI-generated content. This affects text analysis accuracy, necessitating innovative solutions. We introduce an AI-driven model within the Model-View-Controller (MVC) architecture, categorising text as AI-generated or human-authored. Leveraging supervised learning, the model uses Count Vectorizers and Multinomial Naive Bayes algorithm for training and classification. The proposed model shows a good accuracy of 86.2%. This, with enhanced precision and F1 scores, underscores efficacy in classifying text origins. Beyond its immediate contributions, our model promises to reshape content authenticity verification, from AI-generated social media posts to verifying news sources. The model offers future directions, advancing AI-generated content detection. As AI-generated content evolves, our approach ensures credibility and reliability in text analysis within the MVC framework, addressing the critical need to navigate the AI-generated content landscape. © 2024 IEEE.

关键词： Reliability analysis

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Design of a High Voltage Common Mode Resilient FlexRay Receiver in 180nm/5V CMOS Process 6

Design of a High Voltage Common Mode Resilient FlexRay Recei...

引用

6th Iranian International Conference on Microelectronics, IICM 2024

作者： Mansoury, Hamid Sadat Saeedi, Saeed Atarodi, Mojtaba Sharif University of Technology Dept. of Electrical Engineering Tehran Iran Tarbiat Modares University Dept. of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331511371

A FlexRay receiver that is capable of operating within [-60V, 60V] common mode range using a common mode cancellation method is presented in this paper. The receiver uses 5V MOSFET transistors and incorporates a digital block to reduce the required number of comparators to 2, potentially leading to lower power consumption and die area. Extensive simulations have been conducted to ensure the receiver's functionality within a temperature range of [-40°C, 150°C], which is particularly crucial for automotive applications. Furthermore, corner and mismatch simulations have been performed to ensure the circuit's process tolerance and suitability for mass production. Additionally, Electromagnetic Immunity simulations have been performed to ensure that the receiver, intended for automotive applications, meets the necessary standards. The layout of the receiver occupies an area of 153.6 × 128.7 μm2.. © 2024 IEEE.

关键词： CMOS integrated circuits

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DeepStock Forecast: Unveiling Market Movements through Advanced Deep Learning Models 4

DeepStock Forecast: Unveiling Market Movements through Advan...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Das, Smruti Rekha Mishra, Debahuti Lenka, Arundhati Shaw, Kailash Gitam Deemed to Be University Dept. Computer Science and Engineering Andhra Pradesh Visakhapatnam India Siksha 'o' Anusandhan Deemed to Be University Odisha Dept. Computer Science and Engineering Odisha India Dept. of Aiml Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798350349856

This study uses deep neural network (DNN) methodologies, including auto encoder, deep belief network (DBN), and backpropagation neural network (BPNN), to predict stock prices over 15 and 30 days. Utilizing BSE Sensex and NSE Sensex datasets with diverse technical indicators, the research highlights DNN's superior training accuracy, measured by minimal mean squared error (MSE). The study's key contribution is applying DNN to predict stock market volatility. While acknowledging the limitation of relying solely on training error, the study incorporates additional measures like RMSE, MAPE, MAE, and ARV during testing. Notably, DNN's MAPE results fall within a narrow range (0.0221 to 0.0255), indicating minimal error deviation. In a comprehensive evaluation across all datasets, DNN consistently outperforms DBN and BPNN in both training and testing performance. © 2024 IEEE.

关键词： Deep neural networks

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A Convolution Neural Network Based Classifier for Discerning AI Generated Images and Real Images 4

A Convolution Neural Network Based Classifier for Discerning...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Kumar, Uppala Eswar Basava Vivekreddy, Chintakuntla Sujatha, Kamepalli Vignan's Foundation for Science Technology & Research Deemed to be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350354218

The exponential advancement of artificial intelligence (AI) has led to the creation of synthetic images that closely resemble real-world photographs. However, the widespread distribution of AI-generated content poses significant challenges in distinguishing between genuine and synthetic imagery, particularly in cybersecurity, content moderation, and image forensics. Traditional image classification methods struggle with this task due to the striking visual similarities between the two types of images. Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, leveraging their ability to extract complex features from raw pixel data. In this study, we propose an enhanced CNN methodology to differentiate between AI-generated and real images. Specifically, we focus on three popular CNN architectures: ResNet-50, RexNet-150, and RexNet-200. By augmenting these models with advanced architectural enhancements and training strategies, we aim to improve their discriminative capabilities. Our experimental findings reveal that RexNet-150 surpasses ResNet-50 and RexNet-200, achieving superior accuracy in distinguishing between AI-generated and real images. RexNet-150 attained an impressive 97.9% accuracy on the test dataset, outperforming ResNet-50 (93.1%) and RexNet-200 (90.3%). Moreover, RexNet-150 demonstrated enhanced training convergence and speed, indicating its effectiveness in learning discriminative features for image classification tasks. The superior performance of RexNet-150 holds significant implications for online safety, content moderation, and image authenticity detection. These advancements in CNN technology foster trust in digital imagery and enhance confidence in AI applications across diverse domains. © 2024 IEEE.

关键词： Convolutional neural networks

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Information technology Risk Management Model for Remote Control Vehicles 14

Information Technology Risk Management Model for Remote Cont...

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14th International Conference on Information and Knowledge technology, IKT 2023

作者： Naji, Hamid Reza Ayati, Aref Graduate University of Advanced Technology Dept. of Computer Engineering and Information Technology Kerman Iran

ISBN: (纸本)9798350349412

This paper presents a lightweight and quick IT risk management model for Remote Control Vehicles (RCVs). The increasing use of RCVs in many fields and the existence of different threats are the main reasons for having an efficient risk management method in this field. This paper will cover the information technology risks based on a continuous, iterative, and cyclic flow. This structure is adaptable to agile methods and decreases the chance of losing data, failing any equipment or system, and losing the reputation of the enterprise. Our risk management model is based on qualitative measures and can cover most IT-based risks in this area. © 2023 IEEE.

关键词： Drones

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Denial-of-Service Attack Detection and Mitigation Strategy for Synchrophasor Data Flowing in Power Transmission System 4

Denial-of-Service Attack Detection and Mitigation Strategy f...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Nath, Soumya Ranjan Bhalja, Bhavesh R. Sidhu, Tarlochan S. Indian Institute of Technology Dept. of Electrical Engineering Roorkee India Ontario Tech University Dept. of Electrical and Computer Engineering Oshawa Canada

ISBN: (纸本)9798350383997

The coupling of the legacy power infrastructure with complex computer networks and the integration of communication devices has led to the problem of cyberattacks, which in turn may induce partial/full blackouts. In this regard, an approach based on a Decision Tree for the detection and mitigation of Denial-of-Service (DoS) attacks is proposed in this paper. Based on the patterns and effects observed during DoS attacks on the Synchrophasor data flowing from the Phasor Measurement Unit (PMU) to the Phasor Data Concentrator (PDC), various supervised machine learning models are employed, which classify healthy and attack data and detect DoS attacks. Thereafter, a recurrent deep learning network employing Gated Recurrent Unit (GRU) architecture is utilized to reconstruct the lost data, which in turn is helpful in mitigating the impacts of a DoS attack. The suggested algorithms are validated using a real-world scenario of a communication dataflow between multiple PMUs placed at different buses of a power transmission network and a PDC present at a control center, representing a wide-area network, and are incorporated using Real Time Digital Simulator and openPDC. By comparing various metrics of ML classification, the results indicate that the decision tree based model gives better accuracy along with the least detection time compared to other machine learning-based models. The suggested GRU-based mitigation algorithm can reconstruct the DoS-impacted lost data from the available pre-attack phasors. © 2024 IEEE.

关键词： Phasor measurement units

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