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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Sarkar, Subhasish Kumar, Gautam National Institute of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350365016

Early identification of plant diseases and nutrient deficiencies is crucial for ensuring healthy food production, especially for crops like sugarcane, which contribute significantly to our food supply. Farmers often struggle to differentiate between diseases and deficiencies due to similar symptoms. To overcome such challenges, this study leverages the application of transformers to classify whether the given input is suffering from disease or deficiency. For this purpose, two transformers, i.e., Vision Transformer (ViT) and Swin Transformer is trained on the dataset having leaves of sugarcane. The performance of the model is calculated in terms of precision, recall, accuracy and F-score. From the results, it is found that the Vision transformer with an accuracy of 99.6% can classify diseases and deficiencies more accurately compared to the 99.2% accuracy for Swin transformer. © 2024 IEEE.

关键词： Plant diseases

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Deepfake Detection Using Multi-Modal Fusion Combined with Attention Mechanism 4

Deepfake Detection Using Multi-Modal Fusion Combined with At...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Shirley, C.P. Berin Jeba Jingle, I. Abisha, M.B. Venkatesan, R. Yashvanth Ram, R.V. Elango, Elakkiya Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Government Arts College for Woman Dept. of computer Science Sivaganga India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

The proliferation of deepfake technology poses a significant challenge to the authenticity of digital content. This research explores the application of multimodal fusion techniques to enhance deepfake detection accuracy. By combining visual and audio features, the proposed method leverages the complementary nature of different data types to detect discrepancies introduced by deepfake manipulation. An attention mechanism is incorporated to focus on salient regions within each modality, further improving detection accuracy. Convolutional Neural Networks (CNNs) and Mel-Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction, followed by feature fusion for deepfake detection. This approach demonstrates the effectiveness of multimodal fusion in combating the evolving threat of deepfake technology. By advancing deepfake detection techniques, this research contributes to safeguarding the integrity of digital content and preserving trust in media. © 2024 IEEE.

关键词： Convolutional neural networks

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Intelligent Route Planning for Post-Earthquake Rescues Using IoT 6

Intelligent Route Planning for Post-Earthquake Rescues Using...

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6th International Conference on Electrical Engineering and Information and Communication technology, ICEEICT 2024

作者： Tamzid, Rahat Mahee, Ehfaz Faisal Rahman, Ashikur Tamanna, Tahira Akhtar Rahman, Muntasir Hossain, Md. Mamun Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350385779

Starting rescue operations quickly after an earth-quake is the best way to save lives in such disasters. In the case of large-scale earthquakes, current post-earthquake response systems cannot determine which building or rescue route would save the most lives due to the lack of real-time data availability. Research gaps include a lack of automation in earthquake damage assessment of buildings, location identification, and estimation of the number of trapped people. To fill these gaps, this article proposed an innovative solution using advanced sensors and cloud services for post-earthquake rescue route planning systems. This system aims to improve rescue operations to save the maximum number of lives in the shortest time and minimize impacts by optimizing resource allocation and overall response efficiency. By filling the existing research gap, our system seeks to provide more responsive and effective solutions to post-earthquake emergencies. Tested in a simulated environment with 16 samples, our system successfully collects and processes data from IoT devices deployed in buildings, classifies structural damage, and estimates the number of people affected. Our approach significantly reduces the time required to initiate rescue operations by providing rescuers with optimized routes to disaster sites, which has potential implications for disaster management strategies. © 2024 IEEE.

关键词： Earthquakes

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Multi-Musical Instrument Recognition Neural Network 6

Multi-Musical Instrument Recognition Neural Network

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Timouri, Bardia Aulakh, Ravdeep Cruz, Gathrean Dela British Columbia Institute of Technology Dept. of Computer Science Vancouver Canada

ISBN: (纸本)9798350374162

Artificial intelligence continues to evolve, particularly in the realms of natural language processing (LLMs), image generation, and task automation. Despite these advancements, multi-musical instrument recognition remains a challenging area with limited effective solutions. Addressing this, our research introduces an innovative methodology using a convolutional neural network (CNN) embedded within an artificial neural network framework. This method utilizes the OpenMIC-2018 dataset, meticulously refined for our purposes. We process entire songs by converting them into mel-spectrograms, which are instrumental in distinguishing subtle variations in pitch, dynamics, and timbre. Our approach sets a new benchmark in the field, adept.y identifying and categorizing up to 10 distinct musical instruments within complex audio recordings. It boasts an impressive F1 score of 56%. This significant achievement not only advances audio signal processing but also highlights the versatility and effectiveness of CNNs in handling sophisticated tasks like multi-instrument recognition. Our work serves as a stepping stone for future explorations in audio recognition, potentially paving the way for more nuanced and accurate audio analysis in various applications, from music production to sound engineering. © 2024 IEEE.

关键词： Spectrographs

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A Theoretical Framework for Temporal Graph Warehousing with Applications

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International Journal of Advanced computer Science and Applications 2024年第6期15卷 260-270页

作者： Chou, Annie Y.H. Tseng, Frank S.C. Dept. Computer and Information Science ROC Military Academy ROC Kaohsiung Taiwan Dept. Information Management National Kaohsiung Univ. of Science and Technology ROC Kaohsiung Taiwan

The evolution of data management systems has witnessed a paradigm shift towards dynamic and temporal representations of relationships. Graph databases, positioned as key players in managing highly-connected data with a fundamental requirement for relationship analysis, have recognized the need for incorporating temporal features. These features are crucial for capturing the temporal dynamics inherent in various applications, offering a more comprehensive understanding of relationships over time. This theoretical exploration emphasizes the importance of incorporating temporal dimensions into graph data warehousing for contemporary applications. Temporal features introduce a dynamic dimension to graph data, enabling a more nuanced understanding of relationships and patterns over time. The integration of temporal features in graph data management and analysis not only addresses the dynamic nature of contemporary applications but also contributes to enhanced modeling and analytical capabilities. © (2024), (Science and Information Organization). All Rights Reserved.

关键词： Graph Databases

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Fruit Identification using YOLO v8 and Faster R-CNN -A Commparative Study 2

Fruit Identification using YOLO v8 and Faster R-CNN -A Commp...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sawant, Kedar Shirwaikar, Rudresh D. Ugavekar, Raj Ravindra Chodankar, Chandrakant Shyam Tirodkar, Bhupesh Nilkant Padolkar, Ashwek Gawas, Pramesh Agnel Institute of Technology and Design Dept. Computer Engineering Mapusa India

ISBN: (纸本)9798350382952

Fruits are rich in nutrients, vitamins, and minerals and play a crucial role in both dietary health and a nation's economy. In India, diverse fruit cultivation necessitates efficient identification methods. The manual fruit identification processes are time-consuming and biased, leading to agricultural inefficiencies. Hence, the adoption of machine learning and deep learning has gained immense popularity in the automation of the fruit identification preprocess. This study evaluates the performance of DL models, Faster R-CNN and YOLO v8, on a dataset featuring five fruits namely Apple, Cashew Apple, Banana, Mango and Orange for identifying fruits with the comparison between them. While both models yield impressive results, Faster R-CNN generally exhibits higher mAP. However, YOLO v8 excels in precision for specific fruits like Cashew Apple and Banana. The choice between models involves a trade-off between mAP and precision, emphasizing the need for further optimization considering factors like dataset variations, computational performance, and real-world applicability. © 2024 IEEE.

关键词： Fruits

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From Data to Harvest through Machine Learning based Crop Yield Forecasting 2

From Data to Harvest through Machine Learning based Crop Yie...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Budarapu, Aadithya Vikram Gaddam, John Hyde Boyedi, Ramu Muthyala, Thoshan Kumer Kathirisetty, Nikhila Dept. of Information Technology Hyderabad India Dept. of Computer Science and Data Science Hyderabad India

ISBN: (纸本)9798350370188

The maintenance and enhancement of dynamic soil characteristics are the primary focus of soil management in agriculture to increase crop productivity. Higher productivity may result from efficient soil control of resources and corrective micronutrient treatments. Using CNN and 'KNN' algorithms, the 'soil land classification and crop prediction system' application was created. In this study, two datasets are used: one to obtain crop prediction and the other for soil land categorization. The kind of soils are trained using CNN ('VGG- 19') algorithm, and the accuracy of the model is calculated. The trained model is then utilized in the Flask web app to forecast the type of soil. Another data set with nitrogen, phosphorus, potassium, pH, and temperature as features and the class type of crop as a label is used to forecast crop production. These two algorithms are used to create the flask website, which accepts inputs such as soil picture, soil type prediction, and land parameter inputs for crop prediction. © 2024 IEEE.

关键词： Soils

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Using Game Theory to Prioritize Goals in Requirements Engineering 4

Using Game Theory to Prioritize Goals in Requirements Engine...

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4th International Conference on Next Generation Computing Applications, NextComp 2024

作者： Munthali, Isaac Kankuzi, Bennett Malawi University of Science and Technology Dept. of Computer Science & IT Thyolo Malawi

ISBN: (纸本)9798331517403

Prioritizing competing goals among different stakeholders in requirements engineering is usually a daunting task for system architects as each of the stakeholders have their own interests. This study modeled this conflicting environment among stakeholders as a non-cooperative game in game theory with goals modeled as strategies and utility value of each goal as pay-offs in a pay-off matrix. In a user study that employed the NashPy algorithm to deduce the Nash equilibrium of the strategies in a two player game, the tool was able to automatically deduce goals which needed to be prioritized to achieve a win-win situation for two stakeholders. This was also confirmed by stakeholders. The paper has three contributions: modelling goals for stakeholders in requirements engineering as a non-cooperative game in game theory;automatically deducing goals that need prioritization among stakeholders in requirements engineering based on the Nash equilibrium;and performing a user study to validate the deduced prioritized goals. © 2024 IEEE.

关键词： Requirements engineering

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Grey Wolf Optimizer, Ant Colony Optimization, and Quantum-Based Optimization for Document Retrieval: A Comparative Analysis with FLICM and FDMO 3

Grey Wolf Optimizer, Ant Colony Optimization, and Quantum-Ba...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Chandwani, Gunjan Narender Chaudhary, Meena Manav Rachna Uiversity Dept. of Computer Science & Technology Faridabad India

ISBN: (纸本)9798331528201

The relevancy and accuracy of document retrieval systems are essential for information retrieval tasks, particularly in large-scale and high-dimensional datasets. This study explores the integration of Grey Wolf Optimizer (GWO), Ant Colony Optimization (ACO), and Quantum-based optimization into a Fuzzy Local Information C-Means (FLICM) clustering framework for enhancing deep learning-based document retrieval systems. By correlating with recent advancements in Fractional Dwarf Mongoose Optimization (FDMO) for document retrieval, we investigate how these alternative optimizers affect system performance in terms of precision, recall, and computational efficiency. © 2024 IEEE.

关键词： Ant colony optimization

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Performance Analysis of a Scalable Federated Learning-Based Agricultural Network 4

Performance Analysis of a Scalable Federated Learning-Based ...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Chakraborty, Shatanik Sarkar, Maharnab Gayen, Avijit Jana, Angshuman Chakraborty, Nilotpal Meghnad Saha Institute of Technology Dept. of Computer Science & Engg. Kolkata India Govt. College Of Engineering & Textile Technology Dept. of Computer Science & Engg. Berhampore India Indian Institute of Information Technology Dept. of Computer Science & Engg. Guwahati India Techno India University Dept. of Computer Science & Engg. West Bengal India

ISBN: (纸本)9798350387063

Although agriculture is a substantial part of the Indian economy, it encounters several challenges. Precision agri-culture is a farming management strategy that aims to overcome these challenges. It optimizes several components of the farming process using data and technology, resulting in increased crop yields while reducing waste and raising total resource use effi-ciency. Thus, in its context, we present a study on the development of a Federated Learning-based Agricultural Network consisting of a popular federated learning algorithm FedAvg to perform image classification on an open dataset, Plant Village. In this work, we specifically analyze the performance of the proposed Federated Learning-based Agricultural Network in terms of its scalable deployment. The proposed network yields an average classification accuracy of 97.52 % on test data (C ≈ 70), and provides a host of other benefits it derives from its federated nature, such as upholding the privacy of local client data as only the model updates are shared between the clients and the aggregation server. The prime objective of our work has been to study and explore the possibility of applying federated learning in the agriculture sector so that more efficient and privacy- preserving smart farming networks could be developed. © 2024 IEEE.

关键词： Agribusiness

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