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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

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Homomorphic Encryption for Privacy Preservation in Occupancy Sensor-Based Smart Lighting 7

Homomorphic Encryption for Privacy Preservation in Occupancy...

引用

7th international conference on data science and Its Applications, ICoDSA 2024

作者： Putrada, Aji Gautama Abdurohman, Maman Perdana, Doan Nuha, Hilal Hudan School of Computing Telkom University Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350365351

Occupancy sensor-based smart lighting is very practical because the light status can change automatically according to human activity under the lighting area. However, on the other hand, the user occupancy displayed by the occupancy sensor is sensitive personal information. Our research aim is to apply homomorphic encryption to occupancy sensor data in smart lighting and evaluate its performance. In the first step, we obtain occupancy data for smart lighting using classification integrated moving averages (CIMA). We then design and apply homomorphic encryption to the data. We use three machine learning models to test the data utility of this method: k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and decision trees. Meanwhile, for the privacy preservation benchmark, we use differential privacy. Several metrics to measure the performance of this privacy-preservation mechanism are information loss, entropy, kernel density estimation (KDE), Kullback-Leibler (KL) divergence, and Earth mover's distance (EMD). The test results show that AdaBoost provides the lowest information loss compared to the other two methods, namely 6%. This shows that homomorphic encryption can maintain data utility. Compared with differential privacy, homomorphic encryption has a KDE with low kurtosis. Then, the KL divergence, EMD, and average entropy values of homomorphic encryption are also lower than those of differential privacy. Homomorphic encryption provides optimum privacy preservation in occupancy sensor-based smart lighting. © 2024 IEEE.

关键词： Differential privacy

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Enhancing E-learning Materials Recommendations with Contextual Awareness and Collaborative Filtering 6

Enhancing E-Learning Materials Recommendations with Contextu...

引用

6th international conference on computing and Informatics, ICCI 2024

作者： Anter, Ahmed Moawad, Ibrahim F. Mosaad, Sara M. Faculty of Commerce and Business Administration Helwan University Cairo Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt

ISBN: (纸本)9798350373875

The number of learners using online learning materials has significantly increased in recent years. Due to the overload of information, many learners face difficulty finding relevant and useful educational materials that meet their needs. To solve this problem, recommendation systems (RS) are widely relied upon. These systems are used to provide suitable recommendations to users based on their needs. However, traditional recommendation techniques such as collaborative filtering (CF) and content-based filtering (CB) solely rely on user ratings and content attributes. Therefore, it is imperative to incorporate the learner's context when it comes to precise and personalized recommendations in e-learning. These conventional methods often neglect the learner's context when computing similarities and making recommendations, resulting in less accurate suggestions. This paper proposes a learning materials recommendation approach by integrating context awareness with collaborative filtering (CF) algorithms. Context awareness in the proposed approach entails incorporating context-specific data, like the learner's knowledge level and preferred learning style. Collaborative filtering computes predictions and generates targeted learner recommendations based on contextualized data. Evaluating the proposed recommendation approach (CF-CA) involved experiments comparing it with traditional collaborative filtering (CF) on a dataset of 349 learners. The two-recommendation technique's performance varied with neighbors (5 to 50). The proposed method demonstrated a 0.21 MAE improvement at an optimal size of 5. Furthermore, the proposed approach (CA-CF) consistently outperforms the CF in precision and recall across varying recommendation volumes. Additionally, by utilizing the learner's contextual data, the proposed method helps alleviate the cold-start issue. © 2024 IEEE.

关键词： Recommender systems

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TFGAN: Transformer-based Melody Generate GAN 9

TFGAN: Transformer-based Melody Generate GAN

引用

9th international conference on Image, Vision and computing, ICIVC 2024

作者： Zhang, Liming Shi, Xiuhang Lin, Zicheng Yang, Hao Wang, Songbo Hu, Jianqiao Emerging Risk Research Insitute North China University of Technology Beijing China School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350385991

With the development of deep learning technology, using deep learning to automatically compose music for given lyrics has gradually become one of the research hotspots. In the research work on this task, the method of using generative adversarial networks(GANs) as the basic model can achieve better results and can generate audio data based on lyrics data. However, the current popular methods of using GANs for lyric-based music composition suffer from the insufficient vividness of the generated music and the homogeneity of the generated notes. To solve this problem, we propose TFGAN, an automatic orchestration method based on GAN. In our method, a dual discriminator structure is used to assess the vividness and realism of the generated audio. Additionally, we utilize a generator based on the transformer architecture. Our method takes lyric information as input and outputs musical details such as pitch information. This output can be converted into music in MIDI format and played. Through experimental validation, our proposed method has been demonstrated to exhibit a larger audio distribution in generating musical melodies compared to other methods and it closely resembles a real audio distribution. Additionally, the songs composed of these generated notes also sound more authentic in performance. © 2024 IEEE.

关键词： Deep learning

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Disease Detection And Severity Prediction Of Pomegranate Fruit 15

Disease Detection And Severity Prediction Of Pomegranate Fru...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Paul, Poulin Manishankar, S. Satheesh Kumar, R. Sahrdaya College of Engineering and Technology Dept. of Computer Science and Engineering Kerala Thrissur India Jawaharlal College of Engineering and Technology Dept. of Computer Science and Engineering Kerala Palakkad India

ISBN: (纸本)9798350370249

This paper presents an integrated framework for the comprehensive analysis of diseases affecting pomegranate fruit. The suggested system, which makes use of deep learning techniques, includes semantic segmentation for diseased region localization, multiclass illness classification, and image processing-based severity assessment. Initially, four common diseases - Alternaria, Anthracnose, Bacterial Blight, and Cer-cospora - are successfully identified through the use of a Convolutional Neural Network (CNN) for multiclass classification. Then, annotated photos are used as training data for Semantic Segmentation, which uses the UNet architecture to identify the unhealthy spots within the fruit. The area and percentage of the segmented region are determined, and thresholds for high, medium, and low severity levels are then defined. The segmentation results are then used as inputs for severity estimate. Early identification and intervention tactics are made easier by the severity level prediction, which is based on the proportion of the segmented region. In order to improve pomegranate fruit management methods, the suggested framework provides a thorough method of disease analysis by combining categorization, semantic segmentation, and severity prediction. © 2024 IEEE.

关键词： Annotation Convolutional Neural Networks Deep learning Semantic segmentation Unet

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Artificial Intelligence Based Sugarcane Leaf Disease Prediction System for Smart Farming 7

Artificial Intelligence Based Sugarcane Leaf Disease Predict...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Chauhan, Yash Negi, Nitam Sharma, Sachin Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350372816

In response to the challenges posed by sugarcane diseases, this research introduces a Sugarcane Disease Prediction System, employing deep learning through a Convolutional Neural Network (CNN) using TensorFlow and Keras. The CNN classifies sugarcane images into Bacterial Blight, Healthy, or Red Rot categories. The model is trained on a diverse dataset, employing data augmentation for enhanced generalization. Integrated into a Flask web application, the model enables users to upload sugarcane images, receiving disease predictions and confidence scores. This user-friendly tool aids farmers and researchers in early disease detection, contributing to precision agriculture. The system's efficacy is showcased through evaluations and visualizations of metrics, providing an automated solution for sugarcane disease prediction. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of Comparison Faster R-CNN Backbone for Oil Palm Tree Counting using Drone Imagery data

Analysis of Comparison Faster R-CNN Backbone for Oil Palm Tr...

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2024 international conference on Information Technology and computing, ICITCOM 2024

作者： Sri Rejeki, Dewi Irwansyah, Edy Golda, Beauty School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Representative Indonesia of the International Business Group SuperMap Software Co. Ltd Business Development Beijing China

ISBN: (纸本)9798350379839

The palm oil industry is an important subsector in the Indonesian economy. Counting the number of oil palm trees using drone imagery is crucial in developing efficient strategies for managing oil palm plantations. This research aims to compare the effectiveness of two Faster R-CNN models with ResNet50 and VGG16 backbones for detecting and counting the number of oil palm trees in high-resolution images captured by drones at PTPN IV's oil palm plantations in Gunung Banyu, North Sumatra. The model was trained and tested, and its performance was measured regarding recall, accuracy, and F1 Score. The results of the comparative analysis show that the Faster R-CNN model with the VGG 16 backbone shows high accuracy, namely between 97-99%, while the results for the ResNet50 backbone do not exceed 95%. These accuracy results may be due to the VGG16 architecture being simpler and suited to the data set used. At the same time, ResNet50 requires better parameter adjustments or more significant data to achieve optimal performance. This study is relevant for choosing the right Faster R-CNN backbone to help count oil palm trees in managing plantations better and more efficiently. © 2024 IEEE.

关键词： Drones

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Knowledge Graphs Can be Learned with Just Intersection Features 41

Knowledge Graphs Can be Learned with Just Intersection Featu...

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41st international conference on machine learning, ICML 2024

作者： Le, Duy Zhong, Shaochen Liu, Zirui Xu, Shuai Chaudhary, Vipin Zhou, Kaixiong Xu, Zhaozhuo Department of Computer and Data Sciences Case Western Reserve University United States Department of Computer Science Rice University United States Department of Electrical and Computer Engineering North Carolina State University United States Department of Computer Science Stevens Institute of Technology United States

Knowledge Graphs (KGs) are potent frameworks for knowledge representation and reasoning. Nevertheless, KGs are inherently incomplete, leaving numerous uncharted relationships and facts awaiting discovery. Deep learning methodologies have proven effective in enhancing KG completion by framing it as a link prediction task, where the goal is to discern the validity of a triple comprising a head, relation, and tail. The significance of structural information in assessing the validity of a triple within a KG is well-established. However, quantifying this structural information poses a challenge. We need to pinpoint the metric that encapsulates the structural information of a triple and smoothly incorporate this metric into the link prediction learning process. In this study, we recognize the critical importance of the intersection among the k-hop neighborhoods of the head, relation, and tail when determining the validity of a triple. To address this, we introduce a novel randomized algorithm designed to efficiently generate intersection features for candidate triples. Our experimental results demonstrate that a straightforward fully-connected network leveraging these intersection features can surpass the performance of established KG embedding models and even outperform graph neural network baselines. Additionally, we highlight the substantial training time efficiency gains achieved by our network trained on intersection features. Copyright 2024 by the author(s)

关键词： Knowledge graph

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A Deep learning Approach for Herbal Plant Detection and Recognition 3

A Deep Learning Approach for Herbal Plant Detection and Reco...

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Giridharan, N. Aaffrin Sulthana, R. Mohanraj, R. Sanjay Murugan, M. K.S.Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350375190

In this research, TensorFlow, a powerful deep learning toolkit, is utilized in conjunction with the Python programming language to conduct a comprehensive analysis of plant names and specifications. The research involves compiling a diverse dataset containing various plant species and their corresponding characteristics. Prior to processing, the dataset is meticulously cleaned and standardized to ensure consistency. By leveraging TensorFlow and natural language processing techniques, features are extracted from the plant names, enabling the identification of patterns and relationships. Deep learning models based on TensorFlow are then trained to recognize these patterns and associate them with specific plant attributes. The inclusion of a user-friendly interface facilitates seamless communication and allows users to input plant names for analysis. The research aims to enhance the understanding of plant taxonomy by providing valuable insights with implications for fields such as botany, agriculture, and pharmaceutical research. The project focuses on creating a robust herbal plant detection system using Convolutional Neural Networks (CNNs) with a target accuracy of 90%. CNNs enable precise herbal plant classification through automatic feature extraction from input. © 2024 IEEE.

关键词： Feature extraction

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Evaluating data Poisoning Vulnerability in Selective Reincarnation within c-MARL to Salt and Pepper Noise Attack 15

Evaluating Data Poisoning Vulnerability in Selective Reincar...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Putla, Harsha Patibandla, Chanakya Singh, Krishna Pratap Nagabhushan, P. Department of IT IIIT - Allahabad Prayagraj India HOD - Department of IT IIIT - Allahabad Prayagraj India Vignan’s Foundation for Science Technology & Research Guntur India

ISBN: (纸本)9798350370249

In the domain of cooperative multi-agent reinforcement learning (c-MARL), the deployment in safety-critical applications necessitates rigorous robustness testing. Despite the advancements in c-MARL algorithms facilitated by deep neural networks (DNN), the susceptibility of agent policies to adversarial examples poses significant risks. This study focuses on the susceptibility of selective reincarnation, a method leveraging historical agent experiences to expedite learning, in c-MARL systems against data poisoning attack targeting the offline teacher dataset, which consists of curated experiences collected during tabula rasa training. We tested the influence of salt and pepper noise on selective reincarnation and employed Hamming distance to quantify ranking disruptions. Our results reveal significant performance declines, with top-performing agent combinations (configurations) under clean conditions experiencing up to a 50% decrease in performance under adversarial conditions. Furthermore, configurations with three or more agents exhibited a 100% Hamming distance, signaling complete ranking reversals that critically affect reincarnation decisions. Our research not only enhances the understanding of the effects of data poisoning on c-MARL selective reincarnation but also contributes to the advancement of more secure multi-agent learning algorithms, ensuring robustness in critical applications. ©2024 IEEE.

关键词： Salt and pepper noise

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AI-Based Methods to Detect and Counter Cyber Threats in Cloud Environments to Strengthen Cloud Security

AI-Based Methods to Detect and Counter Cyber Threats in Clou...

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2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Chaudhary, Diwakar Verma, S.K. Mohan Shrimal, Vijay Madala, Ravikiran Baliyan, Rashi Sathish, M. Noida International University School of Business Management Greater Noida India Chandigarh University Department of Computer Science Engineering Punjab Mohali India University of Cumberlands Department of Information North Carolina Durham United States Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350378092

Traditional cloud security solutions often employ outdated techniques and find it difficult to stay up to date with the ever-evolving cyber threat scenario. Complex attacks may be made against cloud systems since static signatures and rule-based methods have their limits. To address these problems, the study proposes a novel AI-based approach to enhance cyber threat detection and mitigation in cloud settings. By use of sophisticated anomaly detection and machine learning (ML) methods, the proposed system continuously analyzes large amounts of data to dynamically identify and eliminate new threats. Some of the key components include robust preprocessing, a variety of ML model selections, repetitive model training, real-time threat detection, and automated response mechanisms. Compared to the current system, the results demonstrate notable increases in detection accuracy, false positive and false negative rates, and area under the curve (AUC) values across many test datasets. Specifically, the proposed system reduces the false negative and false positive rates to 2.0% and 1.2%, respectively, and achieves 98.5% detection accuracy as compared to 89.7% for the existing system. Moreover, on several test datasets, the proposed system routinely produces higher AUC values than the existing system, proving its efficiency in improving the accuracy of cyber threat identification in cloud systems. © 2024 IEEE.

关键词： Cloud security

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