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共 36234 条记录，以下是421-430 订阅

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A Framework for Personality Prediction for E-Recruitment Using Machine Learning Algorithms 14

A Framework for Personality Prediction for E-Recruitment Usi...

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Thapa, Lakshyajit Pandey, Aniket Gupta, Deepanshu Deep, Ayush Garg, Rakesh Amity University Dept. of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350344837

With rapid advancements in technology, the process of recruitment has evolved from traditional methods to online and automated methods known as e-recruitment. While e-recruitment streamlines the initial screening process, assessing the personality and suitability of candidates continues to remain a challenge. In this paper, we propose a framework for online personality prediction to aid the e-recruitment process. The framework utilizes machine learning algorithms to analyze user data from multiple online sources like resumes, social media profiles, and responses to psychometric tests, and predict their personality type based on established psychological theories. Four popular personality prediction models - Big Five, Myers Briggs, HEXACO, and Enneagram are discussed. State-of-the-art natural language processing and deep learning techniques like BERT are explored for feature extraction from text data. Popular supervised machine learning algorithms like logistic regression, Naive Bayes, k-nearest neighbors, support vector machines, random forest, XGBoost, and LSTM are compared to build predictive models. The proposed framework can help recruiters shortlist candidates better matched to job requirements, leading to increased hiring satisfaction and productivity. © 2024 IEEE.

关键词： Forecasting

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Streamlining Livestock Surveillance: YOLOv8 Approach to Cattle Body Segmentation

Streamlining Livestock Surveillance: YOLOv8 Approach to Catt...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Chugh, Bharti Kumar, Pushepndra Mishra, Upendra Chaudhary, Himanshi Kiet Group of Institutions Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350375237

This research paper introduces an innovative approach to optimize livestock surveillance by employing YOLOv8 for cattle body segmentation. The study attained a mAP(50), mean Average Precision of 0.598 and 0.464 and mAP(50-95) on a dataset comprising 704 images. Preceding model training, extensive preprocessing and image augmentation techniques were implemented, resulting in a dataset of 1476 training, 171 validation, and 71 testing images. The developed model demonstrates considerable promise in enabling real-time mon-itoring of cattle and their health status. Additionally, to enhance its suitability for edge computing environments, quantization-aware training (QAT) abd weight compression techniques were employed, ensuring efficient deployment on resource-constrained devices. The results highlight the effectiveness and practicality of employing YOLOv8-based segmentation for livestock surveillance, with notable implications for precision agriculture and animal welfare. However, further enhancements are warranted, particularly in achieving higher mAP scores across a broader range of Intersection over Union (IoU) thresholds, to fully address the segmentation challenges in this domain. © 2024 IEEE.

关键词： computer vision

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Deep Learning for Tea Leaf Disease Detection: Comparing VGG16, Vision Transformer, EfficientNetB3, and Xception 15

Deep Learning for Tea Leaf Disease Detection: Comparing VGG1...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ayon, Rokonozzaman Mojumdar, Mayen Uddin Mubin, Arif Shahriar Chakraborty, Narayan Ranjan Hridoy, Al-Amin Gazi Md. Jahidul, Islam Dept. of Computer Science & Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9798350370249

The need for efficient disease management in crop production is highlighted by the economic relevance of agriculture. Various leaf diseases cause major losses for tea, a staple product in many places. In this paper, a deep learning-based method is provided for the automated identification of illnesses affecting tea leaves. Using the unique architectural benefits of four models: VGG16, ViT (Vision Transformer), EfficientNetB3, and Xception, their performance is examined. Each model's accuracy and effectiveness in classifying diseases are evaluated by conducting a thorough series of experiments on a dataset of photos of tea leaves. The results show that ViT and VGG16 perform better than expected, with accuracies of 96.09% and 98.29%, respectively. These results highlight the effectiveness of transformer-based designs and conventional CNN in the identification of tea leaf disease. Our research also demonstrates how deep learning approaches can improve agricultural practices by enabling early and accurate disease identification, which would facilitate proactive disease control tactics and increase crop output. ©2024 IEEE.

关键词： Plant diseases

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Identification of Sugarcane Leaf Diseases and Deficiency Disorders Using Transformers 1

Identification of Sugarcane Leaf Diseases and Deficiency Dis...

引用

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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TraceChainBD: Securing Supply Chains from Counterfeiting in Bangladesh with Blockchain and IoT Integration

TraceChainBD: Securing Supply Chains from Counterfeiting in ...

引用

2024 International Conference on Innovations in science, engineering and Technology, ICISET 2024

作者： Karmoker, Sagar Khan, Md. Adib Lamia, Sanjida Alam Tanjid, Mobinul Ahasan Dihan, Shahriar Sadman Anik, Nur Adnan Chowdhury East West University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350355499

The scheme uses blockchain technology in Bangladesh's supply chain to eliminate counterfeit products, improve tax compliance, and build consumer trust. By ensuring product authenticity, it safeguards brand reputations and boosts government revenue. Manufacturers, distributors, and retailers can enter product data which will then generate QR codes. In case of receiving the product or checking its authenticity, they can scan these QR codes via their mobile or a dedicated IOT device and all the information will be stored in Blockchain. Since blockchain data is secure and immutable, it cannot be altered, consumers can easily check product details by scanning QR codes, and if a product is flagged as suspicious, the Mobile Court Management System can trace its origin. This blockchain-based approach ultimately helps prevent counterfeit goods from entering the market and enhances the government's ability to collect taxes. © 2024 IEEE.

关键词： Blockchain

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The Tree Autoencoder Model, with Application to Hierarchical Data Visualization 38

The Tree Autoencoder Model, with Application to Hierarchical...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Carreira-Perpiñán, Miguel Á. Gazizov, Kuat Dept. of Computer Science and Engineering University of California Merced United States

We propose a new model for dimensionality reduction, the PCA tree, which works like a regular autoencoder, having explicit projection and reconstruction mappings. The projection is effected by a sparse oblique tree, having hard, hyperplane splits using few features and linear leaves. The reconstruction mapping is a set of local linear mappings. Thus, rather than producing a global map as in t-SNE and other methods, which often leads to distortions, it produces a hierarchical set of local PCAs. The use of a sparse oblique tree and of PCA in its leaves makes the overall model interpretable and very fast to project or reconstruct new points. Joint optimization of all the parameters in the tree is a nonconvex nondifferentiable problem. We propose an algorithm that is guaranteed to decrease the error monotonically and which scales to large datasets without any approximation. In experiments, we show PCA trees are able to identify a wealth of low-dimensional and cluster structure in image and document datasets. © 2024 Neural information processing systems foundation. All rights reserved.

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Multiple language Discovery Abuse of Cyberspace with Advanced Transformer Architecture 2

Multiple language Discovery Abuse of Cyberspace with Advance...

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Singh, Arvind Tripathi, Abhinandan Buddha Institute of Technology Dept. of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

The number of reported instances of cyber abuse has also increased in tandem with the growth in the quantity of people that are regularly using the internet. Users' online freedom and privacy are jeopardized by such occurrences. Traditionally, the removal of such content from the internet has been accomplished through manual moderation and reporting systems. This strategy has, however, certain drawbacks, such as a reliance on people, longer delays, and less privacy for data. Traditional recurrent sequence models and supervised machine learning have been used in previous attempts to automate this procedure, but they have a tendency to perform badly on non-English material. A versatile solution that can handle multilingual text is urgently needed, given the increasing diversity of consumers using the internet. Moreover, translations frequently lose important context and emotion from content written in vernacular languages. In this work, we offer a generative deep learning based method for the detection of cyber abuse throughout Hindi, English with mixed codes, and Hindi text. The method uses bidirectional transformer-based BERT *** the TRAC-1 standard aggressiveness identification task, the suggested architecture can get extremely positive outcomes on the task leaderboard for English and accomplish cutting-edge performance on the Hindi dataset with mixed codes. The outcomes show that the new strategy is superior to the current ones because they were obtained without the need of numerous models or ensemble-based methods. Effective models that utilize deep learning techniques to analyze multilingual text can process a wider variety of inputs, making them indispensable in combating these types of social ills. © 2024 IEEE.

关键词： Generative adversarial networks

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A Random Forest Framework for Predicting Cardiovascular Disease in Diverse Populations' 2

A Random Forest Framework for Predicting Cardiovascular Dise...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Rahi, Parvez Kang, Sandeep Singh Singh, Ajay Pal Singh, Inderjeet Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798331519056

According to the latest information from WHO, the World Health Organization, heart disease remains a major global health challenge, resulting in millions of deaths each year. Heart disease presents in various forms, including Hypertensive Heart Disease, Congestive Heart Failure (CHF), Coronary Artery Disease, Cardiomyopathy, and Arrhythmia. Accurate classification of these conditions is crucial for reducing mortality rates and improving patient outcomes. This study utilizes an enhanced Random Forest classifier to improve the accuracy of heart disease classification. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to rectify the disparities in class. and Polynomial Feature engineering was employed to capture complex interactions within the data. The dataset underwent comprehensive preprocessing, including feature normalization and label encoding, followed by extensive hyperparameter tuning using GridSearchCV. The optimized model achieved a high accuracy of 92% and a Mean Squared Error (MSE) of 0.05. Key performance indicators, including an F1 score of 0.87, recall of 0.88, and accuracy of 0.92, highlight the model's efficacy in differentiating between various forms of heart disease. Future research could explore incorporating additional clinical data to further enhance the model's accuracy and applicability across diverse patient populations. © 2024 IEEE.

关键词： Clinical research

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A High Throughput Bloom Filter Based TCAM Architecture for Fast NIDS

A High Throughput Bloom Filter Based TCAM Architecture for F...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Shaik, Sameera Noor Mahammad, S.K. IIITDM Kancheepuram Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331528126

One of the computationally most intensive tasks of an Network Intrusion Detection System (NIDS) is string/pattern matching where the payload of incoming packet is compared against predefined rule-set in order to detect any intrusion or threat. Although, various software algorithms and hardware architectures have been proposed to accelerate the performance of NIDS, wire-speed matching of these patterns still remains as a challenge. TCAMs are popularly used in many networking applications as they can perform the pattern matching in single clock cycle, but they always suffer with high power dissipation and delay due to their complex circuitry. Hence, most of the research focuses on efficient utilization of TCAM to cope up with high-speed links. In this paper, a high-throughput TCAM based architecture employed with Bloom filters is proposed. When a Bloom filter is deployed with TCAM, the number of patterns to be matched on TCAM reduces significantly since most of the patterns are eliminated in the prefiltering stage. There exist various Bloom filters such as Standard Bloom filter [1], Cuckoo filters [2], Counting Bloom filters [3] and Compressed filters [4]. Our proposed variant of Bloom filter outperforms all the existing filters in terms of speed while using less memory. We performed experiments on widely used open-source Snort NIDS and the proposed approach is able to achieve a throughput of 128Gbps for version 3.0c and 77.5Gbps for version 2.9.17. © 2024 IEEE.

关键词： Open source software

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DynaCache: A Checkpoint Aware Reconfigurable Cache for Intermittently Powered Computing Systems 32

DynaCache: A Checkpoint Aware Reconfigurable Cache for Inter...

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32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Mahanta, Rishabh Kapoor, Hemangee K. Indian Institute of Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331539672

Energy harvesting devices are rapidly evolving to rival battery-backed technologies. Batteries have a shorter life- time and need maintenance compared to capacitors. Moreover, the usage of batteries comes with undeniable environmental costs. Distinct challenges have emerged such as performance enhancement, crash consistency of data, energy management, etc. To tackle these challenges we propose DynaCache: A checkpoint-aware reconfigurable cache for Intermittent powered computing systems. It is an intermittent computing architecture consisting of a reconfigurable L1 cache that dynamically trans- forms its write policy to strike a favorable trade-off between per- formance, data consistency, and forward progress of a program. Our proposal achieves a speedup in performance of 1.85x and 1.5x compared to non-volatile write-back cache and volatile write- through cache respectively. It also achieves a 99% reduction in dirty data compared to a volatile write-back cache guaranteeing a near absolute data consistency in a scenario of a power failure. © 2024 IEEE.

关键词： Cache memory

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