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27th International Conference on information technology, IT 2023

作者： Malallah, Fahad Layth Abduljabbar, Ali I. Shareef, Baraa T. Al-Janaby, Ali Othman Ninevah University College of Electronics Engineering Computer and Information Engineering Department Mosul41002 Iraq Ninevah University Computer Center Mosul41002 Iraq Ahlia University Department of Information Technology Collage of Information Technology Manama Bahrain Ninevah University College of Electronics Engineering Communication Department Mosul41002 Iraq

ISBN: (纸本)9798350397512

Nowadays, storing confidential documents using cloud services is getting popular due to advantages regarding security and fast processing operations. One of the major services of cloud computing is cloud storage, which is conversely raises some issues regarding information security. Storing information for cooperation such as a bank in the cloud needs a fast processing operation to avoid any bottleneck in the work procedure. Therefore, Quick Response (QR) code is highly required to be utilized in these operations. The advantage of the QR code is easier for reading information by just scanning using any reader available on smart devices. The methodology is to integrate the security with QR code to save the text bank information. This is done by converting text to the QR code as an image form, then applying a cryptography algorithm to the QR code image then upload it to the cloud storage. Later on, once the plaintext is required to be reconstructed, the same operations are applied exactly to the encryption phase. In which the same algorithm will be applied for decryption then a QR code reader is used to preview the target bank text. For security matters, a key is used to be embedded in the protection operation. The type of encryption is proposed to be a symmetric One-time-pad (OTP) cryptography algorithm. © 2023 IEEE.

关键词： Cloud storage

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Multi-server assisted data sharing supporting secure deduplication for metaverse healthcare systems

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Future Generation computer Systems 2023年 140卷 299-310页

作者： Zhang, Tao Shen, Jian Lai, Chin-Feng Ji, Sai Ren, Yongjun School of Computer Science Nanjing University of Information Science & Technology China School of Information Science and Technology Zhejiang Sci-Tech University China Department of Engineering Science National Cheng Kung University Taiwan Taizhou University China

The integration of Metaverse and healthcare will improve the allocation and utilization of healthcare resources. Metaverse healthcare data is stored on a public server and downloaded using Artificial Intelligence devices. Patients are given a diagnosis promptly through Metaverse healthcare systems. However, storing Metaverse healthcare data on public servers leads to a variety of problems, such as leakage of patient health information and loss of vital medical record data. Attribute-based encryption (ABE) has played an important role in securely and efficiently Metaverse healthcare data sharing. In this paper, a feature-rich and efficient ABE-based Metaverse healthcare data sharing scheme is presented, which realizes constant encryption computation overhead according to a multi-server structure. To reduce the number of invalid ciphertexts and achieve secure deduplication, Metaverse healthcare ciphertext validity and equivalence detection are also provided in this scheme. Furthermore, a novel attribute-based re-encryption is proposed for authority delegation after deduplication. Finally, the experiment simulated on the PBC library shows that the proposed scheme possesses good performance. The freshness of Metaverse healthcare data and the utilization of the medical center's private server are ensured in this paper. © 2022 Elsevier B.V.

关键词： Diagnosis

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The Smart Predictive Network Maintenance Model using AI/ML Time Series Analysis in Cloud Networks

The Smart Predictive Network Maintenance Model using AI/ML T...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Prabha, S. Upadhyay, Ramakant Goswami, Pankaj Kumar Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

The ever-growing amount of facts generated from cloud networks requires a new predictive version for community renovation. System learning (ML) and synthetic Intelligence (AI) have been carried out to improve the accuracy of predictive fashions for community maintenance. This paper presents a new method of using time series evaluation with ML and AI to design a clever, predictive community protection model for cloud networks. The proposed version is based on finding patterns and correlations from ancient statistics using AI and ML techniques for time collection analysis. This version uses various features, including packet loss, latency, throughput, unique network popularity metrics, CPU utilization, memory, disk space, and community traffic. The model can detect anomalous behaviors inside the community in early degrees and may provide predictions of destiny network activities. The proposed version can additionally conform to converting network situations through superior self-mastering ML algorithms. Universal, the proposed model could permit higher choice-making and streamline the network maintenance tasks employing offering well-timed records and insights about the community's typical health. © 2024 IEEE.

关键词： Time series analysis

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Prediction of Steel Plate Fault Classification Using CART Fuzzy Logic and ANFIS Models 9

Prediction of Steel Plate Fault Classification Using CART Fu...

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9th International Conference on information technology Trends, ITT 2023

作者： Akpinar, Mustafa Adak, M. Fatih Higher Colleges of Technology Computer and Information Science Sharjah United Arab Emirates Sakarya University Computer Engineering Department Sakarya Turkey

ISBN: (纸本)9798350327502

Some systems correct faulty production to reduce the cost and increase the performance of steel plates. The first element of quality production is to identify and classify defects. However, when the error classification is not done well, the cost of correcting the error increases. This study proposes a classification and regression tree (CART) based fuzzy model to determine which fault class the faulty steel plates fall into. The second model used in the study is the adaptive neuro-fuzzy interface system (ANFIS). In both approaches, error classes were determined using fuzzy logic. In the two models created, it was observed that when the information of the detected faulty steel plate was entered, it was unsuccessful in determining which class this fault belonged to. Although it suggests a quick solution for detecting the error class, it has been seen that these approaches are not appropriate to use because they do not offer the right solution. © 2023 IEEE.

关键词： Errors

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Recurrent Neural Networks for Improved Medical Image Classification

Recurrent Neural Networks for Improved Medical Image Classif...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Umesh Kumar Kavitha, R. Saraswat, Pankaj College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Computer Science and Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

In recent years, scientific imagery has ended up with an increasing number of essential approaches for diagnosing and monitoring many sicknesses. As a result, scientific photo classification has become a crucial research area. Deep learning procedures have opened new avenues for the medical photo category, with current tendencies because of recurrent neural networks (RNNs). Recurrent neural networks are robust neural networks that could discover ways to version temporal or sequential systems. Using RNNs, researchers can train a deep community in a supervised fashion without the need for manual photo segmentation. It has been validated to improve performance in scientific image type, with examples in the skin lesion category and lung nodule classification. The latest paintings have additionally validated the usage of RNNs to find latent features in clinical imagery, including latent anatomical systems and covariate relationships between disorder states. This type of evaluation can be beneficial in developing greater correct classifiers for medical images, similar to presenting a higher know-how of the imaging records. In precis, recurrent neural networks (RNNs) display promise in improving the accuracy of medical image class obligations. RNNs are crucial to discovering new features and covariate relationships between disease states in medical pics. With ongoing advances, RNNs will offer powerful equipment for scientific imaging. © 2024 IEEE.

关键词： analysis classification developing relationships understanding

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Ensemble Learning for Soil Moisture Estimation: A Case Study of the Tibetan Plateau 24

Ensemble Learning for Soil Moisture Estimation: A Case Study...

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6th International Conference on Image, Video and Signal Processing, IVSP 2024

作者： Hao, Xiang Wang, Liejun He, Lijun Li, Fan School Of Computer Science And Technology Xinjiang University Urumqi China School Of Information And Communications Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798400716829

Soil moisture (SM) plays a critical role in agriculture, water management, climate modeling, ecosystem health, and hazard prediction. The majority of SM estimation methods rely on individual machine learning models, which are less robust and more prone to underfitting or overfitting. To overcome the issue, we design SM estimation models based on ensemble learning. The land-surface temperature (LST) has been identified as the most influential indicator of SM in prior research. However, the MOD11A1 LST product suffers from significant data gaps, rendering it unsuitable for direct application in SM estimation. To incorporate the crucial indicator of LST in SM estimation, we employ Random Forest (RF) to impute the missing data in the MOD11A1 LST product. Subsequently, we develop an RF-based LST reconstruction and estimation by utilizing the imputed MOD11A1 LST product and in situ soil temperature measurements. Next, we construct separate SM estimation models based on Extra Tree (ERT) and Light Gradient Boosting Machine (LightGBM), respectively, using the reconstructed LST and in situ measured SM. Finally, to enhance the accuracy of SM estimation, we construct an EL-Stacking-based SM estimation model by combining predictions from ERT or LightGBM with in situ SM measurements. The results substantiate the superior performance of our proposed EL-Stacking methods compared to other models. The root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of EL-Stacking were 0.0349-0.0657m3m-3, 0.0227-0.0525m3m-3 and 0.7305-0.9657, respectively. Furthermore, EL-Stacking effectively captures spatial dynamics and exhibits stronger consistency with in situ measurements. © 2024 ACM.

关键词： Soil moisture

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Enhanced Multimodal Depression Detection With Emotion Prompts

Enhanced Multimodal Depression Detection With Emotion Prompt...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Teng, Shiyu Liu, Jiaqing Sun, Hao Chai, Shurong Tateyama, Tomoko Lin, Lanfen Chen, Yen-Wei College of Information Science and Engineering Ritsumeikan University Shiga Japan College of Computer Science and Technology Zhejiang University Hangzhou China Department of Intelligent Information Engineering Fujita Health University Japan

ISBN: (纸本)9798350368741

Depression is a pervasive mental health disorder that remains frequently undiagnosed and untreated due to societal barriers and the subjective nature of its symptoms. Leveraging recent advances in large language models (LLMs), we propose a novel depression detection pipeline that generates emotion prompts tailored to individual data, enhancing detection accuracy. Our approach integrates cross-modality fusion via cross attention mechanisms to combine depressive and emotional features, creating a comprehensive representation of depression indicators. Evaluated on the E-DAIC and EATD datasets, our method outperforms state-of-the-art techniques, demonstrating its potential for more precise emotion-based depression detection. © 2025 IEEE.

关键词： Depression detection Emotion prompt Large language model Multi-modal fusion

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Deciphering Plant Seedlings: Enhancing Classification and Interpretability with Vision Transformers 5

Deciphering Plant Seedlings: Enhancing Classification and In...

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Feng, Jinke Xu, Xintao Donghua University College of Computer Science and Technology Shanghai China Lanzhou Jiaotong University School of Electronic and Information Engineering Lanzhou China

ISBN: (纸本)9798350373820

The application of deep learning in agriculture for tasks such as plant seedling classification holds the potential to revolutionize crop management and yield optimization. With the popularity of transformer in computer vision, leveraging this architecture for classification tasks has obtained a great attention in both academic and industurial fields. This paper explores the utilization of a Vision Transformer (ViT), a model architecture that has shown superior performance in various image classification tasks, extending its application to the agricultural domain. By fine-tuning a pre-trained ViT model to distinguish among 12 plant species, we achieve high classification accuracy, reaching a specificity of 95.1%, which demonstrates the model's potential in smart agriculture. Moreover, we enhance model interpretability through Explainable Artificial Intelligence (XAI) techniques, employing attention heatmaps to reveal the model's focus and decision-making process. Our results showcase the viability of using ViTs for domain-specific challenges and contribute to the advancement of transparent and reliable AI tools in sustainable farming practices. © 2024 IEEE.

关键词： Deep learning

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Adaptive Clustering Algorithm Applied towards Robust Image Segmentation of Hyper Spectral Scans

Adaptive Clustering Algorithm Applied towards Robust Image S...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Murugan, R. Solanki, Madhav Gupta, Arun Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Computer Science and Engineering Uttar Pradesh Mathura India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

This paper provides a new adaptive clustering set of rules for the robust segmentation of hyperspectral scans. This set of rules is based on a self-organizing adaptive clustering method, which employs an aggregate of ok-method clustering and evolutionary algorithms. This clustering algorithm dynamically adapts its parameters using retaining the tune of the degree of delight of the pixels, which can be allocated to the clusters. Through experimental validation on three hyperspectral photographs, it's been shown that that is a dependable and green method to fast and appropriately segment hyperspectral photograph records. The effects acquired display that this set of rules plays better and faster than many other current algorithms and is extraordinarily strong to adjustments in environmental situations. Hence, it can be effectively hired for an extensive style of programs. The adaptive clustering set of rules carried out toward sturdy photo segmentation of hyperspectral scans is a technique utilized to phase a picture that consists of multiple physical characteristics accurately. The algorithm is based on an unmanaged clustering method, which collectively assigns pixels of similar intensity. That is performed to accurately perceive land cover lessons inside the imagery and, in the end, assist inside the type of hyper-spectral experiment. The adaptive clustering set of rules is extensively categorized into comfortable and most advantageous clustering. Relaxed clustering refers to the set of rules capable of reducing the search radius over time and converging at the best clustering solution. That is beneficial because it could assist in keeping away from trapping pixels in local optima and additionally result in faster convergence of the segmentation technique. The most appropriate clustering, however, immediately seeks the global optima, which ends up in better accuracy of the segmentation method. The adaptive clustering technique utilizes numerous heuristics to determi

关键词： adapts algorithms numerous parameters traits

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Privacy and Security Issues in Mobile Communication Systems

Privacy and Security Issues in Mobile Communication Systems

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Saraswat, Nidhi Agarwal, Puneet Bhuvana, J. Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India College of Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Mobile conversation systems have emerged as an essential part of daily life, enabling people to live related and access records at the cross. But with the increasing use of these devices, there's a developing challenge about privacy and protection issues. This paper aims to provide a technical abstract of the essential privacy and safety challenges in cellular communication systems. The primary undertaking is the vulnerability of the win-wireless community. Using unsecured networks and vulnerable encryption protocols could make devices vulnerable to attacks, including eavesdropping and information interception. Cellular apps pose a considerable risk to privacy as they can access touchy facts without consumer consent. Malicious apps can make the most vulnerable inside the tool's operating device to Wi-Fi get access to personal attributes. Some other Wi-Fi is the dearth of standards for privateers and security in cellular communique structures. This ends in inconsistent practices and makes dealing with and mitigating ability threats difficult. © 2024 IEEE.

关键词： Attributes Community Conversation Systems Inconsistent Mobile Vulnerable

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