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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Bang Zeng Hongbin Suo Yulong Wan Ming Li Data Science Research Center Duke Kunshan University Kunshan China School of Computer Science Wuhan University Wuhan China Data&AI Engineering System OPPO Beijing China

Most multi-channel speaker extraction schemes use the target speaker’s location information as a reference, which must be known in advance or derived from visual cues. In addition, memory and computation costs are enormous when the model deals with the fusion input. In this paper, we propose Speaker-extraction-and-filter Network (SeafNet), which is a low-complexity multi-channel speaker extraction network with only speech cues. Specifically, the SeafNet separates the mixture by utilizing the correlation between an estimation of target speaker on reference channel and the mixed input on rest channels. Experimental results show that compared with the baseline, the SeafNet model achieves 6.4% relative SISNRi improvement on the fixed geometry array and 8.9% average relative SISNRi improvement on the ad-hoc array. Meanwhile, the SeafNet achieves 60% relative reduction in the number of parameters and 42% relative reduction in the computational cost.

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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification

Elevating Ocular Diagnosis: Harnessing the Power of Efficien...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Vankadari Mohith Kalpana Raja I. R. Oviya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven USA

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the EfficientNetV2S framework we achieve an overall accuracy rate of 94.6% in classifying images depicting common eye conditions like cataract, diabetic retinopathy, glaucoma, and normal cases. Our model exhibits capabilities across a diverse spectrum of medical conditions due to meticulous preprocessing and fine-tuning procedures. The results highlight the potential for automated technology integration in healthcare settings, which might improve patient care and treatment plans. This paper highlights the use of advanced neural network designs in the diagnosis of ocular illnesses, offering insights at the nexus of learning and medical image processing.

关键词： Glaucoma Deep learning Diabetic retinopathy Accuracy Computational modeling Neural networks Medical treatment

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Intelligent Predictive Model for Hepatitis C

Intelligent Predictive Model for Hepatitis C

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Artificial Intelligence (ICAI), International Conference on

作者： Mehreen Shahzadi Faisal Bukhari Numan Shafi Department of Data Science Faculty of Computing & Information Technology University of the Punjab Lahore Pakistan Department of Computer Science University of Engineering and Technology Lahore Pakistan

Hepatitis C is the liver's festering that can lead to severe liver damage, usually caused by the hepatitis C virus. Hepatitis C has different stages. It is tough to cure in it's last stages; at the same time, it is expensive and painful process. The current research, however, is an alternative precaution to this issue. Hepatitis C can be predicted early by using multiple factors. The dataset related to hepatitis C was not publicly available. To overcome this challenge, the healthy and HCV effected samples were collected from different hospitals in Punjab. A questionnaire based survey was taken including different HCV related factor i.e. gender, weight loss, hives/ rashes, swelling, jaundice, drug addiction history, hepatic encephalopa-thy (drowsiness, slurred speech), Ascites (fluid buildup in belly/ abdomen), spider angiomas (Spiderlike blood vessels), shared syringe usage, medical history, and severeness. Different cleaning, scaling, and feature selection techniques were applied to collect the best feature data. After selection, various machine learning algorithms were applied. Random forest, KNN, Decision Tree, SVC, and MLP were used, but MLP yielded optimal results in all classification algorithms. We have gained 95.9 % accuracy when tested on unknown data based on the MLP model. As the predictions' results were satisfactory, it would be helpful for the people and act as a critical awareness.

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Evaluation of CAN Bus Security Vulnerabilities and Potential Solutions

Evaluation of CAN Bus Security Vulnerabilities and Potential...

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Women in data science at Prince Sultan University (WiDS PSU), International Conference of

作者： Asma Alfardus Danda B. Rawat Department of Electrical Engineering and Computer Science Data Science and Cybersecurity Center (DSC2) Howard University Washington D.C USA

The past decade has seen a significant increase in the automobile industry, which has come with some serious challenges and threats. Modem vehicles are now made up of complex mechanical systems, as well as sophisticated electronic devices and connections to the outside world. Various electronic devices utilize standard communication protocols, including the Controller Area Network (CAN), to establish communication with each other. Unfortunately, CAN lacks some fundamental security features, such as encryption and authentication, which makes it vulnerable to security attacks. This can lead to accidents and financial losses for the users of these vehicles. To address this issue, researchers have proposed a number of security measures, such as cryptography and Intrusion Detection Systems (IDS). This paper addresses the security vulnerabilities associated with CAN and proposes potential solutions to overcome its limitations.

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Feature Selection for Classification on High- Dimensional data Using Swarm Optimization Algorithms

Feature Selection for Classification on High- Dimensional Da...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Pradeep kumar D Sowmya BJ Anitha Kanavalli Deeptashree D Department of Computer science and engineering Ramaiah Institute of Technology Bangalore India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bangalore India

Feature selection plays a crucial role in classification by identifying relevant features. Bio-inspired algorithms, such as genetic algorithms, particle swarm optimization, and ant colony optimization, have gained popularity due to their ability to mimic natural processes and explore high-dimensional feature spaces efficiently. The analysis includes evaluating classification accuracy, and computational efficiency as performance metrics. The experimental setup involves dataset selection, choosing a classification algorithm, implementing bio-inspired feature selection, evaluation, parameter tuning, and comparative analysis. The findings provide valuable insights into the effectiveness and efficiency of bio-inspired algorithms for feature selection in classification. The project focuses on reducing high- dimensional datasets to low-dimensional ones using feature selection through classification with swarm optimization algorithms. It aims to validate the effectiveness of the Jellyfish Search algorithm (JSA), Horse Herd Optimization algorithm (HOA), and Binary Bat algorithm (BBA) on real-world datasets. The objectives include evaluating the algorithms' performance, conducting a comprehensive performance analysis, execution of classifiers like RF, DT, KNN, Voting Classifier and SVM, obtaining and tuning the results, and publishing the findings. The research outcomes contribute to the knowledge of feature selection for classification and provide guidance in selecting appropriate algorithms for specific classification tasks.

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ENHANCE TRANSFERABILITY OF ADVERSARIAL EXAMPLES WITH MODEL ARCHITECTURE

arXiv

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arXiv 2022年

作者： Fan, Mingyuan Guo, Wenzhong Yu, Shengxing Ying, Zuobin Liu, Ximeng College of Computer and Data Science Fuzhou University China Faculty of Data Science City University of Macau China School of Electronics Engineering and Computer Science Peking University China

Transferability of adversarial examples is of critical importance to launch black-box adversarial attacks, where attackers are only allowed to access the output of the target model. However, under such a challenging but practical setting, the crafted adversarial examples are always prone to overfitting to the proxy model employed, presenting poor transferability. In this paper, we suggest alleviating the overfitting issue from a novel perspective, i.e., designing a fitted model architecture. Specifically, delving the bottom of the cause of poor transferability, we arguably decompose and reconstruct the existing model architecture into an effective model architecture, namely multi-track model architecture (MMA). The adversarial examples crafted on the MMA can maximumly relieve the effect of model-specified features to it and toward the vulnerable directions adopted by diverse architectures. Extensive experimental evaluation demonstrates that the transferability of adversarial examples based on the MMA significantly surpass other state-of-the-art model architectures by up to 40% with comparable overhead. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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A Pattern-Oriented Reference Architecture for Governance-Driven Blockchain Systems

A Pattern-Oriented Reference Architecture for Governance-Dri...

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Working IEEE/IFIP Conference on Software Architecture

作者： Yue Liu Qinghua Lu Guangsheng Yu Hye-Young Paik Liming Zhu Data61 CSIRO Australia School of Computer Science and Engineering University of New South Wales Australia

Blockchain technology has been integrated into diverse software applications by enabling a decentralised architecture design. However, the defects of on-chain algorithmic mechanisms, and tedious disputes and debates in off-chain communities may affect the operation of blockchain systems. Accordingly, blockchain governance has received great interest for supporting the design, use, and maintenance of blockchain systems, hence improving the overall trustworthiness. Although much effort has been put into this research topic, there is a distinct lack of consideration for blockchain governance from the perspective of software architecture design. In this study, we propose a pattern-oriented reference architecture for governance-driven blockchain systems, which can provide guidance for future blockchain architecture design. We design the reference architecture based on an extensive review of architectural patterns for blockchain governance in academic literature and industry implementation. The reference architecture consists of four layers. We demonstrate the components in each layer, annotating with the identified patterns. A qualitative analysis of mapping two concrete blockchain architectures, Polkadot and Quorum, on the reference architecture is conducted, to evaluate the correctness and utility of proposed reference architecture.

关键词： Industries Software architecture Software algorithms Systems architecture computer architecture Maintenance engineering Software

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks

Agricultural Water Conservation through AI-Enhanced Solar Io...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： S. P. Maniraj Monisha J R. Amutha R. Kavitha P. J. Shriidhar C. Srinivasan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha Universityi Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future.

关键词： Irrigation Accuracy Food security Artificial neural networks Water conservation Real-time systems Internet of Things Sustainable development Water resources Intelligent sensors

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Integrating Deep Learning with Case-based Reasoning for Enhanced Brain Tumor Detection: A Review

Integrating Deep Learning with Case-based Reasoning for Enha...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Redrouthu Vamsi Krishna Jagjit Singh Dhatterwal Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Department of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation (KLEF) Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Detecting brain tumors (BT) early is vital for effective treatment planning and improved patient outcomes. The fusion of Deep Learning (DL) and case-based reasoning (CBR) presents a promising solution for enhancing BT detection accuracy and reliability. DL methods, such as Convolutional Neural Networks (CNNs), excel in automatically extracting discriminative features from medical images, bolstering diagnostic capabilities. Case-based reasoning, on the other hand, leverages past diagnostic cases to provide context-aware decision support for new cases. This review study provides an overview of recent advancements in the integration of DL with CBR for brain tumor detection. Then discuss the strengths and limitations of both approaches and highlight how their integration can address key challenges in brain tumor detection, such as limited labeled data, interpretability of DL models, and the need for personalized diagnosis. Furthermore, review existing studies that have applied DLand CBR techniques individually and in combination for brain tumor detection. Finally, discuss challenges and future research directions and opportunities for leveraging this integrated approach to improve brain tumor diagnosis and patient care. In this, review total 55 papers are taken based on the Integrating DL with Case-Based Reasoning for Enhanced Brain Tumor Detection. In this, the research articles published between 2021 and early 2024 were taken into consideration for this study.

关键词： Deep learning Reviews Learning (artificial intelligence) Feature extraction Cognition data models Planning Reliability Convolutional neural networks Medical diagnostic imaging

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Resilient Collaborative Caching for Multi-Edge Systems with Robust Federated Deep Learning

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IEEE/ACM Transactions on Networking 2024年

作者： Chen, Zheyi Liang, Jie Yu, Zhengxin Cheng, Hongju Min, Geyong Li, Jie Fuzhou University College of Computer and Data Science Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom University of Exeter Faculty of Environment Science and Economy Department of Computer Science ExeterEX4 4QF United Kingdom Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

As a key technique for future networks, the performance of emerging multi-edge caching is often limited by inefficient collaboration among edge nodes and improper resource configuration. Meanwhile, achieving optimal cache hit rates poses substantive challenges without effectively capturing the potential relations between discrete user features and diverse content libraries. These challenges become further sophisticated when caching schemes are exposed to adversarial attacks that seriously impair cache performance. To address these challenges, we introduce RoCoCache, a resilient collaborative caching framework that uniquely integrates robust federated deep learning with proactive caching strategies, enhancing performance under adversarial conditions. First, we design a novel partitioning mechanism for multi-dimensional cache space, enabling precise content recommendations in user classification intervals. Next, we develop a new Discrete-Categorical Variational Auto-Encoder (DC-VAE) to accurately predict content popularity by overcoming posterior collapse. Finally, we create an original training mode and proactive cache replacement strategy based on robust federated deep learning. Notably, the residual-based detection for adversarial model updates and similarity-based federated aggregation are integrated to avoid the model destruction caused by adversarial updates, which enables the proactive cache replacement adapting to optimized cache resources and thus enhances cache performance. Using the real-world testbed and datasets, extensive experiments verify that the RoCoCache achieves higher cache hit rates and efficiency than state-of-the-art methods while ensuring better robustness. Moreover, we validate the effectiveness of the components designed in RoCoCache for improving cache performance via ablation studies. © 1993-2012 IEEE.

关键词： cache space partitioning content popularity prediction Multi-edge collaborative caching proactive cache replacement robust federated deep learning

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