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Knowledge engineering and Communication systems (ICKECS), International Conference on

作者： Abdul Saleem Javeed C Gopala Krishnan Dept. of Computer Science & Engineering GITAM School of Technology Bengaluru INDIA

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Over the Internet, an efficient approach and promising solution to retrieve significant information envisages the beginning of Question Answering systems (QAS). Because of data sources availability, the deep learning model based on open domain QAS focuses a substantial quantity of research work. Because of the lack of medical datasets, less attention is received in the medical domain. The deep learning (DL) utility increases the development of huge data in the healthcare domain. In the past years, popularity has grown in hardware parallelism and quick data storage availability. The challenge, pipelines, and tasks involved in medical NLP and medical imaging deploy the existing review of research in this work. The medical NLP and imaging fields deploy DL structures with its extensive survey is presented here. Over all 39 research papers are collected for this survey analysis from the years 2017 to 2023. The diagnosis is enhanced with medical imaging, Natural language processing, and DL combination to detect the appropriate combination. Each of the outcomes is shown graphically. The information provided in this survey is quite useful for newcomers carrying out research in medical informatics.

关键词： Deep learning Surveys Reviews Soft sensors Pipelines Medical services Question answering (information retrieval)

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Unraveling Patterns in Healthcare Fraud through Comprehensive Analysis

Unraveling Patterns in Healthcare Fraud through Comprehensiv...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Khushi E Chirchi B Kavya Dept. of Computer Science and Engineering Dayananda Sagar University Bangalore India

This study addresses healthcare provider fraud in Medicare, employing advanced machine learning models on a diverse dataset to predict potential fraud. The goal is to contribute insights for effective fraud detection and mitigate its impact on overall healthcare costs. Utilizing Logistic Regression, Random Forest, and addressing class imbalance through SMOTE, we discern patterns in provider behavior. Evaluation metrics, including confusion matrices, accuracy, sensitivity, specificity, Kappa values, AUC, and F1-scores, comprehensively assess each model’s performance. Findings highlight distinct behavior patterns and underscore SMOTE’s effectiveness in mitigating class imbalance challenges. Comparative analysis discusses algorithm strengths and weaknesses, offering insights for real-world implementation, impacting fraud prevention strategies for insurance companies, healthcare providers, and beneficiaries. In summary, this research makes a noteworthy contribution to the detection of healthcare fraud, offering insights for effective strategies. The incorporation of SMOTE enhances model robustness, leading to improved fraud detection capabilities and, consequently, reducing the impact of fraud on healthcare costs.

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Improving IJTAG Test Efficiency and Security

Improving IJTAG Test Efficiency and Security

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2022 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2022

作者： Wang, Sying-Jyan Shih, Yen-Chang Li, Katherine Shu-Min Lin, Chen-Yeh Chong, Song-Kong National Chung Hsing University Dept. Computer Science Engineering Taichung Taiwan National Sun Yat-sen University Dept. Computer Science Engineering Kaohsiung Taiwan Cybersecurity Technology Institute Institute for Information Industry Taipei Taiwan

ISBN: (纸本)9781665409216

The IEEE Std. 1687 (IJTAG) provides a more efficient and flexible mechanism to access embedded instruments in complex system-on-chips (SoC). Embedded instruments are mainly used for testing, debugging, diagnosing, and post-silicon validation. In this paper, we propose a comprehensive protection method against data flow attacks in IJTAG networks. The network structure is arranged according to the access times of each instrument in to optimize the test efficiency and prevent third-party intellectual property attacks. Furthermore, we provide a method to group instruments to reduce hardware overhead and maintain the security. Experimental results show that the proposed methods achieve minimal overall access time (OAT) and segment insertion bit programming overhead. © 2022 IEEE.

关键词： system-on-chip

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Blockchain Based Credit system to Increase the Creditability through Carbon Offset Level

Blockchain Based Credit System to Increase the Creditability...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： A Padmavathi G Charan Reddy Gauri Santhosh Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India

Our country has relied significantly on agriculture as its main source of income for several decades. The demand for food and crops is growing due to the growing population, which offers the agriculture sector tremendous prospects for growth. However, despite the soaring demand and bright future, India’s agriculture industry has failed to demonstrate appreciable profitability. In the paper that follows, we provide a novel strategy meant to revolutionize agricultural marketing networks. Blockchain technology is included in our approach to enable the virtualization of products and carbon offset certificates. This system encourages agricultural producers to reduce carbon emissions and address environmental concerns. By utilizing blockchain in a particular way, carbon offset credits can be used to securely track, trade, and for other fostering sustainability practices. This integration of technologies assures to change how agricultural products are marketed currently. It also helps in promoting sustainability and provides consumers with all the valuable information about the environmental impact of their purchases.

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Key Vertex Pairs Extraction for Graph Classification Based on Probability Distribution Learning

Key Vertex Pairs Extraction for Graph Classification Based o...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Jianming Huang Hiroyuki Kasai Dept. of Computer Science and Communication Engineering WASEDA University Tokyo Japan

Graph classification is a hot topic of machine learning for graph-structured data, and it is also a very potential and valuable research. However, the difficulty of graph classification is challenging and special, which is quite different from the normal classification problems. One of the most difficult points of graph classification is that the numbers of vertex neighbors in graphs are usually variable, which makes the number of weights uncertain and ambiguous. Recent work such like the graph attention network apply the transformer on the graph neural network. However, the learned attentions cannot strictly reveal the importance of each part of graph, which makes the model less explainable. Moreover, for small datasets, it performs less effectively because of the excessive parameters. In order to overcome these difficulties, we propose a lightweight model with an edge weighting function based on the probability distributions of node pair features learned by the Gaussian mixture model. Although the proposed framework is simple, the experimental results shows its effectiveness on small datasets.

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Network Intrusion Detection system Using Decision Tree and KNN Algorithm

Network Intrusion Detection System Using Decision Tree and K...

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Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020 International Conference on

作者： Km Shivani Singh Khundrapam Johnson Singh Dept. of Computer Science and Engineering National Institute of Technology Imphal Manipur

The complexity of attacks is increasing, making it increasingly difficult to effectively discover breaches. A network intrusion detection system is needed to handle the aforementioned problem. An automated software program called a network intrusion detection system alerts administrators when someone attempts to compromise the system by participating in hazardous behaviors. A system or network is protected from harmful invasions by hardware and software firewalls. They function like filters, removing any data that could put the system or network at risk. We aimed to design the best intrusion detection system possible so that it could distinguish between “normal” and “attacked” categories of network data with high accuracy. The best accuracy can be obtained by using soft computing techniques like Decision Trees and KNN. We tested a variety of techniques, such as preprocessing the data, feature selection, principal component analysis (PCA) reduction, standardization, and normalization, in order to increase our model’s accuracy scores. We also assess the two approaches’ results according to their accuracy in order to decide which way is more effective.

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An Adaptive Nature-Inspired Paticle Bee Colony Deep Neural Network Technique for Event Detection in Video Sequences

An Adaptive Nature-Inspired Paticle Bee Colony Deep Neural N...

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Cognitive, Green and Ubiquitous Computing (IC-CGU), International Conference on

作者： Kumud Sachdeva Jasminder Kaur Sandhu Rakesh Sahu Dept. of Computer Science Engineering Chandigarh University Gharuan Punjab India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

Classifying and detecting difficult video events based on visual modalities remains an uncertain problem. Conventional video presentation approaches ineffectively extract each modality, hindering video event detection (VED) rates. The research methodology involves pre-processing steps: uploading video data samples, extracting video frames, converting to grayscale (rgb2gray), enhancing, and calculating smooth frames from the UCF -101 dataset. This pre-processing phase aims to deliver high-quality frames without data loss. Next, the feature extraction process employs the HoG method to extract feature vectors from refined video frames, facilitating the training process and efficiently reducing dimensions. An adaptive, nature-inspired PBC method is then implemented to select reliable and optimized feature sets from the extracted ones. These selected optimized feature vectors are input into different events of the deep neural network (DNN) classifier. Finally, reliable feature sets are identified through feature matching, and experimental outcomes demonstrate a significant 21.3% enhancement in VED and classification, assessed through accuracy rate, specificity (SP), sensitivity (SN), etc. Compared with traditional approaches using manually designed feature sets, the proposed approach proves more effective. Simulation outcomes on publicly available VED databases consistently outperform state-of-the-art video representation methods such as EFS-linear multi-support vector machine (MSVM), convolutional neural network (CNN), etc.

关键词： Training Visualization Adaptive systems Sensitivity Event detection Video sequences Feature extraction

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Physical Layer Authentication via Underwater Acoustic Multipath Channel Crafting

Physical Layer Authentication via Underwater Acoustic Multip...

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OCEANS

作者： Davide Eccher Paolo Casari Dept. of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

With the increasing number of actors in the under-water environment and the development of new applications, such as large-scale monitoring and autonomous underwater vehicle control, securing underwater communications is becoming a primary necessity. Security was not prioritized in the past due to the constraints of underwater acoustic communications, which cannot sustain the overhead of typical cryptographic techniques. In this paper, we propose a method to authenticate a network device by exploiting the physical properties of the acoustic channel. In particular, our method hinges on the uniqueness and quasi-reciprocity of the channel, from which the authenticator (Alice) node can extract several parameters such as the number of multi path channel components, their delay and amplitude. These values are similar on both ends of a link between Alice and a legitimate transmitter (Bob), and can be used as a seed to craft a new artificial channel, that is then applied to transmissions from Bob to Alice. With this procedure, Alice can distinguish Bob from an impersonating attacker (Eve), given a previous message exchange history. Eve can try to bypass the protocol by estimating the channel parameters and by trying to replicate Bob's signal by crafting a similar channel. In our tests, we observe that the estimation error for Eve, caused by her wrong channel estimates, becomes significant even for short distances betwen Eve and Bob. This error results in a discrepancy between the signal generated by Eve and the one expected by Alice, and reveals Eve as an attacker.

关键词： Estimation error Protocols Transmitters Channel estimation Authentication Delays History Cryptography Underwater acoustics Multipath channels

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Streamlining Livestock Surveillance: YOLOv8 Approach to Cattle Body Segmentation

Streamlining Livestock Surveillance: YOLOv8 Approach to Catt...

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Intelligent systems for Cybersecurity (ISCS), International Conference on

作者： Bharti Chugh Pushepndra Kumar Upendra Mishra Himanshi Chaudhary dept. of Computer science and engineering KIET Group of institutions Ghaziabad India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

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.

关键词： Training Precision agriculture Animals Surveillance Computational modeling Cows Real-time systems

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Gaze into the Pattern: Characterizing Spatial Patterns with Internal Temporal Correlations for Hardware Prefetching

arXiv

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arXiv 2024年

作者： Chen, Zixiao Wu, Chentao Gu, Yunfei Jia, Ranhao Li, Jie Guo, Minyi Dept. of Computer Science and Engineering Shanghai Jiao Tong University China

Hardware prefetching is one of the most widely-used techniques for hiding long data access latency. To address the challenges faced by hardware prefetching, architects have proposed to detect and exploit the spatial locality at the granularity of spatial region. When a new region is activated, they try to find similar previously accessed regions for footprint prediction based on system-level environmental features such as the trigger instruction or data address. However, we find that such context-based prediction cannot capture the essential characteristics of access patterns, leading to limited flexibility, practicality and suboptimal prefetching performance. In this paper, inspired by the temporal property of memory accessing, we note that the temporal correlation exhibited within the spatial footprint is a key feature of spatial patterns. To this end, we propose Gaze, a simple and efficient hardware spatial prefetcher that skillfully utilizes footprint-internal temporal correlations to efficiently characterize spatial patterns. Meanwhile, we observe a unique unresolved challenge in utilizing spatial footprints generated by spatial streaming, which exhibit extremely high access density. Therefore, we further enhance Gaze with a dedicated two-stage approach that mitigates the over-prefetching problem commonly encountered in conventional schemes. Our comprehensive and diverse set of experiments show that Gaze can effectively enhance the performance across a wider range of scenarios. Specifically, Gaze improves performance by 5.7% and 5.4% at single-core, 11.4% and 8.8% at eight-core, compared to most recent low-cost solutions PMP and vBerti. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

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