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检索条件"机构=Institute of Computer Science Software Systems Engineering"

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Experimental Evaluation of A Lightweight RSS-Based PLA Scheme in Multi-Node Multi-Cell Mesh Networks

Experimental Evaluation of A Lightweight RSS-Based PLA Schem...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Hossien B. Eldeeb Anshul Pandey Martin Andreoni Sami Muhaidat Center of Cyber-Physical Systems Electrical Engineering & Computer Science Khalifa University UAE Technology Innovation Institute Masdar City Abu Dhabi UAE

In infrastructure-less Internet of Things (IoT) mesh networks, node authentication is a critical challenge. The situation becomes even more complex when considering the utilization of resource-constrained and low-end devices, as this restricts the applicability of existing cryptographic solutions. Physical layer authentication (PLA) has recently emerged as a complementary solution to enable a secure authentication process for infrastructure-less IoT deployments. Consequently, this research paper aims to empirically investigate the utilization of a PHY layer-based feature, namely received signal strength (RSS), for establishing a robust PLA in multi-node (MN) multi-cell (MC) wireless mesh networks. First, we develop a low-complex yet secure and efficient RSS-based PLA framework to execute continuous authentication for multiple legitimate nodes from different cells. Then, through extensive experiments, we evaluate the performance of the proposed scheme in terms of authentication rate, the detection rate of illegitimate nodes, and variations in the reference RSS values. Our evaluations consider different numbers of nodes, their respective locations, and relative distances concerning the receiver node (authenticator node). Furthermore, through a comprehensive analysis of the experimental data, we demonstrate the impact of the RSS threshold on the overall system performance. The obtained results demonstrate that the authentication rate is directly proportional to the RSS threshold while the reverse is correct for the detection rate.

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Design and Comparison of Collaborative Filtering Technology for Product Suggestions in E-Commerce 1

Design and Comparison of Collaborative Filtering Technology ...

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1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRS 2022

作者： Rajasekar, P. Mohanraj, B. Padhi, S.N. Sivakumar, N. Lavanya, J. Charles Prabu, V. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Information Technology Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Tamil Nadu Chennai600029 India Department of Fashion Designing SRM Institute of Science and Technology CSH Kattankulathur campus Tamil Nadu Chennai603203 India Department of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600 062 India

ISBN: (纸本)9781665460842

E-commerce websites and internet purchasing have never been more necessary. Product recommendations are a key tactic employed by online buying platforms. This study compares two different filtering algorithms that are used in product recommendationson websites like Amazon and Flipkart. Reviews of electrical devices, user data, and timestamps were all gathered from Kaggle and put into a dataset. These specifics then undergo several preprocessing steps. The steps are data visualization, duplicate value analysis, missing value analysis, and datatype analysis. The exhibited data is then used to train and test the machine learning models. For this purpose, two machine learning models are developed using two different algorithms. The algorithms K-Nearest Neighbor(KNN) and Support Vector Decomposition (SVD) are employed. The models are then trained to acquire used in the prediction procedure after that. The best models for the prediction process are then determined by scrutinizing the results of both models. The Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) values are located and compared because the findings of the analysis weren't *** erroneous data are plotted into a graph and evaluated to further demonstrate the point. As the SVD method is discovered to be the best of the group, the algorithm's greatest and worst predictions are discovered and examined. Even the poorest model constructed for the study's predictions turned out to be superior to several other prediction algorithms used for product suggestions. This algorithm can be deployed into the backend of any e-commerce website making it more effective. © 2022 IEEE

关键词： Value engineering

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Enhancing Eye Health Diagnosis through Deep Transfer Learning: Unveiling Insights from Low Quality Fundus Images

Enhancing Eye Health Diagnosis through Deep Transfer Learnin...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Sathishkumar, R. Karthikeyan, T. Kumar, P. Praveen Rengarajan, Chitra Tharagesh, A. Govindarajan, M. Manakula Vinayagar Institute of Technology Dept. of Cse Puducherry India Sri Manakula Vinayagar Engineering College Dept. of Computer Science and Business Systems Puducherry India Sri Manakula Vinayagar Engineering College Dept. of Information Technology Puducherry India Tamil Nadu Agricultural University Centre for Agricultural Nanotechnology Tamil Nadu India Annamalai University Dept. of Cse Tamil Nadu Chidambaram India

ISBN: (纸本)9798331510022

Due to the frequency of eye illnesses, effective and precise diagnostic instruments are required. This work suggests an approach that uses low quality fundus images with deep transfer learning more precisely, the EfficientNetB0 architecture to improve eye health diagnosis. We tackle the problem caused by the quality of fundus photographs that are commonly found in clinical settings, which frequently display noise and abnormalities. Our methodology consists of pretraining the EfficientNetB0 model on a sizable dataset of excellent fundus photos, followed by fine-tuning it on a dataset of poor fundus photos. By employing this transfer learning technique, the model enhances its diagnostic capabilities by learning to identify significant features from the low-quality images. We ran tests on a variety of datasets that included fundus photos of varying degrees of deterioration in order to assess our approach. As compared to conventional techniques, the results reveal a significant improvement in diagnostic accuracy, demonstrating the effectiveness of deep transfer learning for improving eye health diagnosis from difficult fundus images. With fused features from MobileNet and DenseNet-121 models, the ANN specifically achieved accuracies of 98.5% for cataracts, 99.1% for diabetic retinopathy, 99% for glaucoma, and 99.5% for normal conditions. © 2024 IEEE.

关键词： Electronic health record

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Algorithm selection for software verification based on adversarial LSTM 7

Algorithm selection for software verification based on adver...

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7th IEEE International Conference on Big Data Security on Cloud, 7th IEEE International Conference on High Performance and Smart Computing, and 6th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2021

作者： Wang, Qiang Jiang, Jiawei Zhao, Yongxin Cao, Weipeng Wang, Chunjiang Li, Shengdong Institute of System Engineering Chinese Academy of Military Science Beijing China School of Software Engineering East China Normal University Shanghai China School of Computer Science and Engineering Shenzhen University Shenzhen China Technology Tianjin China

ISBN: (纸本)9781665439275

As a prevalent technique for checking the correctness of software, software verification has achieved a significant progress in the past decades, reaching a point where a large number of verification algorithms and tools are available and sophisticated enough to handle the large-scale industrial software. However, it remains a difficult task to select a suitable verification algorithm or tool for the software at hand, given the fact that the underlying algorithms are diverse and the performance tradeoffs are hard to accurately characterize. In this paper, we study the algorithm selection problem for software verification, and propose a novel algorithm selection model based on the Long Short Term Memory network (LSTM). Our solution employs word2vec to obtain the embedding representation of the code, avoiding constructing the software features manually. We also propose a novel approach to construct the adversarial code examples in order to solve the sparsity and data imbalance problem. The experimental evaluations on the latest available dataset show that our solution improves the prediction accuracy by about 7% compared with the state-of-the-art selection algorithm. © 2021 IEEE.

关键词： Long short-term memory

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Enhancement of IoT based Flood Detection and Prevention using Arduino UNO with WiFi Module

Enhancement of IoT based Flood Detection and Prevention usin...

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International Conference on Advanced Computing and Communication systems (ICACCS)

作者： R. M. Dilip Charaan J. Shobana P. Krishnamoorthy B. Anni Princy R. J. Abinaya K. Murugesan Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Tamil Nadu India Department of Computer and Communication Engineering Panimalar Engineering College Tamil Nadu India Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Tamil Nadu India Department of Electronics and Communication Engineering Loyola Institute of Technology Tamil Nadu India

The overflow of water from a lake or river usually causes flooding. Sometimes, a dam breach might result in the unexpected release of vast quantities of water. Some of the water seeps into the ground, flooding the region. In a station, rivers are involving the riverbanks. Along with a lack of goods and houses, businesses, and offices, street infrastructure floods contain bacteria, sewage from waste sites, and chemical spills, which later cause a number of diseases. The rate of change in river stage in real time, which can assist signal the gravity and immediacy of this hazard, is a crucial piece of information for flood predictions. Understanding the type of storm that produced the moisture, including its duration, strength, and actual extent, is important for determining the possible severity of the flood. In this system, an Arduino Uno is connected to four separate sensors: a humidity sensor, a flow sensor, a float sensor, and an ultrasonic sensor. The float sensor detects when the water is full. With the aid of IOT, these sensor combinations are utilised to predict floods, alert the appropriate authorities, and sound an immediate alarm in adjacent communities to rapidly relay information about potential floods. These sensors provide data via the IOT’s WiFi module. When flooding conditions are detected, the system warns the nearby villages and places and estimates how long it would take for help to reach at a particular location. The technology also determines when it might be considered a flood and gives them a window of time to leave in case it does.

关键词： Dams Storms Sensor systems Real-time systems Sensors Rivers Floods

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TAJ: A Platform for Integrating Tumor Detection and Depth Perception for Endoscopic Surgery

TAJ: A Platform for Integrating Tumor Detection and Depth Pe...

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International Symposium on Medical Robotics (ISMR)

作者： Ali Fakhry Thaison Le Junzhe Zhang Rui Li The Department of Computer Science Flexible AI-enabled Mechatronics Systems Lab (FAMS) Courant Institute of Mathematical Sciences New York University Manhattan NY The Department of Computer Science Flexible AI-enabled Mechatronics Systems Lab (FAMS) Tandon School of Engineering New York University Brooklyn NY Department of Mechanical Engineering Flexible AI-enabled Mechatronics Systems Lab (FAMS) The Vertically Integrated Projects (VIP) Tandon School of Engineering New York University Brooklyn NY

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

Surgery for lung cancer requires precision and efficiency, with computer-aided systems displaying signs for enhancing these areas. Yet, the availability of software-based tools to assist in such surgeries remains limited. This paper proposes a collection of software-based tools that will address this issue. This collection includes tumor-detection, a custom depth-estimation algorithm, and a custom user-interface (TAJ) that stores and displays information in real time. Tumors would be detected using YOLOv5, an object detection model, with a confidence baseline of 0.75. This baseline was estab-lished to maximize precision while maintaining a satisfactory recall-which the test results show in a 95.5 % precision and 85.3 % recall. To estimate relative depth, two main approaches were utilized. The first was relative depth estimation using a popular collection of monocular depth algorithm-MiDaS. The three algorithms within MiDaS that were used were “MiDaS-Small,” “DPT-Hybrid,” and “DPT-Large.” The second approach relied on using the detected tumor's frame size in comparison to the dimensions of the camera's feed, with the results being normalized using a modified sigmoid function. This custom algorithm showed a positive trend between real depth and relative depth throughout the domain of the test, whereas the tests with MiDaS did not exhibit as strong of a correlation. Additionally, the custom algorithm was 1.48, 2.97, and 4.33 times quicker than the MiDaS-Small, DPT-Hybrid, and DPT-Large associated algorithms, respectively. The custom user interface was built using Python's Tkinter library, with the live graph being generated using Matplotlib. Thus, this research offers a data-driven solution to enhancing surgery by providing an easy-to-use user interface that offers depth and detection information to surgeons.

关键词： YOLO Correlation Accuracy Systematics Surgery Estimation User interfaces

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Graph Encoding-Enhanced Transformer for Drug Recommendation

Graph Encoding-Enhanced Transformer for Drug Recommendation

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IEEE International Conference on Big Data

作者： Xunsheng Cai Syauki Aulia Thamrin Arbee L.P. Chen Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

Doctors prescribe drugs for the patient with the objective of curing the patient. Some drugs cannot be consumed together since doing so may cause negative effects. This can be avoided by knowing the effects caused by consuming combinations of drugs. However, for complex cases of a patient, it can be difficult to decide the best combination of drugs. Therefore, automatic drug recommendation method was used to recommend drugs with minimal negative effects. It is performed by using a deep learning model which is trained on drug data. A graph called drug-drug interaction (DDI) is used to represent the drugs and effects of consuming one drug with other drugs. Additionally, information about the combination of drugs prescribed in the past for a patient is also important for drug recommendation. It can also be represented as a graph called drug concurrence relation (DCR). The DDI and DCR graphs can be input to the deep learning model through an encoding process. In this paper, we propose a graph encoding-enhanced transformer (GEET) to recommend drugs. The DDI and DCR graphs are encoded by using Graph Attention Network (GAT). The graph encoding model has multi-head attention, which makes the GEET model aware of the most important DDI and DCR from the graphs. Additionally, the encoding outputs are combined, and activation function and normalization methods are used to improve the performance. The model has been evaluated on the publicly available MIMIC-III dataset and has the best results on F1, Jaccard and PRAUC scores compared to the models proposed by the existing related research papers.

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

A Multi-Player Potential Game Approach for Sensor Network Lo...

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IEEE Conference on Decision and Control

作者： Gehui Xu Guanpu Chen Baris Fidan Yiguang Hong Hongsheng Qi Thomas Parisini Karl H. Johansson Department of Electrical and Electronic Engineering Imperial College London London UK Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Control Science and Engineering Tongji University shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies.

关键词： Location awareness Noise Games Position measurement Nash equilibrium Distance measurement Noise measurement

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Physically Informed Graph-Based Deep Reasoning Net for Efficient Combinatorial Phase Mapping

Physically Informed Graph-Based Deep Reasoning Net for Effic...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Yimeng Min Ming-Chiang Chang Shufeng Kong John M. Gregoire R. Bruce van Dover Michael O. Thompson Carla P. Gomes Computer Science Cornell University Ithaca USA Materials Science and Engineering Cornell University Ithaca USA Software Engineering Sun Yat-sen University Guangzhou China Liquid Sunlight Alliance California Institute of Technology Pasadena USA

Phase mapping is a crucial challenge in materials discovery, which entails determining crystalline phase distribution in condition space based on a collection of X-ray diffraction (XRD) data. This task involves exploring the space of potential phases, identifying existing phases, and determining their respective weight distribution in the condition space while adhering to strict physics constraints. In recent years, there has been a growing interest in leveraging machine learning (ML) techniques to tackle the phase mapping problem. ML methods offer the potential to handle larger and more complex phase mapping instances and provide enhanced accuracy compared to traditional approaches. Among promising ML approaches, DRNets, which formulates the phase mapping problem as an unsupervised pattern demixing problem, represents the current state of the art. Despite its practical effectiveness, DRNets does have certain limitations. For instance, it employs a single multiplicative factor to calculate the stick locations in XRD patterns, which may not accurately reflect the underlying physics of X-ray diffraction. Additionally, DRNets relies on an expensive path-based schema to enforce phase weight smoothness. To overcome these limitations, we propose a novel approach called Physically-informed Graph-based DRNet (PG-DRNet). PG-DRNet incorporates a physical decoder that estimates the crystals' lattice parameters and reconstructs XRD patterns based on Bragg's law. Additionally, we introduce a graph-based schema to enforce phase weight smoothness as well as lattice and peak intensity shift. This graph-based schema provides several advan-tages, including improved computational efficiency compared to the path-based schema utilized in DRNets. To thoroughly evaluate the effectiveness of our approach, we conducted experiments on various chemical systems. Notably, our evaluation went beyond the scope of previous studies that solely focused on varying compositions and extends to explore

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Analysis of Flexibility-centric Energy and Cross-sector Business Models

Analysis of Flexibility-centric Energy and Cross-sector Busi...

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International Conference on European Electricity Market, EEM

作者： Luís Rodrigues Diogo Faria Fábio Coelho João Mello João T. Saraiva José Villar Ricardo J. Bessa INESC TEC - Institute for Systems and Computer Engineering Technology and Science Universidade do Minho Faculdade de Engenharia da Universidade do Porto Portugal

The new energy policies adopted by the European Union are set to help in the decarbonization of the energy system. In this context, the share of Variable Renewable Energy Sources is growing, affecting electricity markets, and increasing the need for system flexibility to accommodate their volatility. For this reason, legislation and incentives are being developed to engage consumers in the power sector activities and in providing their potential flexibility in the scope of grid system services. This work identifies energy and cross-sector Business Models (BM) centered on or linked to the provision of distributed flexibility to the DSO and TSO, building on those found in previous research projects or from companies’ commercial proposals. These BM are described and classified according to the main actor. The remaining actors, their roles, the interactions among them, how value is created by the BM activities and their value propositions are also described.

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