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Annals of Data Science 2023年第3期10卷 619-635页

作者： Zehtab-Salmasi, Aidin Feizi-Derakhshi, Ali-Reza Nikzad-Khasmakhi, Narjes Asgari-Chenaghlu, Meysam Nabipour, Saeideh Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department Computer and Electrical Engineering University of Mohaghegh Ardabili Ardabil Iran

Price prediction is one of the examples related to forecasting tasks and is a project based on data science. Price prediction analyzes data and predicts the cost of new products. The goal of this research is to achieve an arrangement to predict the price of a cellphone based on its specifications. So, five deep learning models are proposed to predict the price range of a cellphone, one unimodal and four multimodal approaches. The multimodal methods predict the prices based on the graphical and non-graphical features of cellphones that have an important effect on their valorizations. Also, to evaluate the efficiency of the proposed methods, a cellphone dataset has been gathered from GSMArena. The experimental results show 88.3% F1-score, which confirms that multimodal learning leads to more accurate predictions than state-of-the-art techniques. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Price prediction Multimodal learning Convolutional neural network Inception Classification Prediction Multimodal Price Deep learning Data science Mobile phone

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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Neural Computing and Applications 2024年第20期36卷 12357-12370页

作者： Abid, Fazeel Rasheed, Jawad Hamdi, Mohammed Alshahrani, Hani Al Reshan, Mana Saleh Shaikh, Asadullah Department of Information Systems Dr. Hassan Murad School of Management University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Software Engineering Istanbul Nisantasi University Istanbul34394 Turkey Department Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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A Blockchain-based Dual Identity Management and Authentication Framework for IoT Networks 10

A Blockchain-based Dual Identity Management and Authenticati...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Hussein, Dana Haj Houlahan, Ethan Janelle-Goode, Alexandre Lumsden, Thomas Ibnkahla, Mohamed Carleton University Department of Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9798350373011

The widespread adoption of Internet of Things (IoT) devices has introduced an era of unparalleled connectivity and data-driven innovation. Nevertheless, this rapid proliferation introduced significant security challenges. The limited computational capabilities of IoT devices impose constraints on the implementation of robust security mechanisms, rendering them susceptible to malicious attacks. Additionally, the dynamic nature of IoT systems which allows multiple administrators to add or remove IoT devices, necessitates the establishment of an identity management system. This system is crucial for managing both devices and system users, ensuring the traceability of device registration activities, and maintaining accountability of system users. Furthermore, traditional centralized authentication systems encounter scalability issues and high costs associated with centralized servers. To address these challenges, this paper proposes a Dual Identity Management and Authentication (DIMA) framework, leveraging blockchain technology. The proposed framework addresses two primary issues. Firstly, it assigns dual identities to each IoT node, facilitating a secure authentication process and regulating network access. Secondly, it supports the traceability of system device registration activities, enabling accountability for system users and ensuring a secure administrator-in-the-loop device identification process. Moreover, experimental testing and security analysis are undertaken to validate the usability and ascertain the critical security strengths of the DIMA framework. © 2024 IEEE.

关键词： Authentication Protocol

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Event Sourcing in Jason: Event-Driven State Reconstruction for BDI Agents

Event Sourcing in Jason: Event-Driven State Reconstruction f...

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2024 IEEE International Conference on Agents, ICA 2024

作者： Davies, Curtis Esfandiari, Babak Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798331539917

Event sourcing is a software design pattern that uses a log of events as the ground truth for recreating an application's state and for auditing purposes. In the context of agents, event sourcing uses event data captured from an agent's runtime, allowing for complex queries to be made about its behaviour during a mission. We present a proof-of-concept of this insight in the form of an 'agent simulator' for Jason (a BDI agent framework) to replay an agent's decision-making process and reconstruct its state at any given point in time. Through this process, we identify discrepancies between Jason's formal semantics and its Java implementation, leading us to propose revised semantic rules that accurately reflect an agent's reasoning cycle during runtime and provide a foundation for event-based explainability in agent-based systems. © 2024 IEEE.

关键词： Software design

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THE DIGITAL engineering FACTORY: CONSIDERATIONS, CURRENT STATUS, AND LESSONS LEARNED

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INCOSE International Symposium 2024年第1期34卷 927-943页

作者： Gregory, Joe Salado, Alejandro O'Neal, Sharon Larez, Richardo Reda, C.J. Martell, Niko Martin, Evan Colson, Matthew Masterson, John Armenta, David Dept. of Systems and Industrial Engineering University of Arizona United States Electrical and Computer Engineering University of Arizona United States

In industry, the advancement of digital engineering and the digital thread aims to reduce the impact of knowledge ‘siloes’ by providing a way to integrate data across the entire system lifecycle and across multiple domains. In a typical engineering curriculum, however, courses are still treated as ‘siloes’, and students often do not have the opportunity to experience this industrially relevant approach to engineering. The Digital engineering Factory (DEF) is a digital engineering environment under development at the University of Arizona to support engineering students. The DEF supports students by providing access to multiple engineering tools and is structured using a ‘hub-and-spoke’ approach to consolidate data from these tools. Through this connected architecture, students can transfer data generated in a particular course to tools for use in other courses. Connecting course activities in this way enables students to experience a complete end-to-end system lifecycle. At its ‘hub’, the DEF uses Violet to integrate data from multiple sources, create a digital thread, and generate a graph representation of the dataset. This knowledge graph, written in the Ontological Modeling Language (OML), can be viewed in OML Rosetta and is structured according to the University of Arizona Ontology Stack (UAOS). The use of the UAOS and OML Rosetta allows instructors to leverage semantic web technologies to support teaching activities such as grading. In this paper, the authors review the objectives of the DEF, discuss the status of the project, and highlight current limitations and lessons learned with regards to its deployment. These may be useful to inform similar developments in industrial settings. Copyright © 2024 by Author Name. Permission granted to INCOSE to publish and use.

关键词： Life cycle

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Comparative Analysis of Real-Time Data Processing Architectures: Kafka versus MQTT Broker in IoT 4

Comparative Analysis of Real-Time Data Processing Architectu...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Ho, Chung Lai Deryck Lung, Chung-Horng Mao, Zhengding Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350360721

The exponential growth of Internet of Things (IoT) devices highlights the critical need for robust and efficient messaging systems to ensure seamless communication and real-time data exchange. In this research, a thorough comparative analysis was conducted focusing on the real-time data processing architectures of two leading messaging brokers in the IoT domain: Apache Kafka and Message Queuing Telemetry Transport (MQTT) broker. Despite widespread adoption, Kafka and MQTT brokers exhibit uniqueness in their architectures, features, and functionality in real-time data processing scenarios. The study aims to unravel the strengths and weaknesses of each technology within the context of real-time data processing, providing valuable insights to optimize their IoT data workflows. © 2024 IEEE.

关键词： Internet of things

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Pedestrian Detection: An Explainable Approach 23

Pedestrian Detection: An Explainable Approach

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Pang, Hongbo Huang, Changcheng Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350374889

In the rapidly advancing field of autonomous technologies, particularly in transportation and surveillance, there is a critical need for accurate and reliable pedestrian detection systems. While deep learning models have achieved remarkable success in this area, their 'black box' nature often concerns their trustworthiness and practical applicability. Our study introduces a modified YOLOv5s model, augmented with a Global Attention Mechanism and enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM) for improved explainability. This approach maintains the high accuracy typical of deep learning models while significantly increasing their transparency. Our modified YOLOv5s architecture is tailored for real-time applications, achieving an inference speed of 9.4 milliseconds on a single NVIDIA A100 GPU. Incorporating Grad-CAM provides a more intuitive interpretation of the model's decisions, potentially increasing user confidence in its predictions. We evaluate our enhanced framework on the CityPersons dataset, demonstrating improvements in precision, recall, and mAP@0.5 compared to the baseline YOLOv5s model. Our results indicate a promising advancement in pedestrian detection, balancing high performance with improved explainability. © 2024 IEEE.

关键词： Deep learning

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COVID-19 Classification Using Pre-Trained Models and Disease Severity Score Masks 1

COVID-19 Classification Using Pre-Trained Models and Disease...

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1st International Conference on Computing, Internet of Things and Microwave systems, ICCIMS 2024

作者： Nehary, Ebrahim A. Rajan, Sreeraman Rossa, Carlos Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350351736

The significance of early detection of COVID-19 has been widely acknowledged as a means of reducing its spread and mortality rates among patients. Deep learning techniques for COVID-19 classification based on ultrasound (US) data have been extensively employed. However, detecting COVID-19 based on US images continues to be challenging primarily due to limited datasets with noisy and low-resolution images. This study investigates methods to enhance classification performance by incorporating disease severity score masks while training pretrained models enhanced with self-attention mechanisms. The disease severity scores range from 0 for healthy lung tissue to 1 for initial signs of abnormality, and 2 and 3 for advanced pathological artifacts. These masks and their corresponding US images are employed as inputs to pre-trained models for feature extraction. Subsequently, features extracted from the masks are utilized to recalibrate features obtained from US images using self-attention mechanisms. The proposed method achieves classification accuracy of 95.4, 90.4, 95%, 83%, and 92% when using pre-trained models VGG16, NASNet-Mobile, MobileNet_V2, ResNet50, and Xception, respectively. Further, all pre-trained models yield a low standard deviation of less than 5%. The results demonstrate that incorporating disease severity masks improves the classification performance, thus offering promising techniques for enhancing COVID-19 detection using ultrasound imaging. © 2024 IEEE.

关键词： COVID-19

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DDoS Flood Detection and Mitigation using SDN and Network Ingress Filtering - an Experiment Report 4

DDoS Flood Detection and Mitigation using SDN and Network In...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Marleau, Sebastien Rahman, Prianna Lung, Chung-Horng Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350360721

Distributed Denial of Service (DDoS) attacks are a common security threat to overwhelm a target server. IP source spoofing is a common approach used for DDoS attacks. It is claimed that all large DDoS attacks require IP spoofing and around 20% of the Internet still allows IP spoofing. DDoS attacks can also be launched much more easily using low-cost Internet of Things (IoT) devices. DDoS attacks originating from IoT devices have increased 5-fold in a year. Software-defined networking (SDN) has been proposed as a new paradigm to reduce complexity. However, security issues are still challenging for SDN, as DDoS attacks on the controller could fail the entire network. A mechanism that can detect and mitigate DDoS attacks on SDN is crucial. Ingress filtering has been adopted to reduce the probability of exploiting a network to launch an attack. This, among other things, mandates the dropping of packets if the source IP address of incoming packets is inconsistent with the configured one. Best Current Practice 38 (BCP 38) is a network ingress filtering technique to prevent source IP spoofing. By validating incoming packets, their IP addresses, and possibly other attributes at the ingress devices, it provides a first line of defense against suspicious traffic. We present an approach to DDoS detection and mitigation using BCP 38 and SDN. To evaluate the effectiveness of the proposed approach, a simple method was designed to detect and mitigate DDoS floods using BCP 38 and SDN. Experiments were conducted using Mininet and the Ryu controller. The result demonstrated that the proposed approach corresponded to DDoS attacks with IP spoofing efficiently and effectively. © 2024 IEEE.

关键词： Controllers

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Analysis of Statistical and Deep Learning Techniques for Temperature Forecasting

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Recent Advances in computer Science and Communications 2024年第2期17卷 49-65页

作者： Kruthika, Sriram Ganesan Rajasekaran, Umamaheswari Alagarsamy, Malini Sharma, Vandana Department of Computer Science and Business Systems Thiagarajar College of Engineering Tamil Nadu Madurai India NCR Delhi India

In the field of meteorology, temperature forecasting is a significant task as it has been a key factor in industrial, agricultural, renewable energy, and other sectors. High accuracy in temperature forecasting is needed for decision-making in advance. Since temperature varies over time and has been studied to have non-trivial long-range correlation, non-linear behavior, and seasonal variability, it is important to implement an appropriate methodology to forecast accurately. In this paper, we have reviewed the performance of statistical approaches such as AR and ARIMA with RNN, LSTM, GRU, and LSTM-RNN Deep Learning models. The models were tested for short-term temperature forecasting for a period of 48 hours. Among the statistical models, the AR model showed notable performance with a r2 score of 0.955 for triennial 1 and for the same, the Deep Learning models also performed nearly equal to that of the statistical models and thus hybrid LSTM-RNN model was tested. The hybrid model obtained the highest r2 score of 0.960. The difference in RMSE, MAE and r2 scores are not significantly different for both Statistical and Vanilla Deep Learning approaches. However, the hybrid model provided a better r2 score, and LIME explanations have been generated for the same in order to understand the dependencies over a point forecast. Based on the reviewed results, it can be concluded that for short-term forecasting, both Statistical and Deep Learning models perform nearly equally. © 2024 Bentham Science Publishers.

关键词： Decision making

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