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2nd Zimbabwe Conference on information Communication and Technology, ZCICT 2023

作者： Muzenda Allan, C. Samuel, Mugijima Jeremia, Musariwa Martin, Masheka Women's University in Africa Department of Information Systems and Computer Science Harare Zimbabwe

ISBN: (纸本)9798350318128

This study aims to understand how students perceive the usage of Learning Management systems (LMSs). A descriptive survey design was used. A sample of 50 students was randomly selected from the information systems department. For data triangulation, 10 lecturers were purposively selected for the study. For data collection semi-structured questionnaire, focus group discussions, and online survey were engaged. Descriptive statistics were used to analyse quantitative data while qualitative data was manually analyzed according to emerging themes. Research findings show that implementation of LMS is at times punctuated with challenges such as poor internet connectivity, exorbitant data, lack of knowledge and skills as well as insufficient student orientation. Furthermore, students showed that they were satisfied with the implementation of LMS and acknowledged through improved pass rate. The study recommended that training, workshops, and investing in more inappropriate mobile devices are integral components in improving LMS implementation. © 2023 IEEE.

关键词： Students

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Detection of Guns and Knives Images Based on YOLO v7 3

Detection of Guns and Knives Images Based on YOLO v7

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Aung, Yi Yi Oo, Kyi Zar University of Computer Studies Mandalay Faculty of Computer Systems and Technologies Mandalay Myanmar Faculty of Information and Communication Technology Pyin Oo Lwin Myanmar

ISBN: (纸本)9798350372120

The threat of violence involving guns and knives presents a persistent challenge to global public safety, necessitating effective detection systems despite the small size and potential ambiguity of these weapons. This study endeavors to address this challenge by developing a computerized system tailored for identifying these prevalent weapons, with a focus on enhancing public safety in both military and civilian contexts. Leveraging recent advancements in deep learning, a gun and knife detection system is introduced by utilizing YOLO v7 and YOLO v8 frameworks. Our system provides bounding box annotations for detected weapons, facilitating efficient monitoring and detection. Evaluation results underscore the effectiveness of the YOLOv7-e6 model, exhibiting a notable increase in mean Average Precision (mAP) compared to alternative approaches, thereby enhancing the detection capability of guns and knives. This research contributes to the ongoing efforts to improve security measures and protect communities from the pervasive threat of armed violence. © 2024 IEEE.

关键词： Object detection

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Load-Aware Resource Scheduling in Fog Computing Based Delay-Sensitive IoT Networks

Load-Aware Resource Scheduling in Fog Computing Based Delay-...

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2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Rajya Lakshmi, L. Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani India

ISBN: (纸本)9798350302523

Internet of Things (IoT), with its capability to connect anything, anywhere, anytime, bringing in revolutionary changes in data collection, processing and utilization. IoT networks generate a huge amount of data, which is difficult to process and store on IoT devices. Cloud computing with its huge availability of resources, can process and store these huge volumes of data. However, owing to the long distance between the cloud and IoT devices, the cloud computing suffers the issues like high latency, network congestion and unreliability. To address these issues, fog computing, which is a decentralized computing paradigm has been proposed as an extension to cloud computing. To utilize the fog resources efficiently and to avoid over-utilization and under-utilization of resources, this paper proposes a resource scheduling algorithm which is capable of balancing the load on fog nodes while satisfying the delay requirements of IoT applications. Extensive simulations are conducted to evaluate the performance of the proposed method. The simulation results establish that the load is almost equally distributed among the fog nodes in the network in most of the traffic arrival scenarios. © 2023 IEEE.

关键词： Fog computing

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computer Modeling of the Distribution of Pollutants in the Bed of the Volga River and the Akhtuba River 5

Computer Modeling of the Distribution of Pollutants in the B...

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5th International Conference on Control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Baskakov, Eugenii Savin, Egor Kornaukhova, Mariya Volgograd State University Dept. Information Systems and Computer Modeling Volgograd Russia

ISBN: (纸本)9798350309126

The process of describing the distribution of impurities is described using two examples: phenomena in the bed of the Volga River in the northern section of the Volga-Akhtuba floodplain without a source under the influence of velocity fields and the flow of impurities from two sources. computer simulation is implemented on the basis of software developed using supercomputer technologies written using the CUDA software and hardware architecture. A mathematical model is proposed that describes the process of transfer of impurities in the aquatic environment. The process of developing and testing a software package is presented. The functionality of the created program allows you to predict the speed and range of propagation of various impurities in a continuous medium with a visual representation. The software package consists of two modules: calculation and visualization. The calculation module is implemented by transferring the computational load to the video card. The graphics module is implemented in Python. The results of numerical modeling of the distribution of pollutants in the Volga River bed are presented. The obtained result shows agreement with the real situation and the results of other researches. © 2023 IEEE.

关键词： Rivers

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LSFuseNet: Dual-Fusion of Landsat-8 and Sentinel-2 Multispectral Time Series for Permutation Invariant Applications 10

LSFuseNet: Dual-Fusion of Landsat-8 and Sentinel-2 Multispec...

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10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023

作者： Kaur, Arshveer Goyal, Poonam Goyal, Navneet Bits Pilani Department of Computer Science and Information Systems Pilani Campus Rajasthan India

ISBN: (纸本)9798350345032

Satellite data provides valuable insights into environmental changes and natural resource management, such as monitoring deforestation, mapping land use changes, and identifying areas at risk of soil degradation. Landsat-8 and Sentine1-2 are the publicly available high spatial resolution satellites launched in recent years. But, both have a moderate temporal resolution which limits their use in the applications like precision agriculture, land cover mapping, disaster monitoring, etc. For such applications, daily or weekly monitoring is better suited. Fusing data from the two satellites can provide enhanced observations. Both Landsat-8 and Sentine1-2 satellites have the same geographic coordinate systems which makes them amiable for fusion. But, fusing data at the pixel level for these satellites is challenging as they visit the same location on different days. The proposed model 'LSFuseNet' effectively fuses data at the feature level. It is a dual-fusion model in which bi-directional cross-modal attention is used to identify and exchange the hotspot information in the two modalities. A feature alignment module learns the fine-grained features and mitigates the noise in the data. We have innovatively applied contrastive learning to improve the quality of the learned representations of the data from the two satellites. We evaluate our model for two applications - crop yield prediction and snow cover prediction. For crop yield prediction, we have taken two crops, viz. corn, and soybean, for approximately 500 counties in the US. For snow cover prediction, we considered approximately 1300 US counties. Our extensive experiments show that LSFuseNet outperforms competing models. Also, the benefit of fusing the data from two satellites over using the data from a single satellite is evident from the results of both applications. We have further modified the model to include meteorological and/or soil data (if applicable) to further enhance the performance of the model. © 2023

关键词： Forecasting

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Development of a Robot Arm with Neural Network Control 3

Development of a Robot Arm with Neural Network Control

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3rd International Conference on Technology Enhanced Learning in Higher Education, TELE 2023

作者： Zhitenko, Maxim Polyakov, Maxim Volgograd State University Depart. of Information Systems and Computer Modeling Volgograd Russia

ISBN: (纸本)9798350326567

This article is devoted to the development of a robot arm with neural network control. This article presents the process of creating the electronic component of the device and design based on 3D printing technologies. The article describes the process of creating a prototype of a robot and its hardware, as well as software for controlling and training a neural network. We present the principles of operation of the neural network, its architecture, and training stages. We demonstrate the effectiveness of neural network control compared to traditional methods of robot arm control for some tasks. This article can be useful for both specialists in the field of robotics and in the field of education. © 2023 IEEE.

关键词： Intelligent systems

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Log Preservation in Custody Dual Blockchain With Energy Regime and Obfuscation Shuffle

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IEEE Transactions on Network Science and Engineering 2024年第4期11卷 3495-3511页

作者： Chew, Chit-Jie Lee, Wei-Bin Chen, Tzu-Hao Lin, Iuon-Chang Lee, Jung-San Feng Chia University Department of Information Engineering and Computer Science Taichung407 Taiwan Hon Hai Research Institute Information Security Research Center Taipei236 Taiwan National Chung Hsing University Department of Management Information Systems Taichung402 Taiwan

The rise of cybercrime has led to an explosion of threats in recent years, resulting in the increasingly important of digital forensics. To achieve successful investigation and prosecution, the log preservation is a critical aspect of incident response. The blockchain technology is considered as one of the powerful log storage mechanism, which bringing data transparency and non-tempering property aside from taking off the hypothesis of a trusted third party. Nevertheless, the sensitivity of the data makes it vulnerable to malicious attacks, and the adoption of blockchain has significantly burdened participants regarding consensus time and storage. To address these challenges, we aim to develop a custody dual blockchain (CDB) to secure the confidentiality and integrity of log data. The design of reputation consensus attains that only attractive participants need to offer computation and storage for efficiency enhancing, while the energy regime constructs a positive feedback loop that invigorates users to contribute to CDB. Moreover, the obfuscation shuffle effectively obscures the explicit log plaintext to prevent illegal access. Thus, the confidential data on the CDB nodes can even be used as evidence of digital forensics. Simulation results have demonstrated that the ledger overhead can be significantly reduced, and CDB could pass the robust examination of two formal proof tools: Proverif (Blanchet et al. 2021) and AVISPA. © 2013 IEEE.

关键词： Blockchain

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IoT-Enabled Ethanol Detection System: A Transformative Stride in Ensuring Food Authenticity

IoT-Enabled Ethanol Detection System: A Transformative Strid...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Deenadayalan, T. Deepakraj, E. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350315127

In the present era, the rapid rise of IoT technology has brought forth innovative solutions, particularly in ensuring food safety. This study introduces an IoT-based ethanol detection system tailored to preserve the quality and security of food products. Ethanol, a key indicator of food spoilage, can endanger consumer well-being in excessive amounts. Food adulteration with hazardous substances like ethanol poses a serious public health challenge;the Food Safety and Standards Authority of India (FSSAI) reported that 30% of tested food samples (2017-2019) exceeded safe ethanol levels. Addressing this, we present an IoTbased Ethanol Detection System (IoTEDS), merging sensitive ethanol sensors and IoT tech. IoTEDS monitors ethanol levels in real-time across the food supply chain, enabling remote data analysis and swift risk detection. Through advanced algorithms, the system processes sensor data, generating precise alerts to prevent compromised product distribution. This tech enhances food safety, cuts losses, and safeguards public health. By incorporating touch-sensitive ethanol sensors with IoT capabilities, this research pioneers modern quality assurance, fostering transparent supply chains and industry accountability. IoTEDS' real-time monitoring, data analysis, and risk mitigation position it as a transformative stride in ensuring food authenticity. © 2023 IEEE.

关键词： Quality control

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Problem of Improving the Environmental and Socio-Economic System of the Volga-Akhtuba Floodplain: Position Examination of Large Dam in the Volga River Based on Computational Fluid Dynamics 5

Problem of Improving the Environmental and Socio-Economic Sy...

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5th International Conference on Control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Klikunova, Anna Yu. Agafonnikova, Ekaterina O. Khoperskov, Alexander V. Volgograd State University Dept. Information Systems and Computer Modeling Volgograd Russia

ISBN: (纸本)9798350309126

The system of hydroelectric power stations on the Volga River, built in the 20th century, completely changed the hydrological regime of the Volga River. The current over-regulation of the water flow is the cause of the accelerating degradation of the Volga-Akhtuba floodplain. Modern analysis of water availability indicates a critical point associated with the spring filling of the Akhtuba binnacle. Various projects suggest a solution to this problem. This study is aims at identifying the most optimal location of a large dam below the Volzhsky hydroelectric power stations. We use a new model of hydraulic resistance based on the combined effect of both the friction force in the Manning model and the turbulent viscosity. Our study involves varying of the dam's location in a numerical shallow water model. We calculate the hydrological relationship between the release of water through the Volzhsky dam and the source of the Akhtuba, as a target function for analyzing the effectiveness of the project to construction such a dam. © 2023 IEEE.

关键词： Dams

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Utilizing MODIS Fire Mask for Predicting Forest Fires Using Landsat-9/8 and Meteorological Data 10

Utilizing MODIS Fire Mask for Predicting Forest Fires Using ...

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10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023

作者： Gupta, Yash Goyal, Navneet Varghese, Vishal John Goyal, Poonam Bits Pilani Department of Computer Science and Information Systems Pilani Campus Rajasthan India

ISBN: (纸本)9798350345032

Recent years have seen some of the largest forest fires ever, including the 2020 California megafires and the Australian bushfires, causing billions of dollars in property damage and destroying millions of acres of green reserves. The subject of forest fires becomes even more alarming when viewed in conjunction with the increasingly concerning problems of climate change and global warming. The planning regarding prevention and mitigation of forest fires and management of nearby areas can greatly benefit from an accurate prediction model. The objective of this study is to develop deep learning models which use satellite images and meteorological data to pinpoint potential fires at a pixel granularity. Data from the recently launched Landsat-8 and Landsat-9 satellite systems have been used to predict forest fires at a spatial resolution of 30m. The proposed solution uses the comprehensive geographical, meteorological, and MODIS-based fire history of the region, integrated from different data sources with pixel-level reprojection, as a multivariate time series (MVTS) to model the prediction problem as a binary classification problem. We adopt an encoder-classifier architecture: the BiLSTM-attention-based encoder is trained with supervised contrastive learning, while the fully-connected classifier is optimized against a weighted loss for increased recall. Our experiments demonstrate that the proposed model is robust to spatial and temporal variations in occurrence of fires, thereby making its deployment possible in any region of the world. With a mean AUC of 0.99, our proposed model outperforms the existing forest fire prediction models. © 2023 IEEE.

关键词： Landsat

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