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Computing and Machine Intelligence (ICMI), International Conference on

作者： Chiragee C. Joshi Jaya S. S. K. Payyavula Soham Patel Yasser M. Alginahi Electrical and Computer Engineering Dept. University of Windsor Windsor Canada Computer Science Dept. Adrian College Michigan USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

This paper provides a comprehensive study on prediction and detection of wildfire using Machine Learning and Deep Learning algorithms. Due to the current environmental trends, wildfire possess a great threat to the ecosystem and human lives at a great cost. Multiple factors are the root cause for wildfires which include environmental factors like temperature, humidity, air pressure index, forest terranean, vegetation. Taking these factors into consideration, a Machine Learning model was built considering diverse algorithms to learn the previous trends and predict future wildfires instances. Based on the satellite imagery of previous wildfires, using CNN and AlexNet algorithms to detect wildfires that are currently taking place for early detection so to contain and control the fire without it causing any damage. Amalgamating these two algorithms, in a single graphical user interface, enhances user accessibility and convenience, providing an invaluable tool in wildfire management. The algorithm achieved an accuracy of average 96.33 % to predict wildfires and was able to detect them based on images at the rate of 93.66%.

关键词： Temperature sensors Wildfires Machine learning algorithms Accuracy Prediction algorithms Market research Data models

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Multiclass Brain Tumor Detection using Deep Transfer Learning 1

Multiclass Brain Tumor Detection using Deep Transfer Learnin...

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1st International Conference on Cognitive Computing and engineering Education, ICCCEE 2023

作者： Wagh, K.S. Dhotre, Aishwarya Lagad, Krishna Yadav, Aakanksha Kamble, Aishwarya All India Shri Shivaji Memorial Society's Institute of Information Technology Pune Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350332803

Brain tumor are lumps or growths of aberrant cells in the brain. It is essential to categorise brain tumours in order to evaluate the tumours and select the appropriate course of therapy for each class. It is the most crucial phase for figuring out whether a problem poses a threat to life and interferes with how effectively the human body typically operates. The classification method for brain tumours provides efficient treatment for patient recovery and diagnostics. To identify the pathological disorders that occur in the brain, likewise can employ a range of medical pictures technologies and methods. Magnetic resonance pictures (MRI), which produces images of greater quality and emits no ionising radiation, is frequently used in medical pictures. Because of the great visual quality, it is used. The usefulness of deep learning, a subset of AI, in the field of medical diagnosis applications has opened the way for speedy improvements in brain tumour detection from MRI to the higher prediction rate. Digital image processing plays a number of crucial functions in the analysis and diagnosis of medical pictures. Brain tumours come in many different forms. Brain tumours may be benign (noncancerous) or malignant (cancerous) (malignant). The distinction between abnormal and normal brain tissue is made in the categorization of brain tumours. The most comprehensive and widely used deep learning method for analysing, segmenting, and categorising brain tumours is convolution neural network (CNN). In this work, likewise compare the abilities of the pre-trained CNN models VGG-16, ResNet-50, and Inception-v3 to identify and forecast brain tumour cells automatically. Brain tumour MRI images are used to present pre-trained models. The three types of brain tumors - glioma, meningioma, and pituitary - are to be divided into separate groups. © 2023 IEEE.

关键词： Magnetic resonance

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Data-driven Forecasting of Weather in Bangladesh Leveraging Transformer Network and Strong Inter-feature Correlation 25

Data-driven Forecasting of Weather in Bangladesh Leveraging ...

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25th International Conference on computer and Information technology, ICCIT 2022

作者： Islam, Zahidul Fariha, Raisa Islamic University of Technology Dept. of Computer Science and Engineering Gazipur1704 Bangladesh

ISBN: (纸本)9798350346022

Accurate weather forecasting is indispensable for countries like Bangladesh because of their reliance on agriculture and vulnerability to frequently occurring natural disasters such as floods, cyclones, and riverbank erosion. Bangladesh has a fairly regular annual weather pattern where the weather features, such as temperature, humidity, and rainfall, are highly correlated. Leveraging this inter-feature correlation between temperature and rainfall, we propose a flexible transformer based neural network that can forecast monthly temperature or rainfall by analyzing the past few data-points of any one or both of these weather features. We evaluated the proposed method using a public dataset called Bangladesh Weather Dataset which contains 115 years of Bangladesh weather data comprising month-wise average temperature and rainfall measurements. Our method demonstrates substantial improvements over the previously proposed approaches for this task in both metrics- mean squared error and mean absolute error. Our proposed transformer network is also significantly more lightweight and computationally efficient and accurate. This transformer aided method would pave a way to understand and leverage the complex relationship between the weather features and open up possibilities of coming up with even more robust methods of forecasting weather data. © 2022 IEEE.

关键词： Weather forecasting

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A Three-Terminal Output AC/DC Converter With Automatic Power Decoupling and Common Ground

A Three-Terminal Output AC/DC Converter With Automatic Power...

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Annual Conference of Industrial Electronics Society

作者： Zhehao Wu Weimin Wu Houqing Wang Frede Blaabjerg Mohamed Orabi Liang Yuan Dept. of Electrical Engineering Shanghai Maritime University China Dept. of Electrical Engineering Anhui University of Science and Technology Huainan China Dept. of Electrical Engineering University of Arkansas Fayetteville United States Dept. of Energy Technology Aalborg University Aalborg Denmark Dept. of Electrical Engineering Aswan University Aswan Egypt Dept. of Safety Science and Engineering Anhui University of Science and Technology Huainan China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

As the search for sustainable, environmentally friendly energy sources continues, DC devices such as new energy-electric vehicles and energy-saving LED lights have been actively promoted. Consequently, the necessity for the DC microgrid in grid system development will become increasingly apparent, and the research on three-terminal output converters is increasing. Besides, double-frequency ripple power is an inherent problem in single-phase power electronic systems. This paper proposes a three-terminal output AC/DC converter with automatic power decoupling and common ground for the DC microgrid as a solution to the issues above. The equivalent circuit has been subjected to a detailed analysis to elucidate its underlying operating principles. Due to the structure of the common ground, the common mode leakage current can be eliminated in this converter. In addition, the implementation of automatic power decoupling enables the replacement of electrolytic capacitors with film capacitors, leading to higher power density and longer lifespan. Furthermore, the employed deadbeat control has a fast dynamic response capability. Finally, the simulation results are presented to verify the theoretical analysis and the performance of this converter.

关键词： Dynamic response AC-DC power converters Power system measurements Density measurement Simulation Capacitors Microgrids Robustness Power electronics Leakage currents

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Energy aware firefly based sink mobility data routing in wireless sensor networks 2

Energy aware firefly based sink mobility data routing in wir...

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2nd International Conference on Next Generation Intelligent Systems, ICNGIS 2022

作者： Jaiswal, Kavita Anand, Veena National Institute of Technology Raipur Dept. of Computer Science and Engineering Chhattisgarh India

ISBN: (纸本)9781665467926

In a sink mobility-based Wireless Sensor Network, the sink node follows a path through the network region, gathering data from neighbouring sensor nodes. Sink mobility reduces the distance between the average source node and the sink, hence conserving energy during network transmission. The Firefly Algorithm (FA) is used to offer a WSN sink mobility technique in this paper. An appropriate sink position saves the most node energy in data transmission. To select the ideal sink site for a cluster, the FA generates a chromosomal population. A network energy consumption model that implements the FA process's fitness evaluation operator is also proposed. The FA model creates a set of suitable sink sites on the trajectory for each cluster. FA-based sink mobility extends network lifetime more than other protocols, according to the research. © 2022 IEEE.

关键词： Energy efficiency

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Probabilistic Modelling and Safety Assurance of an Agriculture Robot Providing Light-Treatment 19

Probabilistic Modelling and Safety Assurance of an Agricultu...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Adam, Mustafa Ye, Kangfeng Anisi, David A. Cavalcanti, Ana Woodcock, Jim Morris, Robert Robotics Group Faculty of Science & Technology Norway University of York Dept. of Computer Science United Kingdom Faculty of Engineering and Science Dept. of Mechatronics Norway Saga Robotics As Norway

ISBN: (纸本)9798350320695

Continued adoption of agricultural robots postulates the farmer's trust in the reliability, robustness and safety of the new technology. This motivates our work on safety assurance of agricultural robots, particularly their ability to detect, track and avoid obstacles and humans. This paper considers a probabilistic modelling and risk analysis framework for use in the early development phases. Starting off with hazard identification and a risk assessment matrix, the behaviour of the mobile robot platform, sensor and perception system, and any humans present are captured using three state machines. An auto-generated probabilistic model is then solved and analysed using the probabilistic model checker PRISM. The result provides unique insight into fundamental development and engineering aspects by quantifying the effect of the risk mitigation actions and risk reduction associated with distinct design concepts. These include implications of adopting a higher performance and more expensive Object Detection System or opting for a more elaborate warning system to increase human awareness. Although this paper mainly focuses on the initial concept-development phase, the proposed safety-assurance framework can also be used during implementation, and subsequent deployment and operation phases. © 2023 IEEE.

关键词： Agricultural robots

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques

Sensitive Content Detection in Social Networks Using Deep Le...

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Big Data, Cloud Computing, Data Science & engineering (BCD)

作者： Isidoros Perikos Computer Engineering and Informatics Department University of Patras Patras Greece Electrical and Computer Engineering Dept. Computer Technology Institute and Press “Diophantus” University of Peloponnese Patras Greece

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience.

关键词： Deep learning Privacy Sensitivity Accuracy Social networking (online) Decision making Predictive models Transformers User experience Cultural differences

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A Collaboration Federated Learning Framework with a Grouping Scheme against Poisoning Attacks 6

A Collaboration Federated Learning Framework with a Grouping...

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6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Liu, Chuan-Kang Chiang, Chi-Hui National Chin-Yi University of Technology Dept. of Artificial Intelligence and Computer Engineering Taichung Taiwan Chia Nan University of Parmacy and Science Dept. of Information Management Tainan Taiwan

ISBN: (纸本)9798350301953

Federated learning has been regarded as emerging machine learning framework due to its privacy protection. In the IoT trend, federated learning enables edge clients to predict or classify local detected data with a global model that is computed by a FL server through the aggregation of all local models trained by a base FL algorithm. However, meanwhile, its distributed nature also brings several security challenges. Poisoning attacks are the main security risks that can easily and efficiently affect the accuracy of the global learning model. Previous work proposed a voting strategy which can predict the label of the input robustly no matter the attacks the malicious users use. However, its accuracy also easily falls down as the number of malicious user increases while the number of groups is fixed. This paper proposes a new attack defense algorithm against poisoning attacks in federated learning. This paper uses ID-distribution features to group all clients, including normal and malicious ones. The main idea of this proposed scheme is to put those potential malicious clients in specified groups. Hence, the resulting vote output can accurately classify the dataset inputs, regardless of the number of the groups the learning framework has. Our analytical results also show that our scheme exactly perform better compared to original voting scheme. © 2023 IEEE.

关键词： Classification (of information)

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Optimization of a three-phase Induction Motor for Electric Vehicles Based on Hook-Jews Optimization Method

arXiv

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arXiv 2023年

作者： Mousaei, Arash Mohammadabadi, Sahar Aziz Dept. Electrical and Computer Engineering University of Tabriz Tabriz Iran Dept. Electrical and Computer Engineering University of Pisa Pisa Italy

In this paper, the Hook-Jews (HJ) optimization method is used to optimize a 3-phase Squirrel-Cage Induction Motor (SCIM) as an Electric Vehicle's (EV) motor. Optimal designs with different numbers of poles, different nominal and maximum speeds, and different numbers of grooves are compared and the best one is selected. The optimization method used has advantages such as simple programming, omission of gradients, short convergence times, and the possibility of changing individual parameters. Design parameter variations for optimal designs with rated speeds for 2-pole and 4-pole motors are shown and explained. The results show that his 2-pole motor with the rectangular stator and rotor slots and a rated speed of 1800 rpm has the highest efficiency. © 2023, CC BY.

关键词： Optimal systems

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Runtime Detection of Time-Delay Security Attack in System-An-Chip 15

Runtime Detection of Time-Delay Security Attack in System-An...

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15th IEEE/ACM International Workshop on Network on Chip Architectures, NoCArc 2022

作者： Rajan, Manju Choksey, Mayank Jose, John Indian Institute of Technology Dept. of Computer Science and Engineering Assam Guwahati India

ISBN: (数字)9781665455893

ISBN: (纸本)9781665455893

Soft real-Time applications, including multimedia, gaming, and smart appliances, rely on specific architectural characteristics to deliver output in a time-constrained fashion. Any violation of application deadlines can lower the Quality-of-Service (QoS). The data sets associated with these applications are distributed over cores that communicate via Network-on-Chip (NoC) in multi-core systems. Accordingly, the response time of such applications depends on the worst-case latency of request/reply packets. A malicious implant such as Hardware Trojan (HT) that initiates a delay-of-service attack can tamper with the system performance. We model an HT that mounts a time-delay attack in the system by violating the path selection strategy used by the adaptive NoC router. Our analysis shows that once activated, the proposed HT increases the packet latency by 17% and degrades the system performance (IPC) by 18% over the Baseline. Furthermore, we propose an HT detection framework that uses packet traffic analysis and path monitoring to localise the HT. Experiment results show that the proposed detection framework exhibits 4.8% less power consumption and 6.4% less area than the existing technique. © 2022 IEEE.

关键词： Network-on-chip

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