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12th Indian Conference on computer Vision, Graphics and Image Processing, ICVGIP 2021

作者： Manu, Chippy M. Sreeni, K.G. Computer Vision Lab Dept. Electronics and Communication College of Engineering Kerala Trivandrum India

ISBN: (纸本)9781450391276

This paper presents a novel algorithm to dehaze a given hazy input image using a Multi-Stage progressive image Dehazing Network (MSDNet) architecture. The proposed multi-stage strategy framework splits the challenge of image recovery into sub-tasks for a degraded image to be gradually restored. The network is trained with images from two benchmark datasets - RESIDE and NTIRE 2021. Experimental evaluations have been conducted with several hazy images from different datasets. The performance evaluation of MSDNet is done using various metrics such as Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), Feature Similarity Metric (FSIM), Visibility Index (VI), Realness Index (RI), Fog Aware Density Evaluator (FADE), Naturalness Image Quality Evaluator (NIQE) and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). An average PSNR of 27.687, 36.25, 34.98, 20.52, and 20.97 have been obtained with images from ITS, SOTS indoor, SOTS outdoor, Dense-Haze, and NH-Haze datasets, respectively. The experimental results reveals the enhanced performance of MSDNet compared to other state-of-the-art techniques. © 2021 ACM.

关键词： Image reconstruction

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Automated Weed Species Classification in Rice Cultivation Using Deep Learning

Automated Weed Species Classification in Rice Cultivation Us...

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International Conference on Electrical, computer and communication engineering (ECCE)

作者： Hamdadur Rahman Mahbub Alam Khan Shakil Khan Zishad Hossain Limon Md Ismail Hossain Siddiqui Shimanto Krishna Chakroborty Shamim Forhad Abdullah Al-Sakib Sheikh Shemanto Afridi Shafiur Rahman Dept. of Management of Information System International American University CA USA Dept. of Management Information System Pacific State University CA USA Dept. of Business Analytics International American University CA USA Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Engineering/Industrial Management Westcliff University Irvine CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University CA USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Weeds in rice fields compete for essential resources such as water, nutrients, and light, leading to reduced crop yields, increased production costs, and environmental concerns. Traditional methods of weed detection are often labor-intensive, subjective, and inefficient, highlighting the need for automated approaches in precision agriculture. This study introduces an ensemble model designed for robust and accurate classification of rice weeds, addressing the shortcomings of conventional methods and enhancing detection scalability. The proposed ensemble model combines four deep learning architectures: EfficientNet-V2, SqueezeNet, InceptionResNet-v2, and DenseNet-201. By leveraging the complementary strengths of these models, we aim to improve performance. The study utilizes the Rice Field Weed Detection Dataset, which contains 3,632 high-resolution images that have been augmented to a total of 21,792 samples. Advanced preprocessing techniques such as resizing, normalization, and augmentation are applied to ensure effective feature extraction. Our model achieved a remarkable classification accuracy of 99.51%, significantly surpassing individual models and other state-of-the-art approaches. In addition, we developed a web-based application for real-time weed detection, providing a user-friendly platform for farmers and agricultural professionals to implement timely weed management strategies. This system supports sustainable farming by reducing the need for excessive herbicide use and promoting environmentally friendly practices. This research highlights the potential of ensemble deep learning for precise agricultural weed detection. Future work will focus on integrating diverse datasets and developing mobile applications to enhance accessibility and scalability.

关键词： Deep learning Accuracy Scalability Transfer learning Feature extraction Real-time systems Mobile applications Crop yield Water resources Farming

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Understanding the Impact of AI-driven Clinical Decision Support systems 15

Understanding the Impact of AI-driven Clinical Decision Supp...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Mohammed, Abdul Sajid Neravetla, Anuteja Reddy Nomula, Varun Kumar Gupta, Ketan Dhanasekaran, S. University of the Cumberlands Dept of School of Computer and Information Sciences KY United States University of the Cumberlands Department of Information Technology KY United States Georgia Institue of Technology Department of Analytics Industrial & Systems Engineering United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of engineering Department of Electronics and Communication engineering Coimbatore641202 India

ISBN: (纸本)9798350370249

Medical selection aid structures (CDSSs) are facts systems designed to assist healthcare experts in the decision-making manner. Conventional CDSSs rely upon analytical fashions and understanding-based algorithms to guide choices. However, the latest advances have visible the emergence of AI-pushed CDSSs that utilize artificial intelligence (AI) to help clinicians in their selection-making. AI-pushed CDSSs are predicted to enhance selection-making accuracy and results by way of leveraging massive datasets and greater state-of-the-art modeling strategies. But, there is nonetheless a lot to understand about the impact of AI-driven CDSSs on healthcare shipping and results. This paper evaluates the present-day literature on the impact of AI-driven CDSSs, exploring the results of this generation for healthcare experts and the healthcare gadget as an entire. It highlights areas in need of additional research and suggests possibilities for the design and implementation of AI-pushed CDSSs. © 2024 IEEE.

关键词： Decision making

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Polarimetric HRRP Recognition Using Vision Transformer with Polarimetric Preprocessing and Attention Loss

Polarimetric HRRP Recognition Using Vision Transformer with ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Fan Gao Dawei Ren Junjun Yin Jian Yang Dept. of Electronic Engineering Tsinghua University Beijing China School of Computer and Communication University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Polarimetric High-Resolution Range Profile (HRRP) holds significant potential in Radar Automatic Target Recognition (RATR) due to its ability to provide detailed polarimetric and spatial information. In recent years, deep learning methods have been extensively applied in RATR based on polarimetric HRRP. However, these methods often focus solely on local or temporal features, thereby not fully utilizing the spatial information. Additionally, valuable polarimetric information is frequently overlooked. Moreover, most of these methods do not distinguish between target and noise areas, resulting in a lack of emphasis on crucial range cells. This paper introduces a method for polarimetric HRRP recognition based on the Vision Transformer (ViT), which effectively extracts both local and temporal features. Our approach incorporates a polarimetric preprocessing step in which manual features and Convolutional Neural Networks (CNNs) are combined to enhance the extraction of polarimetric features. To direct the network’s focus toward significant range cells, we design a novel attention loss. Experimental results demonstrate that our proposed method improves recognition accuracy and maintains robustness in noisy environments.

关键词： computer vision Target recognition Noise Scattering Radar Feature extraction Transformers

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A Secure Token-Based Approach for DHCP Client Authentication and Replay Attack Prevention

A Secure Token-Based Approach for DHCP Client Authentication...

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International Conference on computer and Information Technology (ICCIT)

作者： Asaduzzaman Jony Muhammad Nazrul Islam Rahat Ahmed Talukder Dept. of Information and Communication Technology Bangladesh University of Professionals (BUP) Dhaka Bangladesh Dept. of Computer Science and Engineering Military Institute of Science and Technology (MIST) Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Dynamic Host Configuration Protocol (DHCP) plays a crucial role for assigning IP address and other related parameters to clients. Due to lack of client authentication and anti reply mechanism the DHCP protocol is susceptible to DHCP starvation attack. Although most of the existing researches proposed digital certificate based DHCP client authentication that involves a trusted third party named certificate authority (CA), but securing the private key of certificate authority is very challenging and risky task. Again, traditional MAC binding technique has been used to authenticate DHCP clients which could be easily evaded by MAC spoofing attack. Moreover, SMS based one time password (OTP) is also suggested to authenticate DHCP clients which is susceptible to Man-In-the-Middle (MITM) attack as OTP is transmitted in plain text. Therefore, the objective of this research is to propose an effective DHCP framework to authenticate DHCP clients and prevent DHCP replay attack. To achieve this objective, at first, DHCP starvation and DHCP replay attack and their consequences are briefly demonstrated. Secondly, a python-based secured DHCP framework was proposed that authenticates DHCP clients and prevents DHCP replay attack by exchanging security tokens uniquely generated from clients Pre-Shared Key (PSK), MAC address, PIN and system time. Finally, the effectiveness and validation of the proposed DHCP framework is demonstrated using the Multivendor Network Emulation Software (EVE-NG).

关键词： Phase shift keying Protocols Prevention and mitigation Authentication Public key Production Passwords Software Pins Servers

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Vulnerability Testing on IoT Networks Using Machine Learning Algorithm

Vulnerability Testing on IoT Networks Using Machine Learning...

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2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Mohandas, R. Prasanna, D.S.John Deva Baiju, B.V. Nathiya, S. Rajkumar, T. Dhiliphan Sivashankar, C. Srm Institiute Of Science And Technology Department Of Computational Intelligence Chennai Kattankulathur India Srm Institiute Of Science And Technology Department Of Datascience And Business Systems Chennai Kattankulathur India Vit University TamilNadu Vellore India Prince Shri Venkateshwara Padmavathi Engineering College Department Of Electronics And Communication Engineering Ponmar Chennai India Kalasalingam Academy Of Research And Education Dept. Of Computer Science And Engineering Tamilnadu Krishnankovil India Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks;therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities. © 2024 IEEE.

关键词： Internet of things

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Building Collaborative Learning: Exploring Social Annotation in Introductory Programming

arXiv

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arXiv 2024年

作者： de Oliveira Neto, Francisco Gomes Dobslaw, Felix Chalmers The University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden Mid Sweden University Dept. of Quality Mngmt Communication and Inf. Systems Östersund Sweden

The increasing demand for software engineering education presents learning challenges in courses due to the diverse range of topics that require practical applications, such as programming or software design, all of which are supported by group work and interaction. Social Annotation (SA) is an approach to teaching that can enhance collaborative learning among students. In SA, both students and teachers utilize platforms like Feedback Fruits, Perusall, and Diigo to collaboratively annotate and discuss course materials. This approach encourages students to share their thoughts and answers with their peers, fostering a more interactive learning environment. We share our experience of implementing social annotation via Perusall as a preparatory tool for lectures in an introductory programming course aimed at undergraduate students in Software engineering. We report the impact of Perusall on the examination results of 112 students. Our results show that 81% of students engaged in meaningful social annotation successfully passed the course. Notably, the proportion of students passing the exam tends to rise as they complete more Perusall assignments. In contrast, only 56% of students who did not participate in Perusall discussions managed to pass the exam. We did not enforce mandatory Perusall participation in the course. Yet, the feedback from our course evaluation questionnaire reveals that most students ranked Perusall among their favorite components of the course and that their interest in the subject has increased. © 2024, CC BY.

关键词： Students

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Developing and Implementing An Effective Big Data Classification system

Developing and Implementing An Effective Big Data Classifica...

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Integrated Circuits and communication systems (ICICACS), IEEE International Conference on

作者： Manjunath Managuli Sagar Pujar Sangamesh C Managuli Krishnamurthy K. T Rajesh Nandalike Pavan Kunchur Dept. of Electronics & Communication KLS Gogte Institute of Technology Belagavi India Dept. of Computer Science KLS Gogte Institute of Technology Belagavi India Dept. of Mechanical Engineering PES University Bangalore India Dept. of Electronics & Instrumentation B M S College of Engineering Bangalore India Dept. of Electronics & Communication Nitte Meenakshi Institute of Technology Bangalore India

Relevant data Technology advancements have led to a massive growth in classifications. Deep learning systems, however, need help handling such a massive volume of data. This work aims to use the Evaluation of different classification algorithms for big data, Comparison of accuracy, precision, recall, and F1 score among various algorithms, and Identification of algorithms suitable for handling large-scale datasets and conventional deep learning to analyze a large amount of data. Classifiers allowed researchers to examine how various random sampling strategies affected the model's performance. Two methods of feature selection were employed in the tests. The Bayesian Network likewise employs a single deep learning classifier. By lowering the dimensionality of the data, this study seeks to maximize model performance. A recording dataset larger than 60 GB was made less dimensional to conduct the trials. The findings demonstrate that, when employing both sampled and non-sample data, overall performance varies between 78% accuracy and 82% accuracy, a minor variation in performance while utilizing both sampled and non-sample (all the data) datasets. The random subset approach and the Bayesian network classifier were employed in the trials, and the overall findings show that the variety experiment had the best performance out of all the combinations of experiments. Test Cradle Experimentation: Variation length, delay variation, and packet loss are used to assess the predicted scheme's efficiency.

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Unveiling Disparities: NLP Analysis of Software Industry and Vocational Education Gaps

Unveiling Disparities: NLP Analysis of Software Industry and...

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Natural Language-Based Software engineering (NLBSE), IEEE/ACM International Workshop on

作者： Emil Bäckstrand Rasmus Djupedal Lena-Maria Öberg Francisco Gomes De Oliveira Neto Dept. of Quality Management Communication and Inf. Systems Mid Sweden University Östersund Sweden Dept. of Computer Science and Engineering Chalmers|University of Gothenburg Gothenburg Sweden

ISBN: (数字)9798400705762

ISBN: (纸本)9798350352559

The rapid growth of software industry highlights the importance of the education system in producing competent professionals to meet industry demands. Previous research has identified a gap between industry needs and the content of educational programs. This study presents Vocational Education and Labour Market Analyser (VELMA), a tool designed to extract information from job ads and educational curricula (both from Sweden), utilising topic modelling to identify the diverse technologies and skills in demand within the industry and those covered by professional education. Particularly, we use Latent Dirichlet Allocation (LDA) to categorise keywords into cohesive themes for document frequency analysis. Our findings highlight industry demand for skills in cloud and embedded technologies, security engineering, and software architecture. In contrast, the Higher Vocational Education (HVE) curricula emphasise the education of web developers and general object-oriented programming languages. CCS CONCEPTS • Applied computing $\rightarrow$ Document management and text processing; Education; $\cdot$ Software and its engineering $\rightarrow$ Designing software.

关键词： Industries Analytical models Text analysis Object oriented modeling Education Software Natural language processing

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SpeckleDiff-2D: Complex-Valued Speckle Reduction Diffusion Network

SpeckleDiff-2D: Complex-Valued Speckle Reduction Diffusion N...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Danwei Lu Chao Liu Xunhao Lin Junjun Yin Jian Yang Dept. of Electronic Engineering Tsinghua University Beijing China School of Computer and Communication University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

SAR despeckling is a crucial preprocessing step for various SAR applications. This work introduces SpeckleDiff2D, a complex-valued speckle reduction diffusion network. Our approach emphasizes the design of a forward noise-accumulation process that mimics the generation mechanism of speckle noise. The proposed despeckling algorithm is trained exclusively on synthesized speckled images generated by optical dataset and is validated on both simulated and real SAR images. Experimental results demonstrate the method’s efficiency and resilience to domain shifts, positioning it as a promising solution to the persistent challenge of data insufficiency in SAR despeckling design.

关键词： Noise Speckle Diffusion models Optical imaging Radar polarimetry Adaptive optics Optical sensors

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