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Development of a CNN Model for Efficient Leaf Disease Detection

Development of a CNN Model for Efficient Leaf Disease Detect...

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Dagar, Ishant Sharma, Harsh Gusain, Raj Vidyarthi, Anurag Prakash, Rishi Gowri, R. Propagation Research Lab Department of Electronics & Communication Engineering Dehradun India Graphic Era Hill University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350373783

In order to mitigate financial losses for producers and safeguard crops against diseases, the proposed solution presents a CNN model that enables early detection of leaf issues. In the agriculturally dominant nation of India, foliage maladies annually reduce agricultural output by 35%. The process of manually identifying diseases in foliage is time-consuming due to the lack of apparatus and expertise in laboratories required to identify early indicators of such conditions. The following are examples of such diseases: late blight, early blight, black root, bacterial mark, infected leaf, healthy leaf, and so forth. The importance of developing self-governing algorithms to diagnose diseases in leaves through the analysis of visual symptoms has been underscored by recent developments in deep learning and computer vision models. The main aim of the proposed model is to address the challenge of leaf disease diagnosis through the implementation of a straightforward method that minimizes the demand for computational resources. We implemented the multi-layered architecture of convolutional neural networks (CNN) in order to identify and eradicate leaf diseases. A model for image classification is trained utilizing our proposed approach, and it attains an accuracy of 91.5%, surpassing the performance of existing methodologies. © 2024 IEEE.

关键词： Multilayer neural networks

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Generative Adversarial Networks for Synthetic Jamming Attacks on UAVs 9

Generative Adversarial Networks for Synthetic Jamming Attack...

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9th International Conference on computer Science and engineering, UBMK 2024

作者： Sarikaya, Burcu Sönmez Bahtiyar, Şerif Istanbul Technical University Cyber Security and Privacy Research Lab SPFLab Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350365887

Intrusion Detection Systems (IDS) have been significant for Unmanned Aerial Vehicles (UAVs) since high connectivity is essential for such vehicles. Recently, machine learning-based defense mechanisms have contributed to detecting intrusions. Data are crucial to better results with machine learning algorithms. However, security data is not freely distributed so there is a lack of security-related data about UAVs that can be processed with machine learning. In this research, we create synthetic security data for UAVs using Generative Adversarial Networks (GANs) that can be utilized to develop machine learning-based IDS systems. We propose employing Conditional Tabular Generative Adversarial Networks (CTGANs), Gaussian copulas, and Variational AutoEncoders (VAEs) to generate syn-thetic jamming attacks from the widely-used UAV attack dataset. We experimentally evaluated the performance of each model by using many metrics, such as Kullback-Leibler decomposition. Experimental results show that the distribution of CTGAN synthetic data is similar to the distribution of real data. Finally, we created a machine learning-based IDS and applied it to each dataset to validate the success rate of intrusion detection with newly generated synthetic data. It was observed that CTGAN-generated data decreased the IDS accuracy by approximately 25%, while Gaussian copula-generated data resulted in a 20% decrease, and VAE-generated data led to a 1% reduction, all relative to the accuracy achieved with real data. These findings suggest that synthetic data generation holds significant potential for enhancing the security frameworks of UAVs. The experimental results demonstrate that the CTGAN model is particularly effective in producing synthetic data that challenges traditional intrusion detection mechanisms, thereby contributing to the development of more robust IDS capabilities. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancing Employee Promotion Prediction with a Novel Hybrid Model Integrating Convolutional Neural Networks and Random Forest 14

Enhancing Employee Promotion Prediction with a Novel Hybrid ...

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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Ardehkhani, Pouya Moslemi, Seyyed Reza Hooshmand, Haniyeh University of Tehran Deep Learning Research Lab Department of Computer Engineering Faculty of Engineering Tehran Iran University of Tehran Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350349412

In the ever-evolving landscape of human resources, the critical task of identifying employees ready for promotion remains a complex challenge. To address this issue, we propose a novel hybrid model that seamlessly integrates Convolutional Neural Networks (CNNs) with Random Forest. Through a two-step process, we initially train the CNN comprising Conv1D and Dense layers. Subsequently, we harness the extracted features from the Nth layer, merging them with the original dataset. These augmented features are then input into the Random Forest algorithm. This innovative approach has yielded remarkable results, achieving an astounding accuracy rate of 99%. This surpasses the performance of both standalone Random Forest and CNN models, as well as various other machine learning methods. The presented model not only enhances the prediction accuracy for employee promotions but also offers a powerful tool for HR managers seeking to make informed and data-driven decisions in workforce advancement, ultimately contributing to more effective and efficient talent management. © 2023 IEEE.

关键词： Personnel

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Improving Training Stability in Variational Autoencoders Through the Integration of Score Matching Loss 14

Improving Training Stability in Variational Autoencoders Thr...

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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Rad, Amirreza Mokhtari Ardehkhani, Pouya Alborzi, Hormehr University of Tehran Deep Learning Research Lab Department of Computer Engineering Faculty of Engineering Tehran Iran University of Tehran Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350349412

In this research, a Variational Autoencoder (VAE) model was developed, and the CIFAR100 dataset was employed as the primary data source. The problem addressed pertained to the instability in the training process of VAE models. To mitigate this issue, various loss expressions were explored, including the use of score matching loss independently, in conjunction with total variation loss, and in combination with reconstruction loss. The innovative approach revealed that when score matching loss was integrated either with total variation loss or when applied as a standalone loss function, the training process exhibited increased stability. This was evident through smoother loss curves and latent space visualizations that displayed characteristics akin to a normal distribution. As a consequence, this novel approach promises the potential for building more stable generative models, which can significantly enhance the overall training process in VAEs. This innovation provides a valuable contribution to the field of generative modeling, with the prospect of addressing the longstanding challenge of training stability in VAEs, thereby opening avenues for more efficient and effective model development and application. © 2023 IEEE.

关键词： Stability

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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Enhancing the Security of SDN in 5G: A Hybrid Feature Selection Based Ensemble Machine Learning Framework for Classification of Cyber-Attacks

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SN computer Science 2025年第3期6卷 1-16页

作者： Singh, Mahendra Pratap Haimashreelakshmi Singh, Virendra Pratap Gupta, Maanak Cyber Security Research Lab (CSRL) Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangaluru 575025 India Department of Computer Science Tennessee Technological University Cookeville TN United States

In recent years, the adoption of 5G has significantly increased due to its numerous benefits, including high availability, lower latency, improved reliability, and high performance. To manage packet flow, 5G relies on Software-Defined Networking (SDN) that employs software controllers and Application Programming Interfaces (APIs) to route packets and communicate with the hardware, providing advantages like high efficiency, low cost, and dependability. However, due to centralized control, SDN controllers are vulnerable to various cyber-attacks, including Distributed Denial of Service (DDoS), Denial of Service (DoS), Password Brute Forcing, Web Attacks, etc. This paper proposes a framework that comprises a hybrid feature selection method and an ensemble machine learning model. The proposed ensemble model combines the strengths of three different machine learning (ML) classifiers to create a voting classifier for classifying traffic in SDN. Additionally, the optimal value for the hyperparameters of each classifier is obtained through hyperparameter tuning. Finally, the experimental analysis of the proposed model using the InSDN dataset shows 99.96% accuracy, highlighting the proposed model’s effectiveness in addressing the limitations of the existing approaches and detecting multiple attacks in the SDN context. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G Hybrid feature selection Machine learning Software defined networking

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Phishing Classification Based on Text Content of an Email Body Using Transformers

Phishing Classification Based on Text Content of an Email Bo...

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International Conference on Information Security, Privacy, and Digital Forensics, 2022

作者： Somesha, M. Pais, Alwyn R. Department of Computer Science and Engineering Information Security Research Lab National Institute of Technology Karnataka Surathkal575025 India

ISBN: (纸本)9789819950904

Phishing attacks steal sensitive credentials using different techniques, tools, and some sophisticated methods. The techniques include content injection, information re-routing, social engineering, server hacking, social networking, SMS and WhatsApp mobile applications. To overcome such attacks and minimize risks of such attacks, many phishing detection and avoidance techniques were introduced. Among various techniques, deep learning algorithms achieved the efficient results. In the proposed work, a transformers-based technique is used to classify phishing emails. The proposed method outperformed the other similar mechanisms for the classification of phishing emails. The phishing classification accuracy achieved by the proposed work is 99.51% using open-source datasets. The proposed model is also used to learn and validate the correctness of the in-house created datasets. The obtained results with in-house datasets are equally competitive. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic mail

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Explorative Study on Motor Interference During Synchronous Human and Robot Arm Movements Under Varied Presence of a Robot Head

Explorative Study on Motor Interference During Synchronous H...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Kaya, Mertcan Kuhnlenz, Kolja Coburg University of Applied Sciences and Arts Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg Germany

ISBN: (纸本)9781665481090

This paper investigates the influence of a static robot head on deviations of human hand movements from task direction (motor interference) during simultaneous human and robot arm movements using a collaborative robot arm. Synchronous vertical and horizontal human and robot arm movements are conducted in all combinations (within-subjects factors direction and congruency). The presence of the head (head/ no head) is varied as a between-subjects factor. In this first step, only its motionless presence is deliberately focused at, in order to exclude contributions by eye- or head-movement behavior or facial expressions being difficult variables to generalize from. Participants' hands are tracked optically marker-based and deviations from task-related vertical, respectively, horizontal movements are evaluated. In contrast to expectations, results do not show a significant impact of the robot head on motor interference. Thus, there is evidence, that appearance might not be an activating factor for motor interference in HRI. © 2024 IEEE.

关键词： Robotic arms

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Demonstrating the Merits of Integrating Multipath Signals into 5G LoS-Based Positioning Systems for Navigation in Challenging Environments 13

Demonstrating the Merits of Integrating Multipath Signals in...

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13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023

作者： Bader, Qamar Saleh, Sharief Elhabiby, Mohamed Noureldin, Aboelmagd Queen's University Department of Electrical and Computer Engineering KingstonON Canada Research Lab Department of Electrical and Computer Engineering KingstonON Canada Micro Engineering Tech. Inc

ISBN: (纸本)9798350320114

Constrained environments, such as indoor and urban settings, present a significant challenge for accurate moving object positioning due to the diminished line-of-sight (LoS) communication with the wireless anchor used for positioning. The 5th generation new radio (5G NR) millimeter wave (mmWave) spectrum promises high multipath resolvability in the time and angle domains, enabling the utilization of multipath signals for such problems rather than mitigating their effects. This paper investigates the benefits of integrating multipath signals into 5G LoS-based positioning systems with onboard motion sensors (OBMS). We provide a comprehensive analysis of the positioning system's performance in various conditions of erroneous 5G measurements and outage scenarios, which offers insights into the system's behavior in challenging environments. To validate our approach, we conducted a road test in downtown Toronto, utilizing actual OBMS measurements gathered from sensors installed in the test vehicle. The results indicate that utilization of multipath signals for wireless positioning operating in multipath-rich environments (e.g. urban and indoor) can bridge 5G LoS signal outages, thus enhancing the reliability and accuracy of the positioning solution. The redundant measurements obtained from the multipath signals can enhance the system's robustness, particularly when low-cost 5G receivers with a limited angle or range measurements are present. This holds true even when only considering the utilization of single-bounce reflections (SBRs). © 2023 IEEE.

关键词： 5G NR Autonomous Vehicles (AVs) Kalman Filter (KF) mmWave Non-line-of-site (NLoS) Positioning

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Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

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computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

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