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Lightweight Convolutional Neural Network Model for Cassava Leaf Diseases Classification

SN computer science 2024年第3期5卷 284页

作者： Tewari, Anand Shanker Computer Science and Engineering Department National Institute of Technology Patna Patna 800005 India

The world population has increased many folds in the last few years and crossed the figure of 7 billion. Africa has the highest population growth rate. Food and water are the first and foremost necessities for the survival of living beings. Cassava is among the staple foods of Africa and other countries. Its roots and leaves fulfill the daily caloric demands of millions of people. In the last few years, the production of cassava has decreased due to the spread of disease in cassava leaves. The manual identification of these diseases needs large number of people with sufficient level of skills in this field, so an automated technique is required that can help farmers by detecting cassava leaf diseases within a small time frame. The paper proposed a lightweight convolutional neural network (CNN) model to find four types of cassava leaf diseases i.e., cassava mosaic disease, cassava green mottle, cassava bacterial blight, and cassava brown streak leaf disease. It uses depthwise separable convolution operation to reduce the number of parameters that makes it suitable for mobile devices. The proposed model has used both channel attention and spatial attention to highlight disease part of the leaf and suppress background scenes. The modified attention mechanism of the proposed work helps in further improving the accuracy of the model. The experimental results exhibit that the proposed model outperforms other state-of-the-art models like VGG16, ResNet 50, EfficientNet, MobileNet V1, and MobileNetV2. The proposed model uses 1.1 million fewer parameters than MobileNet V2 that makes it suitable for low computing power devices. It uses the natural scene images for the classification that enhances its applicability in real-world scenarios. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Attention Convolutional neural network Deep learning Leaf disease classification

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Improved bounding box regression loss for weapon detection systems using deep learning

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International Journal of Information Technology (Singapore) 2024年 1-17页

作者： Sumi, Lucy Dey, Shouvik Department of Computer Science and Engineering National Institute of Technology Nagaland Chumukedima 797103 India

Rising number of crime rate using firearms (such as open firing, robbery, suicides, mass shootings, homicides, threatening at gun point, etc.), has underscored the growing importance of timely detection of weapons. Bounding box regression, an essential element in object detection, plays a crucial role in accurately identifying and localizing these firearms. This paper introduces a Manhattan-Complete IoU (MCIoU) loss for bounding boxes, which demonstrates significantly faster convergence during training compared to other IoU losses. By incorporating MCIoU into one of the advanced object detection framework, (You Only Look Once) YOLOv7, the proposed work demonstrates consistent improvement on their performance across popular weapon detection benchmarks datasets such as Granda, Synthetic, Cam157, Internet Movie Firearms Database (IMFDB), Gun Movie and Monash. The most encouraging outcomes were obtained on a Gun movie dataset with a precision and recall value of 98.2% and 96.3% respectively, which is an appreciable improvement compared to the baseline YOLOv7 model. Experiments show that the best precision achieved was at 98.2% and mAP@.50:.95 of 42.5% over other existing IoUs. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： CCTV surveillance Deep learning Intersection over union (IoU) Loss function Weapon detection system You Only Look Once v7 (YOLOv7)

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BIO-VR: Design and Implementation of Virtual Reality-Based Simulated Biology Laboratory Using Google Cardboard with an Emphasis on Virtual Education 4th

BIO-VR: Design and Implementation of Virtual Reality-Based S...

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4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Ahmed, Towfik Chowdhury, Afzal Un Nayeem Mozumder, Ziaul Hasan Department of Computer Science and Engineering Leading University Sylhet Bangladesh Department of Computer Science and Engineering Metropolitan University Sylhet Bangladesh

ISBN: (纸本)9789811974014

Virtual reality (VR) has become one of the most emerging technologies of this decade. Researchers can now exchange and study data like never before with the help of virtual reality-based tools. It’s a key pipeline study in the field of human–computer interaction. In this proposed method, we created a virtual reality-based immersive simulated biology laboratory experience that allows students to learn different laboratory equipment. The immersive method for learning outcomes in the biology laboratory aimed to address the global problem where the underprivileged students lack the access of even the basic laboratory equipment. The proposed method was tested and surveyed among high school students coming from an underprivileged geography specifically in Bangladesh. The substantial finding of the study reflects how these individuals can be benefitted from this virtual reality-based biology laboratory. The results came out positive and along with that the ending calls for future works on immersive VR laboratories on other streams for high school students. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Virtual reality

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Road Extraction From Remote Sensing Images Using U-Net

Road Extraction From Remote Sensing Images Using U-Net

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering.science, AMATHE 2024

作者： Sai Kumar, D. Lokesh Jyothi, V. Esther Vardhani, Chennu Meghana, Bayarla Priya, Bobbili Hema Sharika, Alladi Computer Science And Engineering Pvp Siddhartha Engineering College Vijayawada India Vr Siddhartha Engineering College Department Of Computer Applications Vijayawada India

ISBN: (纸本)9798350371567

The enhancement of the transportation database through updated road networks is a critical aspect with a wide range of applications. Traditional methods rely on onsite measurements or human interpretation of remote sensing images for mapping purposes. However, these methods have significant drawbacks, including high expenses, lengthy processing times, and a substantial workforce requirement. To overcome this challenge, we propose a method leveraging the U-Net architecture for road extraction from multispectral satellite images. The satellite imagery used is sampled from the DigitalGlobe Images dataset, covering images captured over Thailand, Indonesia, and India, with a ground resolution of 50 cm/pixel. The U-Net model is trained on both original and augmented data, employing convolutional layers, max-pooling, and up-sampling to extract and reconstruct road masks. Performance evaluation is conducted using custom validation sets, measuring metrics such as IOU and loss. Results demonstrate the effectiveness of the proposed system, achieving high accuracy and validation scores. The extracted road networks contribute to enhancing transportation databases, offering valuable insights for urban planning and management. © 2024 IEEE.

关键词： Roads and streets

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A Comparative Analysis of Supervised Machine Learning Models for Smart Intrusion Detection in IoT Network 3

A Comparative Analysis of Supervised Machine Learning Models...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Mittal, Saksham Mishra, Amit Kumar Tripathi, Vikas Singh, Prabhdeep Pandey, Priyank Department of Computer Science & Engineering Dehradun India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350302288

IoT will become an integral part of everyone's life in making the world more intelligent and smarter. In an IoT ecosystem, objects collect and share information in order to communicate with one another. The objective of this paper is to design a safe and secure IoT network, which is more effective and smart in intrusion detection by identifying potential threats and different attacks and initiating an appropriate action. To build such a smart system, complex machine learning models are required which are trained on intrusion detection datasets. In this paper, at first two different benchmark datasets: CICIDS2017 and Bot-IoT are described briefly, which can be used for a smart intrusion detection system. After this, there is a discussion about the use of four different mathematical models using supervised ML algorithms on both the datasets respectively to classify different types of attacks, followed by a comparative analysis of the results obtained from the models in the results section. © 2023 IEEE.

关键词： Intrusion detection

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Leveraging Social Media Analytics for Student Project Allocation Using Deep Learning 3

Leveraging Social Media Analytics for Student Project Alloca...

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3rd International Conference on Machine Learning and Data engineering. ICMLDE 2024

作者： Thoppil, Indu Joseph Saha, Jayita Gopu, Arunkumar Department of Computer Science and Engineering DAYANANDA SAGAR UNIVERSITY Bengaluru562112 India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The integration of data-driven initiatives and technology has received substantial interest in the educational domain. Capstone projects significantly influence the career trajectories of students. Rather than merely allocating projects, the utilization of social media data to determine the domain interests of students in student project allocation has gained significant attention. This study explores the potential of social media analytics to revolutionize student project allocation using LinkedIn profiles. The proposed CNN model with Word2Vec embeddings demonstrated a superior performance of 96% in classifying the students based on LinkedIn profiles. The effectiveness of CNN utilizing the power of Word2Vec word embedding is employed to to capture syntactic and semantic relationships within text data. This high efficiency underscores the potential of Convolutional Neural Networks(CNN) as a text classification tool for facilitating smooth collaboration in project-based learning environments for quality education. © 2025 The Author(s).

关键词： Convolutional neural networks

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Efficient Image Captioning Based on Vision Transformer Models

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computers, Materials & Continua 2022年第10期73卷 1483-1500页

作者： Samar Elbedwehy T.Medhat Taher Hamza Mohammed F.Alrahmawy Department of Data Science Faculty of Artificial IntelligenceKafrelsheikh UniversityEgypt Department of Electrical Engineering Faculty of EngineeringKafrelsheikh UniversityEgypt Department of Computer Science Faculty of Computer and Information ScienceMansouraEgypt

Image captioning is an emerging field in machine *** refers to the ability to automatically generate a syntactically and semantically meaningful sentence that describes the content of an *** captioning requires a complex machine learning process as it involves two sub models:a vision sub-model for extracting object features and a language sub-model that use the extracted features to generate meaningful ***-based vision transformers models have a great impact in vision field *** this paper,we studied the effect of using the vision transformers on the image captioning process by evaluating the use of four different vision transformer models for the vision sub-models of the image captioning The first vision transformers used is DINO(self-distillation with no labels).The second is PVT(Pyramid Vision Transformer)which is a vision transformer that is not using convolutional *** third is XCIT(cross-Covariance Image Transformer)which changes the operation in self-attention by focusing on feature dimension instead of token *** last one is SWIN(Shifted windows),it is a vision transformer which,unlike the other transformers,uses shifted-window in splitting the *** a deeper evaluation,the four mentioned vision transformers have been tested with their different versions and different configuration,we evaluate the use of DINO model with five different backbones,PVT with two versions:PVT_v1and PVT_v2,one model of XCIT,SWIN *** results show the high effectiveness of using SWIN-transformer within the proposed image captioning model with regard to the other models.

关键词： Image captioning sequence-to-sequence self-distillation transformer convolutional layer

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Extracting Facial Features to Detect Deepfake Videos Using Machine Learning

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International Journal of Advanced computer science and Applications 2025年第4期16卷 834-842页

作者： Aslam, Ayesha Mir, Jamaluddin Zaman, Gohar Rahman, Atta Salam, Asiya Abdus Ali, Farhan Alhiyafi, Jamal Bakry, Aghiad Gul, Mustafa Jamal Gollapalli, Mohammed Mahmud, Maqsood Department of Computer Science Abbottabad University of Science and Technology Havelian Pakistan Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia College of Electronics and Information Engineering Shenzhen University Shenzhen China Department of Computer Science Kettering University FlintMI United States Department of Business Administration University of York York YO10 5DD Heslington United Kingdom Department of Information Technology & Engineering Sydney Met SydneyNSW2000 Australia School of Computing Ulster University Northern Ireland Belfast United Kingdom

Generative adversarial networks (GANs) have gained popularity for their ability to synthesize images from random inputs in deep learning models. One of the notable applications of this technology is the creation of realistic videos known as deepfakes, which have been misused on social media platforms. The difficulty lies in distinguishing these fake videos from real ones with the naked eye, leading to significant concerns. This study proposes a supervised machine learning approach to effectively differentiate between real and counterfeit videos by detecting visual artifacts. To achieve this, two facial features are extracted: eye blinking and nose position, utilizing landmark detection techniques. Both features were trained on supervised machine learning classifiers and evaluated using the publicly available UADFV and Celeb-DF deepfake datasets. The experiments successfully demonstrate that the proposed method achieves a promising and superior performance, with an area under the curve (AUC) of 97% for deepfake detection in contrast to state-of-the-art methods investigating the same datasets. © (2025), (science and Information Organization). All rights reserved.

关键词： Support vector machines

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Learning key steps to attack deep reinforcement learning agents

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Machine Learning 2023年第5期112卷 1499-1522页

作者： Yu, Chien-Min Chen, Ming-Hsin Lin, Hsuan-Tien Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Deep reinforcement learning agents are vulnerable to adversarial attacks. In particular, recent studies have shown that attacking a few key steps can effectively decrease the agent’s cumulative reward. However, all existing attacking methods define those key steps with human-designed heuristics, and it is not clear how more effective key steps can be identified. This paper introduces a novel reinforcement learning framework that learns key steps through interacting with the agent. The proposed framework does not require any human heuristics nor knowledge, and can be flexibly coupled with any white-box or black-box adversarial attack scenarios. Experiments on benchmark Atari games across different scenarios demonstrate that the proposed framework is superior to existing methods for identifying effective key steps. The results highlight the weakness of RL agents even under budgeted attacks. © 2023, The Author(s), under exclusive licence to Springer science+Business Media LLC, part of Springer Nature.

关键词： Reinforcement learning

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Parkinson’s Disease Detection Using Machine Learning 5th

Parkinson’s Disease Detection Using Machine Learning

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5th International Conference on Trends in Computational and Cognitive engineering. TCCE 2023

作者： Samad, Abdul Dhanda, Namrata Verma, Rajat Department of Computer Science and Engineering Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

ISBN: (纸本)9789819719228

Parkinson’s disease (PD) is one of the most fatal and progressive nervous system illnesses affecting movement. It is a common neurological illness that causes disabilities, shortens people’s lives, and has no treatment. Almost 90% of those affected by this condition suffer speech problems. Large datasets are accessible in many data repositories for use in solving real-world problems. A significant amount of study has been done in this area in recent years with positive outcomes. In this modern era, machine learning (ML) is the answer to every problem. ML techniques are also utilized in the detection of PD, which has afflicted many individuals. A Parkinson’s voice dataset is used in this paper. The authors have utilized several machine learning methods like support vector machine (SVM), random forest (RF), logistic regression (LR), K-nearest neighbor (KNN), and XGBoost (XGB) for PD detection. The output of each algorithm is compared based on their accuracies. The KNN outperforms all the other methods by obtaining the highest accuracy of 95% in magnitude. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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