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Detecting Common Diseases of Potato Leaf Applying Deep Learning Techniques 2nd

Detecting Common Diseases of Potato Leaf Applying Deep Learn...

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2nd International Conference on Innovations in Data Analytics, ICIDA 2023

作者： Goon, Surajit Chakraborty, Rahul Dalui, Indrani Obaid, Ahmed J. Department of Computer Science Eminent College of Management & Technology West Bengal Barasat India Faculty of Computer Science and Mathematics Kufa University Kufa Iraq

ISBN: (纸本)9789819749270

Potatoes are a widely recognized vegetable that has been cultivated in India for several decades. However, diseases like early blight and late blight have negatively impacted potato production, increasing production costs. We intend to create an automated and effective disease diagnosis mechanism utilizing potato leaf images to overcome this issue and digitize the system. Our primary goal is to employ a convolutional neural network (CNN) algorithm for diagnosing potato diseases. This research paper proposes a machine learning and image processing-based automated system for detecting and classifying potato leaf diseases. Image processing techniques provide an excellent approach for disease detection and analysis. In this analysis, we divide the pictures into categories: healthy and unhealthy potato leaves. We have collected over 2152 pictures from various sources, including Kaggle, and utilized pre-trained models for accurate recognition and classification of healthy and diseased leaves. The program achieves an impressive accuracy of 99.13%. By making use of advanced technology and machine learning algorithms, we can significantly improve potato production and combat the negative impact of diseases. In the end, this method increases productivity and yields in the potato farming sector by providing a scalable and effective means of detecting diseases in potato crops. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Enhancing Early Childhood Literacy: A Technological Aid for Letter Learning

Enhancing Early Childhood Literacy: A Technological Aid for ...

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2024 International Conference on information and Communication technology for Development for Africa, ICT4DA 2024

作者： De Silva, D.I. Athukorala, K.S.N. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Uva Wellassa University Department of Computer Science and Informatics Badulla Sri Lanka

ISBN: (纸本)9798331527792

Children generally learn at a slower pace than adults, necessitating additional practice, especially during early education stages. Mastery of the alphabet is crucial as it forms the foundation of any language, making it imperative for children to learn it first. Each child possesses a distinct learning style, and the duration required to grasp new concepts can vary considerably among individuals. To address this, the Sinhala Alphabet Learning Tool has been developed, offering preschoolers an interactive platform that enhances engagement and facilitates their learning experience. This research employs the K-Nearest Neighbors algorithm combined with image processing techniques to accurately predict letters input by preschoolers. A substantial dataset comprising 56,000 entries was meticulously collected directly from preschool participants. The data were systematically organized into folders based on letter classification, subsequently used to train the K-Nearest Neighbors model, known for its simplicity and effectiveness in pattern recognition tasks. The tool enables users to input Sinhala letters and immediately receive feedback on the correctness of their entries. The system evaluates the shape of the letters and provides feedback with an impressive accuracy rate of 87%, ensuring that preschoolers receive reliable insights into their learning progress. While previous studies have primarily focused on recognizing Sinhala characters using Optical Character Recognition integrated with machine learning algorithms, the proposed approach diverges by capturing letter inputs through a graphical user interface developed using Tkinter. This design significantly enhances the interactivity and user-friendliness of the tool, making it more suitable and appealing for preschool-aged children. © 2024 IEEE.

关键词： Graphical user interfaces

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Smart Intelligence-Driven Deep Learning Framework for Reliable Detection and Accurate Classification of Skin Cancer Diseases 3

Smart Intelligence-Driven Deep Learning Framework for Reliab...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Rathish Babu, T.K.S. Chandramohan, S. Be, Hajira Niroshini Infantia, H. Srm Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai89 India Sri Chandrasekarendra Saraswathi Viswa Maha Vidyalaya Department of Computer Science Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Computer Applications Maduranthakam Taluk India St. Joseph's Institute of Technology Department of Information Technology OMR Chennai600119 India

ISBN: (纸本)9798331509675

The interest in skin cancer detection is growing remarkably because of the prevalence of this health condition worldwide. It improves the chances of survival and enhances general treatment outcomes when diagnosis has been done early. Most the traditional methods for skin lesion detection involve visual inspection performed by dermatologists;hence, it is time-consuming and, to some extent, subjective. Therefore, with the rise of deep learning-Artificial Intelligence, auto-detection of skin cancer has gained a foothold in improving diagnostic accuracy, hence treatment outcomes. The presented paper proposes a GAN-based approach for skin cancer detection by deep learning analysis of dermoscopic images, classifying them as malignant and benign. The proposed approach uses the generator and discriminator parts of GAN to obtain an effective discrimination between cancerous and non-cancerous lesions with high precision. The proposed method incorporates the native abilities of GANs for boosting diagnostic precision. GANs consist of two major components: a generator and a discriminator. These two adversaries collaborate with each other in a way to encourage the classification performance of the framework. The generator synthesizes the realistic data representations by augmenting the dataset with various variations of dermoscopic images. This helps not only to compensate for the insufficiency of annotated data but also to make the model generalize more robustly to diverse skin lesion patterns. At the same time, the discriminator learns to distinguish malignant from benign lesions with high accuracy by using the adversarial nature in GAN training. These results clearly indicate the effectiveness of the proposed GAN-based approach over state-of-the-art traditional deep learning models. GAN makes not only the detection system more robust but also reduces its dependency on large annotated datasets. © 2025 IEEE.

关键词： Adversarial machine learning

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A Novel Inception Deep Learning model for Mobile AI Stroke Prediction system: AI Research and Industry Perspectives for Connected Health 6

A Novel Inception Deep Learning model for Mobile AI Stroke P...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Elbagoury, Bassant M. Maskeliuans, Rytis Zaghow, Marwa Kamel, Nabil Faculty of Computer and Information Sciences Ain Shams University Computer Science and Artificial Intelligence Programs Egypt Faculty of Computer Science and Engineering King Salman International University KSIU Egypt Kaunas University of Technology Faculty of Informatics Department of Multimedia Engineering Lithuania Dell Technologies EMEA Egypt

ISBN: (纸本)9798350373875

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. The main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed. © 2024 IEEE.

关键词： Learning systems

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Meal Recommendation system for the Elderly (MRS)

Meal Recommendation System for the Elderly (MRS)

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2023 International Technical Conference on Circuits/systems, computers, and Communications, ITC-CSCC 2023

作者： Charernmool, Piyanuch Tawornying, Chonlasit Prapakornwanichakun, Theerasak Vanijkachorn, Pavarit Visutsak, Porawat Pensiri, Fuangfar Chaopraya University Faculty of Science and Technology Nakhon Sawan Thailand King Mongkut's University of Technology Faculty of Applied Science Department of Computer and Information Science North Bangkok Bangkok Thailand School of Computer Science and Technology Beijing Institute Technology Beijing100081 China Kasetsart University Faculty of Science at Sriracha Chonburi Thailand

ISBN: (纸本)9798350326413

In developing countries, the well-being of the elderly is not given enough attention, especially in terms of nutrition. This is a vital point since the meal which the elderly eat typically reflects their health. This paper aims to research and develop a web-based system with the capacity to design appropriate and healthy meal plans for the elderly by nutrition specialist's recommendation. Eating healthy meal plans for a person helps to have good health. It was found that the elderly with diabetic mellitus and kidney failure had the better health together with good mental health. Moreover, the system can accurately calculate the optimal number of calories which the elderly should have each day. A supporting website was developed using HTML and *** for expert evaluation © 2023 IEEE.

关键词： Expert systems

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Fruit Ripeness Detection using RipeNet 3

Fruit Ripeness Detection using RipeNet

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Bhavana, Kapa Sumathi, R. Vanaja, Kandregula Manjula, S. Srinivasulu, T. Divya, Siripuri Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Department of Information Technology Andhra Pradesh Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331509675

Fruits are absolutely delicious for the most part, but more importantly, they are good for healthy life. Fruits are nature's candy and offer all sorts of health benefits besides the great taste. They provide necessary nutrients to the body so that it will not be susceptible to disease or other ailments. Fruits contain enough fiber to keep our digestive system in top health form. Fruits also helps to regulate the water level in body as well as your blood sugar levels, which means that will usually feel hungry less often if we consumes a lot of fruits. The easier it is for people to recognize that a fruit is ripe and ready for selling at the market allows the farmers, in some cases, to sell their crop early before it would normally spoil. If we look closely, we see that many times people don't buy something because it appears rotten or way past its peak ripeness, which demonstrates how products spoil right on time. This study proposed a user-convenient system for fruit ripeness detection using the convolutional neural network (CNN) based on RipeNet structure. © 2025 IEEE.

关键词： Fruits

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IoT Network Forensics based on Transport Layer

IoT Network Forensics based on Transport Layer

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2023 IEEE Conference on computer Applications, ICCA 2023

作者： Naing, Soe Kalayar Oo, Su Thet Oo, Hnin Wit Yi Lin, Phyo Yatanar Nwe, Khin Mya Thwin, Mie Mie Su University of Computer Studies Faculty of Computer Science Yangon Yangon Myanmar University of Computer Studies Yangon Yangon Myanmar University of Computer Studies Faculty of Computer System and Technology Yangon Yangon Myanmar University of Computer Studies Information Technology Support and Maintenance Yangon Yangon Myanmar University of Computer Studies Cyber Security Lab Yangon Yangon Myanmar

ISBN: (纸本)9781665435994

The IoT is concerned in every part of the human beings such as medical care, education, smart cities, home automation, and many business fields. While the IoT gives a myriad of benefits, security remains the biggest issues for using of many IoT applications in various environments. The emergence of new technologies in various fields also pose challenges in the field of forensic researches. It is said that Device Level forensics, Network Level forensics, and Cloud Level forensics are the three types of IoT forensics levels. Among them, Network Level forensics shows all kinds of communication networks that connected to IoT devices over the Internet. In this study, detecting and analyzing TCP-SYN flooding attacks and UDP flooding attacks based on Transport Layer (one type of DoS attacks), network forensics investigation over the traffics are presented. Capturing TCP-SYN flooding attacks and UDP flooding attacks and analyzing them with IO graphs by using Wireshark Network Analysis Tool is also described in this paper. © 2023 IEEE.

关键词： Floods

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Arabic Sentiment Analysis on Mobile Applications Using Levenshtein Distance Algorithm and Naive Bayes 2

Arabic Sentiment Analysis on Mobile Applications Using Leven...

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2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Al-Hagree, Salah Al-Gaphari, Ghaleb Sana'a University Faculty of Computer and Information Technology Computer Science Department Yemen Ibb University Faculty of Sciences Computer Science Department Yemen

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

Due to the specificity of the characteristics and features of the Arabic language and its complexity, the field of sentiment analysis in Arabic texts still represents a major challenge. Few studies have been conducted for Arabic sentiment analysis (ASA) compared to English or other Latin languages. In addition, most of the current studies on ASA have been conducted on data sets collected from Twitter and very few studies on user comments in Mobile applications (apps) reviews in Google Play Store. Therefore, this paper presents a new approach to sentiment analysis in Arabic text based on the mobile app comments dataset of Google Play Store. The proposed approach uses algorithms such as the Levenshtein distance (LD) algorithm for preprocessing the data. Thereafter, it applies various classification models to identify the mobile applications(apps) reviews in the Google Play Store of Arabic text. The results of the experiment show that the proposed approach is effective for sentiment analysis of Arabic text. The experiments were carried out by comparing the accuracy using the Naive Bayes (NB) algorithm, and we obtained an accuracy of 95.80% and compared it with the use of the LD Algorithm, where we obtained a better accuracy of 96.40% to identify the reviews of the sentence when k=9. © 2022 IEEE.

关键词： Sentiment analysis

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Virtual Laboratory Learning Experience in Engineering: An Extended technology Acceptance Model (TAM) 3

Virtual Laboratory Learning Experience in Engineering: An Ex...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2023

作者： Ahmed Alnagrat, Ahmed Jamah Ahmed, Khamiss M. S. Alkhallas, Muhannad Ismail Almakhzoom, Osama Abulaqasim Ibrahim Syed Idrus, Syed Zulkarnain Che Ismail, Rizalafande Higher Institute of Science and Technology Wadi Al-Shati Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya Sebha University Faculty of Information Technology Department of Computer Science Sebha Libya Faculty of Engineering Technology in Zuwarah Department of Software Engineering Zuwarah Libya Department of Communication Perlis Kangar01000 Malaysia Arau02600 Malaysia

ISBN: (纸本)9798350319897

This research presents an enhanced version of the technology Acceptance Model (TAM), which integrates usability and learning objectives to evaluate the effect of adopting virtual laboratories on student performance. Data from 97 participants, gathered via a survey, was used to examine the correlation between perceived usefulness (PU), perceived ease of use (PEOU), and intention to use (IU) concerning their experiences and outcomes with a simulation-based virtual laboratory. A custom questionnaire was designed to assess TAM components, namely PU, PEOU, and IU, specifically in the context of virtual laboratory learning environments. Findings indicate that the integration of specific laboratory learning objectives fosters the creation of a more credible and authentic simulation tool. This, in turn, optimizes the learning process and enhances students' learning outcomes. The refined TAM model provides valuable insights into the determinants of user acceptance of virtual laboratories and offers practical guidance for educators and instructional designers to construct more effective and efficient virtual laboratory environments in educational contexts. © 2023 IEEE.

关键词： Virtual reality

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Comparison of Machine Learning Methods for Air Quality Prediction Based on Particulate Matter (PM2.5)

Comparison of Machine Learning Methods for Air Quality Predi...

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2025 International Conference on Advancement in Data science, E-learning and information system, ICADEIS 2025

作者： Mar'i, Farhanna Marianingsih, Susi Abdillah, Rifqi Elfaiz, Ersha Aisyah Setyo Prayoga, Riza Akhsani Fahmi, Muhamad Universitas Negeri Surabaya Faculty of Engineering Department of Informatics Engineering Surabaya Indonesia Jayapura University of Science and Technology Department of Information System Jayapura Indonesia Universitas Negeri Surabaya Faculty of Engineering Department of Information Technology Education Surabaya Indonesia Universitas Airlangga Faculty of Science and Technology Department of Biomedical Engineering Surabaya Indonesia

ISBN: (纸本)9798331513320

Particulate PM2.5 has a major effect on human health compared to other types of pollutants such as NO2, 03, CO, and SO2. PM2.5 is the worst type of major pollutant for human health that can cause various types of serious diseases and even death. PM2.5 is defined as an ambient air particle, which is one of the particle elements found in the free air of the earth's surface in the troposphere layer, which is very small in size, reaching 2.5 pollutants. The method of predicting air quality index values can be done using machine learning techniques with time series data analysis. This study will compare machine learning methods with two approaches, namely statistical regression with RF and ARIMA and deep learning method, namely LSTM. Based on the research results, the best machine learning method approach is statistical regression, namely the RF method with accuracy evaluation using RMSE, MAE, and MSE, which are respectively with a minimum value of 0.162, 0.108, and 0.026, followed by the LSTM and ARIMA methods. © 2025 IEEE.

关键词： Polycyclic aromatic hydrocarbons

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