Results for 'deep learning'

998 found
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  1. Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework.Tosin ige, Christopher Kiekintveld & Aritran Piplai - forthcoming - Proceedings of the IEEE:8.
    The ever-evolving ways attacker continues to improve their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable (...)
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  2. Deep learning and synthetic media.Raphaël Millière - 2022 - Synthese 200 (3):1-27.
    Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the (...)
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  3.  45
    Using Deep Learning to Classify Eight Tea Leaf Diseases.Mai R. Ibaid & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):89-96.
    Abstract: People all over the world have been drinking tea for thousands of centuries, and for good reason. Many types of teas can help you stay healthy by boosting your immune system, reducing inflammation, and even preventing cancer and heart disease. There is sufficient material to show that regularly consuming tea can improve your health over the long term. A deep learning model that categorizes tea disorders has been completed. When focusing on the tea, we must also focus (...)
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  4.  68
    Using Deep Learning to Detect the Quality of Lemons.Mohammed B. Karaja & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):97-104.
    Abstract: Lemons are an important fruit that have a wide range of uses and benefits, from culinary to health to household and beauty applications. Deep learning techniques have shown promising results in image classification tasks, including fruit quality detection. In this paper, we propose a convolutional neural network (CNN)-based approach for detecting the quality of lemons by analysing visual features such as colour and texture. The study aims to develop and train a deep learning model to (...)
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  5. Deep Learning as Method-Learning: Pragmatic Understanding, Epistemic Strategies and Design-Rules.Phillip H. Kieval & Oscar Westerblad - manuscript
    We claim that scientists working with deep learning (DL) models exhibit a form of pragmatic understanding that is not reducible to or dependent on explanation. This pragmatic understanding comprises a set of learned methodological principles that underlie DL model design-choices and secure their reliability. We illustrate this action-oriented pragmatic understanding with a case study of AlphaFold2, highlighting the interplay between background knowledge of a problem and methodological choices involving techniques for constraining how a model learns from data. Building (...)
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  6.  60
    Using Deep Learning to Classify Corn Diseases.Mohanad H. Al-Qadi & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems (Ijaisr) 8 (4):81-88.
    Abstract: A corn crop typically refers to a large-scale cultivation of corn (also known as maize) for commercial purposes such as food production, animal feed, and industrial uses. Corn is one of the most widely grown crops in the world, and it is a major staple food for many cultures. Corn crops are grown in various regions of the world with different climates, soil types, and farming practices. In the United States, for example, the Midwest is known as the "Corn (...)
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  7.  53
    Classification of Rice Using Deep Learning.Mohammed H. S. Abueleiwa & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):26-36.
    Abstract: Rice is one of the most important staple crops in the world and serves as a staple food for more than half of the global population. It is a critical source of nutrition, providing carbohydrates, vitamins, and minerals to millions of people, particularly in Asia and Africa. This paper presents a study on using deep learning for the classification of different types of rice. The study focuses on five specific types of rice: Arborio, Basmati, Ipsala, Jasmine, and (...)
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  8. Comparative Analysis of Deep Learning and Naïve Bayes for Language Processing Task.Olalere Abiodun - forthcoming - International Journal of Research and Innovation in Applied Sciences.
    Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least (...)
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  9.  71
    Fish Classification Using Deep Learning.M. N. Ayyad & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):51-58.
    Abstract: Fish are important for both nutritional and economic reasons. They are a good source of protein, vitamins, and minerals and play a significant role in human diets, especially in coastal and island communities. In addition, fishing and fish farming are major industries that provide employment and income for millions of people worldwide. Moreover, fish play a critical role in marine ecosystems, serving as prey for larger predators and helping to maintain the balance of aquatic food chains. Overall, fish play (...)
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  10. Lemon Classification Using Deep Learning.Jawad Yousif AlZamily & Samy Salim Abu Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):16-20.
    Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images (...)
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  11. Diagnosis of Pneumonia Using Deep Learning.Alaa M. A. Barhoom & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (2):48-68.
    Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and react like humans. Some of the activities computers with artificial intelligence are designed for include, Speech, recognition, Learning, Planning and Problem solving. Deep learning is a collection of algorithms used in machine learning, It is part of a broad family of methods used for machine learning that are based on learning representations of data. (...)
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  12. A DEEP LEARNING APPROACH FOR LSTM BASED COVID-19 FORECASTING SYSTEM.K. Jothimani - 2022 - Journal of Science Technology and Research (JSTAR) 3 (1):28-38.
    : COVID-19 has proliferated over the earth, exposing mankind at risk. The assets of the world's most powerful economies are at stake due to the disease's high infectivity and contagiousness. The capacity of machine learning algorithms can estimate the amount of future COVID-19 cases, which is now considered a possible threat to civilization. Five conventional measuring models, notably LR, LASSO, SVM, ES, and LSTM, were utilised in this work to examine COVID-19's undermining variables. Each model contains three sorts of (...)
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  13. Cantaloupe Classifications using Deep Learning.Basel El-Habil & Samy S. Abu-Naser - 2021 - International Journal of Academic Engineering Research (IJAER) 5 (12):7-17.
    Abstract cantaloupe and honeydew melons are part of the muskmelon family, which originated in the Middle East. When picking either cantaloupe or honeydew melons to eat, you should choose a firm fruit that is heavy for its size, with no obvious signs of bruising. They can be stored at room temperature until you cut them, after which they should be kept in the refrigerator in an airtight container for up to five days. You should always wash and scrub the rind (...)
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  14.  74
    RAINFALL DETECTION USING DEEP LEARNING TECHNIQUE.M. Arul Selvan & S. Miruna Joe Amali - 2024 - Journal of Science Technology and Research 5 (1):37-42.
    Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in dvance regarding: on-going construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. In (...)
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  15. Classifications of Pineapple using Deep Learning.Amjad H. Alfarra, Lamis F. Samhan, Yasmin E. Aslem, Marah M. Almasawabe & Samy S. Abu-Naser - 2021 - International Journal of Academic Information Systems Research (IJAISR) 5 (12):37-41.
    A pineapple is a tropical plant with eatable leafy foods most monetarily critical plant in the family Bromeliaceous. The pineapple is native to South America, where it has been developed for a long time. The acquaintance of the pineapple with Europe in the seventeenth century made it a critical social symbol of extravagance. Since the 1820s, pineapple has been industrially filled in nurseries and numerous tropical manors. Further, it is the third most significant tropical natural product in world creation. In (...)
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  16. Gender Prediction from Retinal Fundus Using Deep Learning.Ashraf M. Taha, Qasem M. M. Zarandah, Bassem S. Abu-Nasser, Zakaria K. D. AlKayyali & Samy S. Abu-Naser - 2022 - International Journal of Academic Information Systems Research (IJAISR) 6 (5):57-63.
    Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and (...)
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  17. Classification of Real and Fake Human Faces Using Deep Learning.Fatima Maher Salman & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (3):1-14.
    Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised (...)
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  18. Potato Classification Using Deep Learning.Abeer A. Elsharif, Ibtesam M. Dheir, Alaa Soliman Abu Mettleq & Samy S. Abu-Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):1-8.
    Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and benefit human health. They are an important staple food in many countries around the world. There are an estimated 200 varieties of (...)
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  19. Retina Diseases Diagnosis Using Deep Learning.Abeer Abed ElKareem Fawzi Elsharif & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (2):11-37.
    There are many eye diseases but the most two common retinal diseases are Age-Related Macular Degeneration (AMD), which the sharp, central vision and a leading cause of vision loss among people age 50 and older, there are two types of AMD are wet AMD and DRUSEN. Diabetic Macular Edema (DME), which is a complication of diabetes caused by fluid accumulation in the macula that can affect the fovea. If it is left untreated it may cause vision loss. Therefore, early detection (...)
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  20.  60
    Classification of Dates Using Deep Learning.Raed Z. Sababa & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):18-25.
    Abstract: Dates are the fruit of date palm trees, and it is one of the fruits famous for its high nutritional value. It is a summer fruit spread in the Arab world. In the past, the Arabs relied on it in their daily lives. Dates take an oval shape and vary in size from 20 to 60 mm in length and 8 to 30 mm in diameter. The ripe fruit consists of a hard core surrounded by a papery cover called (...)
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  21.  86
    Deep Learning Based Video Captioning through Encoder-Decoder Based Long Short-Term Memory (LSTM).Grimsby Chelsea - forthcoming - International Journal of Advance Computer Science and Application.
    This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over (...)
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  22. Sarcasm Detection in Headline News using Machine and Deep Learning Algorithms.Alaa Barhoom, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):66-73.
    Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...)
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  23. Sarcasm Detection in Headline News using Machine and Deep Learning Algorithms.Alaa Barhoom, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):66-73.
    Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...)
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  24. Type of Tomato Classification Using Deep Learning.Mahmoud A. Alajrami & Samy S. Abu-Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):21-25.
    Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which (...)
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  25. Classification of Sign-Language Using MobileNet - Deep Learning.Tanseem N. Abu-Jamie & Samy S. Abu-Naser - 2022 - International Journal of Academic Information Systems Research (IJAISR) 6 (7):29-40.
    Abstract: Sign language recognition is one of the most rapidly expanding fields of study today. Many new technologies have been developed in recent years in the fields of artificial intelligence the sign language-based communication is valuable to not only deaf and dumb community, but also beneficial for individuals suffering from Autism, downs Syndrome, Apraxia of Speech for correspondence. The biggest problem faced by people with hearing disabilities is the people's lack of understanding of their requirements. In this paper we try (...)
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  26. Deep Learning Based Video Captioning through Encoder-Decoder Based Long Short-Term Memory (LSTM).Grimsby Chelsea - forthcoming - International Journal of Advanced Computer Science and Applications:1-6.
    This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over (...)
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  27.  75
    Vegetable Classification Using Deep Learning.Mostafa El-Ghoul & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):105-112.
    Abstract: Vegetables are an essential component of a healthy diet and play a critical role in promoting overall health and well- being. Vegetables are rich in important vitamins and minerals, including vitamin C, folate, potassium, and iron. They also provide fiber, which helps maintain digestive health and prevent chronic diseases. We are proposing a deep learning model for the classification of vegetables. A dataset was collected from Kaggle depository for Vegetable with 15000 images for 15 different classes. The (...)
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  28. Prediction of Heart Disease Using a Collection of Machine and Deep Learning Algorithms.Ali M. A. Barhoom, Abdelbaset Almasri, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):1-13.
    Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...)
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  29. Classification of A few Fruits Using Deep Learning.Mohammed Alkahlout, Samy S. Abu-Naser, Azmi H. Alsaqqa & Tanseem N. Abu-Jamie - 2022 - International Journal of Academic Engineering Research (IJAER) 5 (12):56-63.
    Abstract: Fruits are a rich source of energy, minerals and vitamins. They also contain fiber. There are many fruits types such as: Apple and pears, Citrus, Stone fruit, Tropical and exotic, Berries, Melons, Tomatoes and avocado. Classification of fruits can be used in many applications, whether industrial or in agriculture or services, for example, it can help the cashier in the hyper mall to determine the price and type of fruit and also may help some people to determining whether a (...)
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  30. Diagnosis of Blood Cells Using Deep Learning.Ahmed J. Khalil & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (2):69-84.
    In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Deep Learning is a new field of research. One of the branches of Artificial Intelligence Science deals with the creation of theories and (...)
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  31. Implementation and Comparison of Deep Learning with Naïve Bayes for Language Processing (4th edition).Abiodun Olalere - 2024 - Internation Journal of Research and Innovation in Appliad Science:1-6.
    Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least (...)
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  32. Predictive Analysis of Lottery Outcomes Using Deep Learning and Time Series Analysis.Asil Mustafa Alghoul & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):1-6.
    Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this (...)
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  33. AISC 17 Talk: The Explanatory Problems of Deep Learning in Artificial Intelligence and Computational Cognitive Science: Two Possible Research Agendas.Antonio Lieto - 2018 - In Proceedings of AISC 2017.
    Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical problem (...)
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  34. Captioning Deep Learning Based Encoder-Decoder through Long Short-Term Memory (LSTM).Grimsby Chelsea - forthcoming - International Journal of Scientific Innovation.
    This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over (...)
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  35. Attack Prevention in IoT through Hybrid Optimization Mechanism and Deep Learning Framework.Regonda Nagaraju, Jupeth Pentang, Shokhjakhon Abdufattokhov, Ricardo Fernando CosioBorda, N. Mageswari & G. Uganya - 2022 - Measurement: Sensors 24:100431.
    The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge (...)
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  36. AI-Completeness: Using Deep Learning to Eliminate the Human Factor.Kristina Šekrst - 2020 - In Sandro Skansi (ed.), Guide to Deep Learning Basics. Springer. pp. 117-130.
    Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...)
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  37. Classification of Chicken Diseases Using Deep Learning.Mohammed Al Qatrawi & Samy S. Abu-Naser - 2024 - Information Journal of Academic Information Systems Research (Ijaisr) 8 (4):9-17.
    Abstract: In recent years, the outbreak of various poultry diseases has posed a significant threat to the global poultry industry. Therefore, the accurate and timely detection of chicken diseases is critical to reduce economic losses and prevent the spread of diseases. In this study, we propose a method for classifying chicken diseases using a convolutional neural network (CNN). The proposed method involves preprocessing the chicken images, building and training a CNN model, and evaluating the performance of the model. The dataset (...)
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  38. Prediction of Heart Disease Using a Collection of Machine and Deep Learning Algorithms.Ali M. A. Barhoom, Abdelbaset Almasri, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):1-13.
    Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...)
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  39. Classification of Sign-Language Using Deep Learning by ResNet.Tanseem N. Abu-Jamie & Samy S. Abu-Naser - 2022 - International Journal of Academic Information Systems Research (IJAISR) 6 (8):25-34.
    American Sign Language, or ASL as its acronym is commonly known, is a fascinating language, and many people outside of the Deaf community have begun to recognize its value and purpose. It is a visual language consisting of coordinated hand gestures, body movements, and facial expressions. Sign language is not a universal language; it varies by country and is heavily influenced by the native language and culture. The American Sign Language alphabet and the British Sign Language alphabet are completely contrary. (...)
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  40. Classification of Sign-Language Using Deep Learning - A Comparison between Inception and Xception models.Tanseem N. Abu-Jamie & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (8):9-19.
    there is a communication gap between hearing-impaired people and those with normal hearing, sign language is the main means of communication in the hearing-impaired population. Continuous sign language recognition, which can close the communication gap, is a difficult task since the ordered annotations are weakly supervised and there is no frame-level label. To solve this issue, we compare the accuracy of each model using two deep learning models, Inception and Xception . To that end, the purpose of this (...)
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  41. Classification of Anomalies in Gastrointestinal Tract Using Deep Learning.Ibtesam M. Dheir & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (3):15-28.
    Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning techniques have been (...)
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  42.  74
    Tomato Leaf Diseases Classification using Deep Learning.Mohammed F. El-Habibi & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):73-80.
    Abstract: Tomatoes are among the most popular vegetables in the world due to their frequent use in many dishes, which fall into many varieties in common and traditional foods, and due to their rich ingredients such as vitamins and minerals, so they are frequently used on a daily basis, When we focus our attention on this vegetable, we must also focus and take into consideration the diseases that affect this vegetable, a deep learning model that classifies tomato diseases (...)
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  43. Menschengestützte Künstliche Intelligenz: Über die soziotechnischen Voraussetzungen von "deep learning".Rainer Mühlhoff - 2019 - Zeitschrift Für Medienwissenschaft (ZfM) 21 (2):56–64.
    Die aktuellen Erfolge von Künstlicher Intelligenz beruhen nicht nur auf technologischen Fortschritten, sondern auch auf einem grundlegenden soziotechnischen Strukturwandel. Denn maschinelle Lernverfahren wie Deep Learning benötigen eine große Menge Trainingsdaten, die nur über menschliche Mitarbeit gewonnen werden können. In einer Konvergenz von Methoden der Human-Computer-Interaction und der KI ist in den letzten zehn Jahren eine Fülle von Mensch-Maschine-Interfaces und medialen Infrastrukturen entstanden, durch die menschliche kognitive Ressourcen in hybride Mensch-Maschine-Apparate eingespannt werden. Diese Apparate vollbringen im Ganzen jene Leistung, (...)
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  44. A Theory Explains Deep Learning.Kenneth Kijun Lee & Chase Kihwan Lee - manuscript
    This is our journal for developing Deduction Theory and studying Deep Learning and Artificial intelligence. Deduction Theory is a Theory of Deducing World’s Relativity by Information Coupling and Asymmetry. We focus on information processing, see intelligence as an information structure that relatively close object-oriented, probability-oriented, unsupervised learning, relativity information processing and massive automated information processing. We see deep learning and machine learning as an attempt to make all types of information processing relatively close to (...)
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  45.  87
    Classification of Apple Diseases Using Deep Learning.Ola I. A. Lafi & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):1-9.
    Abstract: In this study, we explore the challenge of identifying and preventing diseases in apple trees, which is a popular activity but can be difficult due to the susceptibility of these trees to various diseases. To address this challenge, we propose the use of Convolutional Neural Networks, which have proven effective in automatically detecting plant diseases. To validate our approach, we use images of apple leaves, including Apple Rot Leaves, Leaf Blotch, Healthy Leaves, and Scab Leaves collected from Kaggle which (...)
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  46. Handwritten Signature Verification using Deep Learning[REVIEW]Eman Alajrami, Belal A. M. Ashqar, Bassem S. Abu-Nasser, Ahmed J. Khalil, Musleh M. Musleh, Alaa M. Barhoom & Samy S. Abu-Naser - manuscript
    Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...)
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  47. Classification of Age and Gender Using ResNet - Deep Learning.Aysha I. Mansour & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (8):20-29.
    Age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Even Nevertheless, contrast to the large performance improvements recently reported for the closely related task of audio. In this research, we show that performance on these tasks can be significantly improved by learning representations using deep convolutional neural networks (CNN). where we get in the ResNet the training accuracy was 98% ,validation accuracy 95%, testing accuracy (...)
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  48. Predicting Whether Student will continue to Attend College or not using Deep Learning.Samy S. Abu-Naser, Qasem M. M. Zarandah, Moshera M. Elgohary, Zakaria K. D. AlKayyali, Bassem S. Abu-Nasser & Ashraf M. Taha - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (6):33-45.
    According to the literature review, there is much room for improvement of college student retention. The aim of this research is to evaluate the possibility of using deep and machine learning algorithms to predict whether students continue to attend college or will stop attending college. In this research a feature assessment is done on the dataset available from Kaggle depository. The performance of 20 learning supervised machine learning algorithms and one deep learning algorithm is (...)
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  49. Detection of Brain Tumor Using Deep Learning.Hamza Rafiq Almadhoun & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (3):29-47.
    Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and reacts like humans, some of the computer activities with artificial intelligence are designed to include speech, recognition, learning, planning and problem solving. Deep learning is a collection of algorithms used in machine learning, it is part of a broad family of methods used for machine learning that are based on learning representations of data. (...)
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  50.  64
    The Fast Food Image Classification using Deep Learning.Jehad El-Tantawi & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):37-43.
    Abstract: Fast food refers to quick, convenient, and ready-to-eat meals that are usually sold at chain restaurants or take-out establishments. Fast food is often criticized for its unhealthy ingredients, such as high levels of salt, sugar, and unhealthy fats, and its contribution to the growing obesity epidemic. Despite this, fast food remains popular due to its affordability, convenience, and widespread availability. Many fast food chains have attempted to respond to these criticisms by offering healthier options, such as salads and grilled (...)
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