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Utpal Barman, Parismita Sarma, Mirzanur Rahman, Vaskar Deka, Swati Lahkar, Vaishali Sharma and Manob Jyoti Saikia
Invading pests and diseases always degrade the quality and quantity of plants. Early and accurate identification of plant diseases is critical for plant health and growth. This work proposes a smartphone-based solution using a Vision Transformer (ViT) mo...
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Alaa Saeed, A. A. Abdel-Aziz, Amr Mossad, Mahmoud A. Abdelhamid, Alfadhl Y. Alkhaled and Muhammad Mayhoub
Plant diseases affect the availability and safety of plants for human and animal consumption and threaten food safety, thus reducing food availability and access, as well as reducing crop yield and quality. There is a need for novel disease detection met...
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Amrutha K, Prabu P and Ramesh Chandra Poonia
Sign language is a natural, structured, and complete form of communication to exchange information. Non-verbal communicators, also referred to as hearing impaired and hard of hearing (HI&HH), consider sign language an elemental mode of communication ...
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Youngok Kang, Nahye Cho, Jiyoung Yoon, Soyeon Park and Jiyeon Kim
Recently, as computer vision and image processing technologies have rapidly advanced in the artificial intelligence (AI) field, deep learning technologies have been applied in the field of urban and regional study through transfer learning. In the touris...
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Nicola Convertini, Vincenzo Dentamaro, Donato Impedovo, Giuseppe Pirlo and Lucia Sarcinella
This benchmarking study aims to examine and discuss the current state-of-the-art techniques for in-video violence detection, and also provide benchmarking results as a reference for the future accuracy baseline of violence detection systems. In this pape...
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Kaizhou Li, Jianhui Lin, Jinrong Liu and Yandong Zhao
Diseases from Ginkgo biloba have brought great losses to medicine and the economy. Therefore, if the degree of disease can be automatically identified in Ginkgo biloba leaves, people will take appropriate measures to avoid losses in advance. Deep learnin...
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Nikita Andriyanov
The article is devoted to the study of convolutional neural network inference in the task of image processing under the influence of visual attacks. Attacks of four different types were considered: simple, involving the addition of white Gaussian noise, ...
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Bashar Igried, Shadi AlZu?bi, Darah Aqel, Ala Mughaid, Iyad Ghaith and Laith Abualigah
Plant diseases represent one of the critical issues which lead to a major decrease in the quantity and quality of crops. Therefore, the early detection of plant diseases can avoid any losses or damage to these crops. This paper presents an image processi...
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Xin Yang, Shichen Gao, Qian Sun, Xiaohe Gu, Tianen Chen, Jingping Zhou and Yuchun Pan
Lodging depresses the grain yield and quality of maize crop. Previous machine learning methods are used to classify crop lodging extents through visual interpretation and sensitive features extraction manually, which are cost-intensive, subjective and in...
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Biprodip Pal, Debashis Gupta, Md. Rashed-Al-Mahfuz, Salem A. Alyami and Mohammad Ali Moni
The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in severa...
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Maryam Nisa, Jamal Hussain Shah, Shansa Kanwal, Mudassar Raza, Muhammad Attique Khan, Robertas Dama?evicius and Tomas Bla?auskas
As the number of internet users increases so does the number of malicious attacks using malware. The detection of malicious code is becoming critical, and the existing approaches need to be improved. Here, we propose a feature fusion method to combine th...
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Raz Lapid, Zvika Haramaty and Moshe Sipper
Deep neural networks (DNNs) are sensitive to adversarial data in a variety of scenarios, including the black-box scenario, where the attacker is only allowed to query the trained model and receive an output. Existing black-box methods for creating advers...
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Md. Reduanul Haque and Ferdous Sohel
Eggplant is a popular vegetable crop. Eggplant yields can be affected by various diseases. Automatic detection and recognition of diseases is an important step toward improving crop yields. In this paper, we used a two-stream deep fusion architecture, em...
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Safaa Alraddadi, Fahad Alqurashi, Georgios Tsaramirsis, Amany Al Luhaybi and Seyed M. Buhari
Variant approaches used to release scents in most recent olfactory displays rely on time for decision making. The applicability of such an approach is questionable in scenarios like video games or virtual reality applications, where the specific content ...
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Tianhao Gao, Meng Zhang, Yifan Zhu, Youjian Zhang, Xiangsheng Pang, Jing Ying and Wenming Liu
Classifying sports videos is complex due to their dynamic nature. Traditional methods, like optical flow and the Histogram of Oriented Gradient (HOG), are limited by their need for expertise and lack of universality. Deep learning, particularly Convoluti...
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Hassan El-khatib, Ana-Maria ?tefan and Dan Popescu
The incidence of melanoma cases continues to rise, underscoring the critical need for early detection and treatment. Recent studies highlight the significance of deep learning in melanoma detection, leading to improved accuracy. The field of computer-ass...
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Qaisar Abbas, Talal Saad Albalawi, Ganeshkumar Perumal and M. Emre Celebi
In recent years, advances in deep learning (DL) techniques for video analysis have developed to solve the problem of real-time processing. Automated face recognition in the runtime environment has become necessary in video surveillance systems for urban ...
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Md Ishtyaq Mahmud, Muntasir Mamun and Ahmed Abdelgawad
Creating machines that behave and work in a way similar to humans is the objective of artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving, computer activities with artificial intelligence include other activiti...
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Wenxin Yang, Xiaoli Zhi and Weiqin Tong
Current edge devices for neural networks such as FPGA, CPLD, and ASIC can support low bit-width computing to improve the execution latency and energy efficiency, but traditional linear quantization can only maintain the inference accuracy of neural netwo...
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Yiwei Zhong, Baojin Huang and Chaowei Tang
Cassava is a typical staple food in the tropics, and cassava leaf disease can cause massive yield reductions in cassava, resulting in substantial economic losses and a lack of staple foods. However, the existing convolutional neural network (CNN) for cas...
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