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Journal articles on the topic 'Deep Learning techniques'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 September 2023

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1

Firdaus, Naina, and Madhuvan Dixit. "Deep Learning Techniques, Applications and Challenges: An Assessment." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1710–14. http://dx.doi.org/10.31142/ijtsrd14437.

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2

M, Leo Francis, Darshan K. S, Ankith M. C, and Divakara V. "CombiningNLP and Deep Learning Techniques to Generate Captions." International Journal of Research Publication and Reviews 4, no. 5 (June 2023): 4682–91. http://dx.doi.org/10.55248/gengpi.4.523.42704.

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3

Agarwal, Sohit, and Mukesh Kumar Gupta. "Context Aware Image Sentiment Classification using Deep Learning Techniques." Indian Journal Of Science And Technology 15, no. 47 (December 20, 2022): 2619–27. http://dx.doi.org/10.17485/ijst/v15i47.1907.

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Sravani, L., N. Rama Venkat Sai, K. Noomika, M. Upendra Kumar, and K. V. Adarsh. "Image Enhancement of Underwater Images using Deep Learning Techniques." International Journal of Research Publication and Reviews 4, no. 4 (April 3, 2023): 81–86. http://dx.doi.org/10.55248/gengpi.2023.4.4.34620.

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Ibrahim, Dr Abdul-Wahab Sami, and Dr Baidaa Abdul khaliq Atya. "Detection of Diseases in Rice Leaf Using Deep Learning and Machine Learning Techniques." Webology 19, no. 1 (January 20, 2022): 1493–503. http://dx.doi.org/10.14704/web/v19i1/web19100.

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Plant diseases have a negative impact on the agricultural sector. The diseases lower the productivity of the production yield and give huge losses to the farmers. For the betterment of agriculture, it is very essential to detect the diseases in the plants to protect the agricultural crop yield while it is also important to reduce the use of pesticides to improve the quality of the agricultural yield. Image processing and data mining algorithms together help analyze and detection of diseases. Using these techniques diseases detection can be done in rice leaves. In this research, the image processing technique is used to extract the feature from the leaf images. Further for the classification of diseases various machine learning algorithm like the random forest, J48 and support vector machine is used and the result is compared among different machine learning algorithm. After model evaluation, classification accuracy is verified using the n-fold cross-validation technique.

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S., Gayathri, Santhiya S., Nowneesh T., Sanjana Shuruthy K., and Sakthi S. "Deep fake detection using deep learning techniques." Applied and Computational Engineering 2, no. 1 (March 22, 2023): 1010–19. http://dx.doi.org/10.54254/2755-2721/2/20220655.

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Deep fake is the artificial manipulation and creation of data, primarily through photo-graphs or videos into the likeness of another person. This technology has a variety of ap-plications. Despite its uses, it can also influence society in a controversial way like de-faming a person, Political distress, etc. Many models had been proposed by different re-searchers which give an average accuracy of 90%. To improve the detection efficiency, this proposed paper uses 3 different deep learning techniques: Inception ResNetV2, Effi-cientNet, and VGG16. These proposed models are trained by the combination of Facfo-rensic++ and DeepFake Detection Challenge Dataset. This proposed system gives the highest accuracy of 97%.

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T., Senthil Kumar. "Systematic Study on Deep Learning Techniques for Prediction of Movies." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 31–38. http://dx.doi.org/10.5373/jardcs/v12sp4/20201463.

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Nandini, L. Surya, L. Haritha Priya, N. Sruthi, K. S. N. Murthy, M. Ashish Kumar, and N. Lakshmi Devi. "Survey on Aspect Based Sentimental Analysis using Deep Learning Techniques." International Journal of Research Publication and Reviews 4, no. 3 (March 2023): 634–46. http://dx.doi.org/10.55248/gengpi.2023.31886.

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Harsha, Sanda Sri. "Prediction of Silica Impurity Using Deep Learning Techniques for Mining Environment." Revista Gestão Inovação e Tecnologias 11, no. 3 (June 30, 2021): 506–17. http://dx.doi.org/10.47059/revistageintec.v11i3.1953.

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Kibria, Md Golam, and Mehmet Sevkli. "Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques." International Journal of Machine Learning and Computing 11, no. 4 (August 2021): 286–90. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1049.

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The increased credit card defaulters have forced the companies to think carefully before the approval of credit applications. Credit card companies usually use their judgment to determine whether a credit card should be issued to the customer satisfying certain criteria. Some machine learning algorithms have also been used to support the decision. The main objective of this paper is to build a deep learning model based on the UCI (University of California, Irvine) data sets, which can support the credit card approval decision. Secondly, the performance of the built model is compared with the other two traditional machine learning algorithms: logistic regression (LR) and support vector machine (SVM). Our results show that the overall performance of our deep learning model is slightly better than that of the other two models.

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Son, Doan Trung, Nguyen Thi Khanh Tram, and Pham Minh Hieu. "Deep Learning Techniques to Detect Botnet." Journal of Science and Technology on Information security 1, no. 15 (June 8, 2022): 85–91. http://dx.doi.org/10.54654/isj.v1i15.846.

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Abstract— Over the past time, the world has witnessed an unprecedented explosion of Deep Learning. Besides thedevelopment of Information Technology, security and safety threats are also increasing, one of which is the Botnet network. Botnet network is increasingly complex and difficult to detect, and traditional techniques are no longer effective, so one of the urgent problems today is to find an effective solution to detecting botnets [2]. Based on the characteristics of deep learning such as scalability, performance, execution time, interpretability, etc., therefore, in this paper, the author proposes to use deep learning techniques to detect Botnet networks. Tóm tắt— Thời gian qua, thế giới chứng kiến sự bùng nổ một cách mạnh mẽ chưa từng có của Deep Learning. Bên cạnh sự phát triển của Công nghệ thông tin, các mối đe doạ về an ninh, an toàn cũng ngày càng tăng lên, một trong những mối đe doạ đó chính là mạng Botnet. Mạng Botnet ngày càng phức tạp và khó phát hiện, các kỹ thuật truyền thống không còn phát huy được nhiều tác dụng, vì vậy một trong những vấn đề cấp thiết hiện nay đó là tìm ra được một giải pháp thật hiệu quả trong phát hiện mạng Botnet [2]. Dựa trên những đặc điểm của học sâu như: khả năng mở rộng hiệu suất, thời gian thực hiện, khả năng diễn giải… do đó, trong bài báo này, tác giả đề xuất sử dụng kỹ thuật học sâu để phát hiện mạng Botnet.

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12

Tai, Le Quy, and Giang Thi Thu Huyen. "Deep Learning Techniques for Credit Scoring." Journal of Economics, Business and Management 7, no. 3 (2019): 93–96. http://dx.doi.org/10.18178/joebm.2019.7.3.588.

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Sahu, Mridu, Yogita Upadhyay, Namrata Khoriya, Abhilash Biswas, Manas Chandrawanshi, and Omprakash Patel. "Deep Learning Techniques on Brain Images." Journal of Physics: Conference Series 2273, no. 1 (May 1, 2022): 012026. http://dx.doi.org/10.1088/1742-6596/2273/1/012026.

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Abstract Our brain is the most composite organ of the human body with an aggregation of 100 million nerves which communicate in a nexus of synapses. All the activities from thinking, memorizing, storing information, the functioning of other organs of the body are all managed by the brain. Any disease which affects the brain affects the whole of the body. Severe brain diseases paralyze the body. Some of the common categories of brain diseases are seizures, trauma, tumor, and infections. Alzheimer’s, Epilepsy, Brain Cancer, and brain disorders. Research to use Image Processing Techniques in the field of brain diseases still has a long way to go. This paper is one such small step in the process of understanding Deep Learning in brain imaging. It is a detailed study on brain diseases and how algorithms can help in the current treatment. CNN is discussed in detail with its architecture and the reason of its popularity is discussed. This particular paper also comprises a case study of one such disease i.e brain tumor and the effect of various parameters in improving the accuracy of Convolutional neural networks on this particular data-set. The case study involves augmenting the data and applying CNN on it. The effect of CNN is then studied on the basis of three parameters which are Optimizers, Activation Function, and Loss Function. A comparative analysis is then drawn out among all the possible combinations and the best combination of these parameters are found. The models were evaluated in terms of accuracy and time required to train the algorithm. Using the comparison table important findings and conclusions were drawn out.

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Arunkumar O N and Divya D. "Deep Learning Techniques for Demand Forecasting." Information Resources Management Journal 35, no. 2 (April 2022): 1–24. http://dx.doi.org/10.4018/irmj.291692.

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The aim of this study is to categorize research on the applications of deep learning techniques in demand forecasting and suggest further research directions. This study is based upon 56 papers published between 2017 and April 2021 in international peer-reviewed elite journals. Primary objective of this paper is to identify the major problem domains in demand forecasting; hence authors conduct a review of literatures which utilizes deep learning techniques for demand forecasting and proposed directions for future research. After identifying the objective a subject scrutiny of the important papers are done based on the publication quality. These identifications make additions to demand forecasting research in the resulting manner. For accomplishing this task, first, the authors classified the literature into nine major problem domains based on different issues discussed in the literature. Second, the literatures are classified based on different deep leaning techniques used for solving the problem of demand forecasting. Third, seven research propositions provided for future research.

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Chen, Chi-Hua, Hsu-Yang Kung, and Feng-Jang Hwang. "Deep Learning Techniques for Agronomy Applications." Agronomy 9, no. 3 (March 20, 2019): 142. http://dx.doi.org/10.3390/agronomy9030142.

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This editorial introduces the Special Issue, entitled “Deep Learning (DL) Techniques for Agronomy Applications”, of Agronomy. Topics covered in this issue include three main parts: (I) DL-based image recognition techniques for agronomy applications, (II) DL-based time series data analysis techniques for agronomy applications, and (III) behavior and strategy analysis for agronomy applications. Three papers on DL-based image recognition techniques for agronomy applications are as follows: (1) “Automatic segmentation and counting of aphid nymphs on leaves using convolutional neural networks,” by Chen et al.; (2) “Estimating body condition score in dairy cows from depth images using convolutional neural networks, transfer learning, and model ensembling techniques,” by Alvarez et al.; and (3) “Development of a mushroom growth measurement system applying deep learning for image recognition,” by Lu et al. One paper on DL-based time series data analysis techniques for agronomy applications is as follows: “LSTM neural network based forecasting model for wheat production in Pakistan,” by Haider et al. One paper on behavior and strategy analysis for agronomy applications is as follows: “Research into the E-learning model of agriculture technology companies: analysis by deep learning,” by Lin et al.

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Santos Silva, Jose Vitor, Leonardo Matos Matos, Flavio Santos, Helisson Oliveira Magalhaes Cerqueira, Hendrik Macedo, Bruno Otavio Piedade Prado, Gilton Jose Ferreira da Silva, and Kalil Araujo Bispo. "Combining deep learning model compression techniques." IEEE Latin America Transactions 20, no. 3 (March 2022): 458–64. http://dx.doi.org/10.1109/tla.2022.9667144.

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17

Aravinda, Akurathi, Challagulla Yoshitha, Kakarla Meghana, Kandula Sreeja, and B. Tejaswi. "Image Restoration using Deep Learning Techniques." International Journal of Engineering and Advanced Technology 11, no. 5 (June 30, 2022): 13–16. http://dx.doi.org/10.35940/ijeat.e3509.0611522.

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In the modern era, due to the emergence of various technologies, most of the human work is now being performed by the computer system. The computer’s capacity to make everything possible is increasing as by the time. Photos are used to capture or freeze the moments in one’s life. We can embrace those moments at any time by looking at the pictures. It is natural that, as time passes by, these photos gets damaged due to environmental conditions that leads to loss of our important moments. Hence, preserving the photos is as important as taking them. The process of taking corrupt or noisy image and estimating the clean, original image is image restoration. Many forms of noise such as motion blur, camera misfocus etc., increases the complexity to restore the image. Image corruption comes in varying degrees of severity, the complexity of restoring photos in real-world applications will likewise vary greatly. Also, manual restoration is time consuming leading to lots of work to be piled up. To increase the capability of restoring old images from various defects, we must address several degradations intermingled in one old photo, such as structural defects like scratches and dust spots, and unstructured defects like sounds and blurriness. Furthermore, we may use a different face refinement network to restore small details of faces in ancient pictures, resulting in higher-quality photos. The aim of the work is to create a image restoration system that will be used to restore the images irrespective of the type of noise. In this paper, we present a model that would take image as an input and remove all the noises present in it to give a clean and restored image.

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18

C R, Reshma. "Speech Recognition using Deep Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (June 30, 2020): 2199–201. http://dx.doi.org/10.22214/ijraset.2020.6358.

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19

Riaz, Rabia, Sanam Shahla Rizvi, Ayesha Mushtaq, Sana Shokat, and Se Jin Kwon. "Burglar Detection using Deep Learning Techniques." Journal of Engineering and Applied Sciences 14, no. 8 (December 31, 2019): 2672–86. http://dx.doi.org/10.36478/jeasci.2019.2672.2686.

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20

Vogt, Michael. "An overview of deep learning techniques." at - Automatisierungstechnik 66, no. 9 (September 25, 2018): 690–703. http://dx.doi.org/10.1515/auto-2018-0076.

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Abstract Deep learning is the paradigm that profoundly changed the artificial intelligence landscape within only a few years. Although accompanied by a variety of algorithmic achievements, this technology is disruptive mainly from the application perspective: It considerably pushes the border of tasks that can be automated, changes the way products are developed, and is available to virtually everyone. Subject of deep learning are artificial neural networks with a large number of layers. Compared to earlier approaches with ideally a single layer, this allows using massive computational resources to train black-box models directly on raw data with a minimum of engineering work. Most successful applications are found in visual image understanding, but also in audio and text modeling.

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Prashant Krishnan, V., S. Rajarajeswari, Venkat Krishnamohan, Vivek Chandra Sheel, and R. Deepak. "Music Generation Using Deep Learning Techniques." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 3983–87. http://dx.doi.org/10.1166/jctn.2020.9003.

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This paper primarily aims to compare two deep learning techniques in the task of learning musical styles and generating novel musical content. Long Short Term Memory (LSTM), a supervised learning algorithm is used, which is a variation of the Recurrent Neural Network (RNN), frequently used for sequential data. Another technique explored is Generative Adversarial Networks (GAN), an unsupervised approach which is used to learn a distribution of a particular style, and novelly combine components to create sequences. The representation of data from the MIDI files as chord and note embedding are essential to the performance of the models. This type of embedding in the network helps it to discover structural patterns in the samples. Through the study, it is seen how a supervised learning technique performs better than the unsupervised one. A study helped in obtaining a Mean Opinion Score (MOS), which was used as an indicator of the comparative quality and performance of the respective techniques.

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Dimauro, Giovanni, Giorgio Ciprandi, Francesca Deperte, Francesco Girardi, Enrico Ladisa, Sergio Latrofa, and Matteo Gelardi. "Nasal cytology with deep learning techniques." International Journal of Medical Informatics 122 (February 2019): 13–19. http://dx.doi.org/10.1016/j.ijmedinf.2018.11.010.

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Fu, Erjia, Junyuan Xiang, and Chuanhao Xiong. "Deep Learning Techniques for Sentiment Analysis." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 1–7. http://dx.doi.org/10.54097/hset.v16i.2065.

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Sentiment analysis covers a wide range of computational research, including research on the opinions, feelings, emotions, evaluations of people, and attitudes toward products, services, organizations, individuals, issues, events, topics, and their attributes. It plays an increasingly important role in the era of big data. In fact, it has spread from computer science to management and social sciences such as marketing, finance, political science, communications, medical science and even history, generating common interest throughout society due to its commercial importance. TEA is a basic task with the typical used of NLP methods which is full of interest, particularly for fine-grained classification of textual emotional content. It is the process of mastering, inductive analysis and reasoning about emotional content. Simply put, it is the process of analysing, processing, summarising and reasoning about emotive and subjective texts. The Internet generates a large numberof user reviews to gain valuable information about people, events and products. These reviews express a wide range of emotions and emotional tendencies, including joy, anger, sadness, delight, criticism and praise. Potential users can therefore view these subjective reviews to understand how public opinion views an event or product.

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Jyothi, Ms A. Aruna. "Stress Detection using Deep Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 4895–903. http://dx.doi.org/10.22214/ijraset.2023.54543.

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Abstract: Stress is a typical component of daily life that has an impact on people in different circumstances. However, sustained or acute stress can negatively impact our health and interfere with our daily activities. Early recognition of mental stress is essential for good management and the avoidance of future health problems brought on by chronic stress. Understanding the connection between facial expressions and the accompanying emotional experiences of individuals is a topic of great interest. According to research, facial expressions and indications might offer important clues for the study and classification of stress. Notably, changes in the mouth and eyebrows are important signs of stress on the human face. This technology records live video and applies conventional conversion to capture stress levels. In order to analyse the user's stress levels, this system records live video and uses conventional conversion and image processing techniques. The technology provides more precise and effective results in stress prediction by utilising machine learning algorithms that concentrate on brow and lip motions.

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L, Sukanya, Aniketh J, Abhiman Sathwik E, Sridhar Reddy M, and Hemanth Kumar N. "Racism detection using deep learning techniques." E3S Web of Conferences 391 (2023): 01052. http://dx.doi.org/10.1051/e3sconf/202339101052.

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With the pervasive role of social media in the socio-political landscape, various forms of racism have arisen on these platforms. Racism can manifest in various forms on social media, both concealed and overt. It can be hidden through the use of memes or exposed through racist comments made using fake profiles to spread social unrest, violence, and hatred. Twitter and other social media sites have become new settings in which racism and related stress appear to be thriving. Racism also spread based on characteristics including dialect, faith, and tradition. It has been determined that racial animosity on social media poses a serious threat to political, socioeconomic, and cultural equilibrium and has even put international peace at risk. Therefore, it is crucial to monitor social media as the primary source of racist opinions dissemination and to detect and block racist remarks in a timely manner. In this study, we aim to detect tweets containing racist text by performing sentiment analysis using both ML and DL algorithms. We will also build a webpage using Flask framework and SQLite for users to interact with the model.

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V, Anjanadevi, Hemalatha R, Venkateshwar R, Naren J, and Vithya G. "A framework for the Diagnosis of Diabetic Retinopathy Using Deep Learning Techniques." International Journal of Psychosocial Rehabilitation 23, no. 1 (February 20, 2019): 405–11. http://dx.doi.org/10.37200/ijpr/v23i1/pr190252.

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27

Hu, Shengkun. "Deep Learning in Healthcare." Highlights in Science, Engineering and Technology 57 (July 11, 2023): 279–85. http://dx.doi.org/10.54097/hset.v57i.10014.

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This article aims to discuss and demonstrate deep learning techniques used in healthcare. After introducing the feasibility of deep learning in the medical field, the article discussed the opportunities and challenges of deep learning in healthcare from different perspectives. Then, the article showed the current implementations and applications of deep learning in the medical healthcare system. Finally, the article summarizes deep learning techniques and applications in healthcare.

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R.D., Dhaniya, and Dr Umamaheswari K.M. "Brain Tumor Analysis Empowered with Machine Learning and Deep Learning: A Comprehensive Review with its Recent Computational Techniques." Webology 19, no. 1 (January 20, 2022): 764–79. http://dx.doi.org/10.14704/web/v19i1/web19054.

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In driving the medical image research machine-learning and deep-learning algorithm are growing expeditiously. The premature conjecture of disease needs substantial attempts to diagnose the disease. The machine learning algorithm confesses the software application to study from the data and predicts more accurate outcome. The deep learning algorithm drives on extensive dataset imparts on high end machine and clarifies the problem end to end. The primary focus on the survey is to high-spots the machine and deep-learning approaches in medical image analysis that endorses the decision-making practices. The paper provides a plan for the researchers to perceive the extant schemes sustained out for medical imaging with its recognition and hindrances of the machine and deep learning algorithm.

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Durai, Senthil Kumar Swami, and Mary Divya Shamili. "Smart farming using Machine Learning and Deep Learning techniques." Decision Analytics Journal 3 (June 2022): 100041. http://dx.doi.org/10.1016/j.dajour.2022.100041.

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R, Surendiran. "Exploring Computer Vision's Deep Learning and Machine Learning Techniques." International Journal of Computer Science and Engineering 10, no. 2 (February 25, 2023): 1–9. http://dx.doi.org/10.14445/23488387/ijcse-v10i2p101.

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31

Poomka, Pumrapee, Nittaya Kerdprasop, and Kittisak Kerdprasop. "Machine Learning Versus Deep Learning Performances on the Sentiment Analysis of Product Reviews." International Journal of Machine Learning and Computing 11, no. 2 (March 2021): 103–9. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1021.

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At this current digital era, business platforms have been drastically shifted toward online stores on internet. With the internet-based platform, customers can order goods easily using their smart phones and get delivery at their place without going to the shopping mall. However, the drawback of this business platform is that customers do not really know about the quality of the products they ordered. Therefore, such platform service often provides the review section to let previous customers leave a review about the received product. The reviews are a good source to analyze customer's satisfaction. Business owners can assess review trend as either positive or negative based on a feedback score that customers had given, but it takes too much time for human to analyze this data. In this research, we develop computational models using machine learning techniques to classify product reviews as positive or negative based on the sentiment analysis. In our experiments, we use the book review data from amazon.com to develop the models. For a machine learning based strategy, the data had been transformed with the bag of word technique before developing models using logistic regression, naïve bayes, support vector machine, and neural network algorithms. For a deep learning strategy, the word embedding is a technique that we used to transform data before applying the long short-term memory and gated recurrent unit techniques. On comparing performance of machine learning against deep learning models, we compare results from the two methods with both the preprocessed dataset and the non-preprocessed dataset. The result is that the bag of words with neural network outperforms other techniques on both non-preprocess and preprocess datasets.

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32

Yang, Yushu. "Current Trends in Deep Learning." Advances in Engineering Technology Research 5, no. 1 (May 6, 2023): 422. http://dx.doi.org/10.56028/aetr.5.1.422.2023.

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This paper provides an overview of the current artificial intelligence (AI) and machine learning (ML) techniques, including Convolutional Neural Networks(CNN), Adversarial and Generative Techniques, Natural Language Processing (NLP), and Reinforcement Learning(RL). The paper discusses the background, applications, and future trends of these techniques, highlighting their potential for solving real-world problems. The purpose of the paper is to illustrate the trends that are emerging in these areas, as well as the challenges that must be addressed in order to fully realize their potential. By identifying several key areas, this paper concludes the future research and development.

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Sai Susanth, G., L. M. Jenila Livingston, and L. G. X. Agnel Livingston. "Garbage Waste Segregation Using Deep Learning Techniques." IOP Conference Series: Materials Science and Engineering 1012 (January 8, 2021): 012040. http://dx.doi.org/10.1088/1757-899x/1012/1/012040.

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Huang, Yin-Fu, and Yi-Hao Li. "Translating Sentimental Statements Using Deep Learning Techniques." Electronics 10, no. 2 (January 10, 2021): 138. http://dx.doi.org/10.3390/electronics10020138.

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Natural Language Processing (NLP) allows machines to know nature languages and helps us do tasks, such as retrieving information, answering questions, text summarization, categorizing text, and machine translation. To our understanding, no NLP was used to translate statements from negative sentiment to positive sentiment with resembling semantics, although human communication needs. The developments of translating sentimental statements using deep learning techniques are proposed in this paper. First, for a sentiment translation model, we create negative–positive sentimental statement datasets. Then using deep learning techniques, the sentiment translation model is developed. Perplexity, bilingual evaluation understudy, and human evaluations are used in the experiments to test the model, and the results are satisfactory. Finally, if the trained datasets can be constructed as planned, we believe the techniques used in translating sentimental statements are possible, and more sophisticated models can be developed.

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Soumare, Harouna, Alia Benkahla, and Nabil Gmati. "Deep learning regularization techniques to genomics data." Array 11 (September 2021): 100068. http://dx.doi.org/10.1016/j.array.2021.100068.

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Anantha Prabha, P., G. Karthikeyan, K. Kuttralanathan, and M. Manoj Venkatesun. "Intelligent Mask Detection Using Deep Learning Techniques." Journal of Physics: Conference Series 1916, no. 1 (May 1, 2021): 012072. http://dx.doi.org/10.1088/1742-6596/1916/1/012072.

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Yerima, Suleiman Y., Mohammed K. Alzaylaee, Annette Shajan, and Vinod P. "Deep Learning Techniques for Android Botnet Detection." Electronics 10, no. 4 (February 23, 2021): 519. http://dx.doi.org/10.3390/electronics10040519.

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Android is increasingly being targeted by malware since it has become the most popular mobile operating system worldwide. Evasive malware families, such as Chamois, designed to turn Android devices into bots that form part of a larger botnet are becoming prevalent. This calls for more effective methods for detection of Android botnets. Recently, deep learning has gained attention as a machine learning based approach to enhance Android botnet detection. However, studies that extensively investigate the efficacy of various deep learning models for Android botnet detection are currently lacking. Hence, in this paper we present a comparative study of deep learning techniques for Android botnet detection using 6802 Android applications consisting of 1929 botnet applications from the ISCX botnet dataset. We evaluate the performance of several deep learning techniques including: CNN, DNN, LSTM, GRU, CNN-LSTM, and CNN-GRU models using 342 static features derived from the applications. In our experiments, the deep learning models achieved state-of-the-art results based on the ISCX botnet dataset and also outperformed the classical machine learning classifiers.

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Huang, Yin‐Fu, Li‐Ping Shih, Chia‐Hsin Tsai, and Guan‐Ting Shen. "Describing video scenarios using deep learning techniques." International Journal of Intelligent Systems 36, no. 6 (February 25, 2021): 2465–90. http://dx.doi.org/10.1002/int.22387.

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Huang, Yin-Fu, and Yi-Hao Li. "Translating Sentimental Statements Using Deep Learning Techniques." Electronics 10, no. 2 (January 10, 2021): 138. http://dx.doi.org/10.3390/electronics10020138.

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Natural Language Processing (NLP) allows machines to know nature languages and helps us do tasks, such as retrieving information, answering questions, text summarization, categorizing text, and machine translation. To our understanding, no NLP was used to translate statements from negative sentiment to positive sentiment with resembling semantics, although human communication needs. The developments of translating sentimental statements using deep learning techniques are proposed in this paper. First, for a sentiment translation model, we create negative–positive sentimental statement datasets. Then using deep learning techniques, the sentiment translation model is developed. Perplexity, bilingual evaluation understudy, and human evaluations are used in the experiments to test the model, and the results are satisfactory. Finally, if the trained datasets can be constructed as planned, we believe the techniques used in translating sentimental statements are possible, and more sophisticated models can be developed.

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Maheskumar, Mr V. "Building Crack Detection Using Deep Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2330–38. http://dx.doi.org/10.22214/ijraset.2022.44303.

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Abstract: Reliability, performance, and life cycle costs are real concerns for almost all in-service massive structures, such as buildings, bridges, nuclear facilities, hydroelectric structures, and dams. Cracks on these structures are a common phenomenon associated with various internal and external forces, including the corrosion of embedded reinforcement, chemical deterioration of concrete, and the application of adverse loading to the structure. In comparison to the traditional manual inspection-based crack detection system, computer vision and machine learning-based approaches are quickly becoming an integral part of the modern segmentation of civil infrastructures to automate crack detection and identification systems. The project is about the construction and application of a device that uses image processing to detect cracks. The system has a graphical user interface for initializing the device, viewing real time image, taking pictures of a crack, measuring its width, and evaluating if safe or unsafe.

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A. C., Anitha, R. ,. Dhanesha, Shrinivasa Naika C. L., Krishna A. N., Parinith S. Kumar, and Parikshith P. Sharma. "Arecanut Bunch Segmentation Using Deep Learning Techniques." International Journal of Circuits, Systems and Signal Processing 16 (July 26, 2022): 1064–73. http://dx.doi.org/10.46300/9106.2022.16.129.

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Agriculture and farming as a backbone of many developing countries provides food safety and security. Arecanut being a major plantation in India, take part an important role in the life of the farmers. Arecanut growth monitoring and harvesting needs skilled labors and it is very risky since the arecanut trees are very thin and tall. A vision-based system for agriculture and farming gains popularity in the recent years. Segmentation is a fundamental task in any vision-based system. A very few attempts been made for the segmentation of arecanut bunch and are based on hand-crafted features with limited performance. The aim of our research is to propose and develop an efficient and accurate technique for the segmentation of arecanut bunches by eliminating unwanted background information. This paper presents two deep-learning approaches: Mask Region-Based Convolutional Neural Network (Mask R-CNN) and U-Net for the segmentation of arecanut bunches from the tree images without any pre-processing. Experiments were done to estimate and evaluate the performances of both the methods and shows that Mask R-CNN performs better compared to U-Net and methods that apply segmentation on other commodities as there were no bench marks for the arecanut.

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A, Karthikeyan, Nagarjuna S, Hemachandran G, and Kapila Vani R. K. "Covid-19 Diagnosis Using Deep Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 1658–62. http://dx.doi.org/10.22214/ijraset.2022.46871.

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Abstract: The COVID-19 plague is a significant pandemic that has spread to in excess of 150 nations all over the planet, influencing the wellbeing and prosperity of many individuals all over the planet. Recognition of the sickness by X-beam and Xbeam assessment on broad CT-checks is probably the quickest method for diagnosing the patient. Imaging studies showed that the CT output of a contaminated individual was fundamentally divergent for all intents and purposes, size, area, and COVID-19 contrasted with a customary CT examine. In any case, CT screening of contaminated regions is an answer for this issue when doctors are exhausted during an irresistible illness. Consequently, it is important to check COVID19 quicker with the assistance of a PC. In this paper, the most well-known brain organizations, for example, DenseNet-121, ResNet50, Inception V3 Net, and Xception were utilized to analyze COVID-19 in the dissected pictures, think about them, and show the right degree of COVID-19 discovery.

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Baid, Yash, and Avinash Dhole. "Food Image Classification Using Deep Learning Techniques." International Journal of Computer Sciences and Engineering 9, no. 7 (July 31, 2021): 11–15. http://dx.doi.org/10.26438/ijcse/v9i7.1115.

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Arun Akash, S. A., R. Sri Skandha Moorthy, K. Esha, and N. Nathiya. "Human Violence Detection Using Deep Learning Techniques." Journal of Physics: Conference Series 2318, no. 1 (August 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2318/1/012003.

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Abstract The world’s average annual fatality rate from human violence is 7.9 per 10,000 people. Most of this human violence takes place in an isolated area or of sudden. The information delay here is a major impediment in stopping these acts. To thrive on this issue, the detection technique is used in this study. Detecting moving objects from CCTV is one of the most effective computer vision algorithms. CCTV cameras are now in every streets which are extremely helpful in solving cases. Some techniques of deep learning are used as computer vision to predict and detect the action, properties from video. In real-time police reach violent destinations and start checking CCTV cameras, and investigate to proceed further. This study is deliberately designed to detect violent acts from CCTV cameras. The Inception – v3 and Yolo – v5 models detect the violent act, the number of persons involved, and also the weapons used in the situation. The study consists of these deep learning models, which are used to form a video detection system. This model can be used in real-time as an application programming interface (API) or software. The study results showed the proposed model achieves an accuracy of 74%.

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Verma, Dr Neeta. "Assistive Vision Technology using Deep Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 2695–704. http://dx.doi.org/10.22214/ijraset.2021.36815.

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One of the most important functions of the human visual system is automatic captioning. Caption generation is one of the more interesting and focused areas of AI, with numerous challenges to overcome. If there is an application that automatically captions the scenes in which a person is present and converts the caption into a clear message, people will benefit from it in a variety of ways. In this, we offer a deep learning model that detects things or features in images automatically, produces descriptions for the images, and transforms the descriptions to audio for louder readout. The model uses pre-trained CNN and LSTM models to perform the task of extracting objects or features to get the captions. In our model, first task is to detect objects within the image using pre trained Mobilenet model of CNN (Convolutional Neural Networks) and therefore the other is to caption the pictures based on the detected objects by using LSTM (Long Short Term Memory) and convert caption into speech to read out louder to the person by using SpeechSynthesisUtterance interface of the Web Speech API. The interface of the model is developed using NodeJS as a backend for the web page. Caption generation entails a number of complex steps, including selecting the dataset, training the model, validating the model, creating pre-trained models to check the images, detecting the images, and finally generating captions.

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Singh, Hrithik, Shambhavi Kaushik, Shruti Talyan, and Kartikeya Dwivedi. "Skin Cancer Detection Using Deep Learning techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 4296–305. http://dx.doi.org/10.22214/ijraset.2022.43090.

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Abstract: Skin cancer detection is one of the major prob-lems across the world. Early detection of the skin cancer and its diagnosis is very important for the further treatment of it. Artificial Intelligence has progressed a lot in the field of healthcare and diagnosis and hence skin cancer can also be detected using Machine Leaning and AI. In this research, we have used convolutional neural network for image processing and recognition. The models implemented are Vgg-16, mobilenet, inceptionV3. The paper also reviewed different AI based skin cancer detection models. Here we have used transfer learning method to reuse a pre-trained model also a model from the scratch is also built using CNN blocks. A web app is also featured using HTML, Flask and CSS in which we just have to put the diagnosis image and it will predict the result. Hence, these pre-trained models and a new model from scratch are applied to procure the most optimal model to detect skin cancer using images and web app helps on getting the result at the user end. Thus, the methodology used in this paper if implemented will give improved results of early skin cancer detection using deep learning methods. Index Terms: Skin Cancer, VGG-16, deep learning, convolu-tional neural network, transfer learning.

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Elsaraiti, Meftah, and Adel Merabet. "Solar Power Forecasting Using Deep Learning Techniques." IEEE Access 10 (2022): 31692–98. http://dx.doi.org/10.1109/access.2022.3160484.

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S, Srividya M., Anala M. R, and Chetan Tayal. "Deep learning techniques for physical abuse detection." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (December 1, 2021): 971. http://dx.doi.org/10.11591/ijai.v10.i4.pp971-981.

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<span>Physical abuse has become a societal problem. Mostly children, women and old age people are vulnerable to it especially in cases of domestic violence or workplace aggression. Reporting it is in itself a challenge especially if there is a pre-existing relationship between the abuser and victim. In this paper we propose a deep learning technique for human action recognition and human pose identification to tackle physical abuse by detecting it in real time. 3D convolution neural network (CNN) architecture is built using 3D convolution feature extractors which extract both temporal and spatial data in the video. With multiple convolution layer and subsampling layer, the input video has been converted into feature vector. Human pose estimation is done using the detection of key points on the body. Using these points and tracking them from one frame to another gives spatial-temporal features to feed into neural network (NN). We present metrics to measure the accuracies of such systems where real time reporting and fault tolerance capabilities are of utmost importance. Weighted metrics shows accuracy of about 89.42% with precision of about 85.82% and thus shows the effectiveness of the system.</span>

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S. Kasifa Farnaaz and A. Sureshbabu. "Twitter Sentiment Analysis Using Deep Learning Techniques." International Journal for Modern Trends in Science and Technology 8, no. 2 (April 10, 2022): 210–18. http://dx.doi.org/10.46501/ijmtst0802035.

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The World Wide Web has taken a serious look at new ways for individuals to express their viewpoints and conclusions on a variety of topics, models, and concerns. Clients provide material for a variety of media, such as web gatherings, discussion groups, and weblogs, and provide a robust and open foundation for gaining clout in areas such as promoting and research. Strategy, justification research, market estimations, and a business perspective are all important considerations. Theory study eliminates derivations from publicly available data and organizes the sentiments that the author associates with a given object into one of two specified categories (positive and negative). Make a distinction between the two problems. This follows a Twitter speculation audit cycle for quickly seeking unstructured news. Furthermore, we're looking at several ways to present an itemized positive assessment on Twitter News. It also shows a parametric relationship between operations that are influenced by perceived boundaries. The qualities conveyed in them address the tweets: positive, negative, or fair. This work will in general present the defense appreciate exploring on Twitter; the qualities conveyed in them address the tweets: positive, negative, or fair. Twitter is a web-based application that integrates with a blog and a wide range of contacts, allowing users to send brief 140-character messages. It's a rapidly growing partnership with over 200 million endorsers, 100 million of whom are active clients, and a large portion of them follow Twitter on a regular basis, sending out over 250 million tweets. This study aims to perform Sentimental analysis using deep learning with bigrams and trigrams to classify the tweets accurately.

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Ganapathy, Apoorva. "Speech Emotion Recognition Using Deep Learning Techniques." ABC Journal of Advanced Research 5, no. 2 (2016): 113–22. http://dx.doi.org/10.18034/abcjar.v5i2.550.

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The developments in neural systems and the high demand requirement for exact and close actual Speech Emotion Recognition in human-computer interfaces mark it compulsory to liken existing methods and datasets in speech emotion detection to accomplish practicable clarifications and a securer comprehension of this unrestricted issue. The present investigation assessed deep learning methods for speech emotion detection with accessible datasets, tracked by predictable machine learning methods for SER. Finally, we present-day a multi-aspect assessment between concrete neural network methods in SER. The objective of this investigation is to deliver a review of the area of distinct SER.

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