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deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics

JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 2023年第18期41卷 9177-9192页

作者： Raza, Ali Chohan, Talha Ali Buabeid, Manal Arafa, El-Shaima A. Chohan, Tahir Ali Fatima, Batool Sultana, Kishwar Ullah, Malik Saad Murtaza, Ghulam Univ Lahore Fac Pharm Dept Pharmaceut Chem Lahore Pakistan Univ Lahore Inst Mol Biol & Biochem Lahore 5400 Pakistan UVAS Inst Pharmaceut Sci Lahore Pakistan Ajman Univ Coll Pharm & Hlth Sci Dept Clin Sci Ajman U Arab Emirates Ajman Univ Ctr Med & Bioallied Hlth Sci Res Ajman U Arab Emirates Bahauddin Zakariya Univ Dept Biochem Multan Pakistan Govt Coll Univ Dept Pharm Faisalabad Pakistan COMSATS Univ Islamabad Dept Pharm Lahore Campus Lahore Pakistan

Artificial intelligence (AI) development imitates the workings of the human brain to comprehend modern problems. The traditional approaches such as high throughput screening (HTS) and combinatorial chemistry are lengthy and expensive to the pharmaceutical industry as they can only handle a smaller dataset. deep learning (DL) is a sophisticated AI method that uses a thorough comprehension of particular systems. The pharmaceutical industry is now adopting DL techniques to enhance the research and development process. Multi-oriented algorithms play a crucial role in the processing of QSAR analysis, de novo drug design, ADME evaluation, physicochemical analysis, preclinical development, followed by clinical trial data precision. In this study, we investigated the performance of several algorithms, including deep neural networks (DNN), convolutional neural networks (CNN) and multi-task learning (MTL), with the aim of generating high-quality, interpretable big and diverse databases for drug design and development. Studies have demonstrated that CNN, recurrent neural network and deep belief network are compatible, accurate and effective for the molecular description of pharmacodynamic properties. In Covid-19, existing pharmacological compounds has also been repurposed using DL models. In the absence of the Covid-19 vaccine, remdesivir and oseltamivir have been widely employed to treat severe SARS-CoV-2 infections. In conclusion, the results indicate the potential benefits of employing the DL strategies in the drug discovery process. Communicated by Ramaswamy H. Sarma

关键词： Drug discovery artificial intelligence deep learning deep learning algorithms database drug design SARS-CoV-2 SMILES format

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Real-time sports injury monitoring system based on the deep learning algorithm

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BMC MEDICAL IMAGING 2024年第1期24卷 1页

作者： Ren, Luyao Wang, Yanyan Li, Kaiyong Nanjing Forestry Univ Dept Phys Educ Nanjing 210037 Jiangsu Peoples R China Beijing Foreign Studies Univ Dept Phys Educ Beijing 100089 Peoples R China Qinghai Nationalities Univ Coll Phys & Elect Informat Engn Xining 810007 Qinghai Peoples R China

In response to the low real-time performance and accuracy of traditional sports injury monitoring, this article conducts research on a real-time injury monitoring system using the SVM model as an example. Video detection is performed to capture human movements, followed by human joint detection. Polynomial fitting analysis is used to extract joint motion patterns, and the average of training data is calculated as a reference point. The raw data is then normalized to adjust position and direction, and dimensionality reduction is achieved through singular value decomposition to enhance processing efficiency and model training speed. A support vector machine classifier is used to classify and identify the processed data. The experimental section monitors sports injuries and investigates the accuracy of the system's monitoring. Compared to mainstream models such as Random Forest and Naive Bayes, the SVM utilized demonstrates good performance in accuracy, sensitivity, and specificity, reaching 94.2%, 92.5%, and 96.0% respectively.

关键词： Sports injury monitoring deep learning algorithms Machine learning Medical applications

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How much are we exposed to alcohol in electronic media? Development of the Alcoholic Beverage Identification deep learning Algorithm (ABIDLA)

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DRUG AND ALCOHOL DEPENDENCE 2020年第0期208卷 107841-000页

作者： Kuntsche, Emmanuel Bonela, Abraham Albert Caluzzi, Gabriel Miller, Mia He, Zhen La Trobe Univ Ctr Alcohol Policy Res Melbourne Vic Australia La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic Australia

Background: Evidence demonstrates that seeing alcoholic beverages in electronic media increases alcohol initiation and frequent and excessive drinking, particularly among young people. To efficiently assess this exposure, the aim was to develop the Alcoholic Beverage Identification deep learning Algorithm (ABIDLA) to automatically identify beer, wine and champagne/sparkling wine from images. Methods: Using a specifically developed software, three coders annotated 57,186 images downloaded from Google. Supplemented by 10,000 images from ImageNet, images were split randomly into training data (70 %), validation data (10 %) and testing data (20 %). For retest reliability, a fourth coder re-annotated a random subset of 2004 images. algorithms were trained using two state-of-the-art convolutional neural networks, Resnet (with different depths) and Densenet-121. Results: With a correct classification (accuracy) of 73.75 % when using six beverage categories (beer glass, beer bottle, beer can, wine, champagne, and other images), 84.09 % with three (beer, wine/champagne, others) and 85.22 % with two (beer/wine/champagne, others), Densenet-121 slightly outperformed all Resnet models. The highest accuracy was obtained for wine (78.91 %) followed by beer can (77.43 %) and beer cup (73.56 %). Interrater reliability was almost perfect between the coders and the expert (Kappa = .903) and substantial between Densenet-121 and the coders (Kappa = .681). Conclusions: Free from any response or coding burden and with a relatively high accuracy, the ABIDLA offers the possibility to screen all kinds of electronic media for images of alcohol. Providing more comprehensive evidence on exposure to alcoholic beverages is important because exposure instigates alcohol initiation and frequent and excessive drinking.

关键词： Beverage recognition Alcohol exposure deep learning algorithms

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deep learning-Driven Surveillance for Intrusion Identification using Proactive Intelligent Video Recognition 20

Deep Learning-Driven Surveillance for Intrusion Identificati...

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20th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)

作者： Challagundla, Yagnesh Parvatham, Shreeya Dheera Mohanty, Sachi Nandan Ravindra, J. V. R. VIT AP Univ Sch Comp Sci & Engn SCOPE Amaravati India Vardhaman Coll Engn Dept Elect & Commun Engn Hyderabad Telangana India

ISBN: (纸本)9798350374292;9798350374285

In today's security-conscious environment, the need for effective real-time weapon detection systems is paramount, especially in public spaces and sensitive areas. The primary objective of this research is to create a real-time system for detecting weapons utilizing advanced deep learning techniques, namely VGG16 and Faster RCNN. The system's objective is to precisely detect and categorize weapons from photos and video data, offering prompt notifications and improving security procedures. The project's backdrop explores the difficulties encountered, including data gathering, precision standards, immediate processing, confidentiality, and deployment considerations. The system achieves excellent accuracy and real-time detection capabilities by building a comprehensive dataset manually and training the models using GPUs and modern technologies. Using Python as the programming language provides flexibility and simplicity in development, making use of Python's modules such as OpenCV for image processing and Keras for deep learning models. The Tkinter framework enables the creation of a graphical user interface (GUI) that supports various user operations, such as uploading datasets, generating models, processing images and videos, detecting weapons, and visualizing results. The methodology employs a systematic approach, encompassing stages such as data preprocessing, model building, training, testing, and result analysis. The combination of VGG16 and Faster RCNN algorithms demonstrates a compromise between speed and accuracy, with Faster RCNN exhibiting greater performance in real-time detection. The project aims to achieve several objectives, including the compilation of a dataset, training a model, evaluating accuracy, implementing real-time detection, and establishing alerting methods. The system's range encompasses a wide range of surroundings, lighting situations, and orientations, allowing it to be flexible and suitable for a variety of security applications. To sum

关键词： Real-time weapon detection deep learning algorithms VGG16 Faster RCNN Speed vs accuracy tradeoff Real-time detection Performance analysis

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Prediction of Cardiovascular Diseases with Retinal Images Using deep learning 3

Prediction of Cardiovascular Diseases with Retinal Images Us...

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International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Vineetha, Tumu Reddy, Danda Rami Mahendra, Kandimalla Lakshmi, Ballanki Dhana Lakireddy Bali Reddy Coll Engn Comp Sci & Engn Mylavaram India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

cardiovascular diseases (CVDs) represent a formidable global health challenge, necessitating astute early detection and intervention strategies. This study introduces a sophisticated deep learning paradigm, seamlessly integrating the discriminative prowess of convolutional neural networks (CNNs) with the computational efficiency intrinsic to MobileNet architecture, aiming to discern CVDs from retinal images with unprecedented accuracy. The dataset, meticulously curated to encompass diverse clinical scenarios, undergoes scrupulous preprocessing, augmenting the model's robustness. The amalgamation of CNN architecture and streamlined MobileNet design culminates a discerning framework capable of intricate classification tasks related to CVD presence within retinal imagery. This paradigm shift holds promise for expeditious CVD diagnosis, risk stratification, and economically viable medical imaging and envisions a transformative era wherein healthcare professionals are endowed with timely insights, facilitating judicious interventions and proactive healthcare strategies. Nevertheless, the imperatives of rigorous validation and seamless integration into clinical practice loom, their fruition being pivotal to gauging the veritable clinical utility and transformative potential of this innovative synthesis of cutting-edge retinal imaging and deep learning technologies in cardiovascular health.

关键词： Retinal images deep learning algorithms Performance evaluation

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Cross-lingual textual entailment using deep learning approach 3

Cross-lingual textual entailment using deep learning approac...

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3rd International Conference on Information and Communication Technology for Development for Africa (ICT4DA)

作者： Belay, Wubie Meshesha, Million Melesew, Dagnachew Bahir Dar Univ Bahir Dar Inst Technol Fac Comp Bahir Dar Ethiopia Addis Ababa Univ Sch Informat Sci Addis Ababa Ethiopia

ISBN: (纸本)9781665436663

Natural Language processing is dealing with natural language understandings and natural language generation which enable computers to understand and analyze human language. Cross-lingual Textual Entailment (CLTE) is one of the applications of NLU if there exists premise (P) as a source language and hypothesis (H) as a target language. CLTE is challenging for transferring information between under resource (Amharic) language and high resource (English) language. To solve this problem, we have proposed Cross-lingual Textual Entailment model using deep neural network approaches. We have used Bi-LSTM to transfer sequential information, XLNet for handling a position of word and its boundary, MLP for classification and prediction outputs, and FastText to word representations. Neural machine translation is utilized for translating English sentences into Amharic sentences with IBM5 alignment. We have combined Amharic dataset with SNLI dataset and annotated based on multi-way classification. The NMT predicts 96.01% of the testing accuracy. We have obtained 89.92% training and 86.89% testing accuracy for the proposed model. The issue with this research is that it ignores multiple inferences.

关键词： Cross-lingual Textual Entailment deep learning algorithms hybrid model pre-trained models

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AUTOMATIC DEFECT DETECTION IN INFRARED THERMOGRAPHY BY deep learning ALGORITHM 42

AUTOMATIC DEFECT DETECTION IN INFRARED THERMOGRAPHY BY DEEP ...

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Conference on Thermosense - Thermal Infrared Applications XLII

作者： Fang, Qiang Nguyen, Ba Diep Castanedo, Clemente Ibarra Duan, Yuxia Maldague, Xavier Univ Laval Comp Vis & Syst Lab Dept Elect & Comp Engn 1065 Av Med Quebec City PQ G1V 0A6 Canada Cent South Univ Sch Phys & Elect 932 Lushan South Rd Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9781510635968;9781510635951

Non-destructive Evaluation (NDE) is a field that is used to identify all kinds of structural damage in an object of interest without resulting in any permanent damage or modification to the object. This field has been intensively investigated for many years. Among several research topics in this field, the supervised defect detection methods are among the most innovative and challenging. In recent years, the deep learning field of artificial intelligence has made remarkable progress in image processing applications. deep learning has shown its ability to overcome most of the disadvantages suffered by previous existing approaches in a great number of applications. In this paper, we propose a deep learning architecture based on infrared thermography inspection intended to automatically identify defects (including internal and invisible cracks, delamination, etc.) efficiently and accurately. We studied the proposed deep learning algorithms to achieve automatic defect detection and precise localization (subsurface defects case) from different thermal image sequences. To evaluate the efficiency and robustness of the proposed methodology, specimens containing artificial defects were selected for experimental configuration.

关键词： Automatic Defect detection Infrared Thermography deep learning algorithms Non-destructive Evaluation (NDE) Supervised learning Image processing

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Vehicle Trajectory Prediction Using deep learning for Advanced Driver Assistance Systems and Autonomous Vehicles 18

Vehicle Trajectory Prediction Using Deep Learning for Advanc...

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18th Annual IEEE International Systems Conference (SysCon)

作者： Alsanwy, Shehab Qazani, Mohammad Reza Chalak Al-ashwal, Wadhah Shajari, Arian Nahvandi, Saeid Asadi, Houshyar Deakin Univ IISRI Geelong Vic Australia Sohar Univ Fac Comp & Informat Technol FoCIT Sohar Oman Swinburne Univ Technol Melbourn Australia

ISBN: (纸本)9798350358810;9798350358803

Vehicle trajectory prediction is essential for advanced driver assistance systems (ADAS) and autonomous vehicles (AVs), playing a crucial role in collision avoidance, path planning, and traffic control. Traditional models often overlook the variability in driver behavior, particularly in braking patterns, which significantly impacts trajectory predictions. Our study introduces an improved trajectory prediction model that uses Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks while considering driver braking patterns along with vehicle dynamic information. The models' accuracy is evaluated in a simulated environment that replicates real-world driving conditions. This environment captures comprehensive vehicle dynamics data, encompassing critical parameters such as position, rotation, acceleration, speed, and braking patterns. To ensure a realistic and varied dataset, data were meticulously gathered from 17 drivers, each utilizing a driving simulator equipped with the Euro Truck Simulator software. The model was implemented and validated using Python 3.9, Google Colab, and Scikit-learn, selected for their robustness in deep learning applications. Our results indicate that incorporating braking patterns significantly improves position predictions, outperforming the existing models based solely on vehicle dynamic data. This was evident by a notable decrease in Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) when braking patterns were incorporated. This advancement strengthens trajectory prediction systems for ADAS and AVs, enhancing operational safety.

关键词： Vehicle Trajectory Prediction Braking Patterns Autonomous Vehicles deep learning algorithms Road Safety

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Research on Music Soundtrack Recognition Based on deep Computer learning 24

Research on Music Soundtrack Recognition Based on Deep Compu...

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4th International Conference on Computer, Internet of Things and Control Engineering

作者： Yu, Yu Guangdong Univ Sci & Technol Dongguan Guangdong Peoples R China

ISBN: (纸本)9798400711848

In an era where information technology is increasingly prevalent, music score recognition, as a vital branch of intelligent audio processing, has garnered significant attention. It not only enhances the listening experience for users but also holds substantial importance in realms such as music copyright protection, automatic scoring, and intelligent editing. The advent of deep learning technologies in computing resembles a revolution, transforming traditional audio analysis methods and rendering the processing of complex audio data more efficient and precise. By leveraging advanced deep learning models like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and deep Belief Networks (DBN), researchers can extract valuable insights from vast troves of musical data, gradually uncovering the underlying patterns hidden within notes and melodies. Particularly when confronted with an array of diverse forms and styles in musical compositions, deep learning techniques significantly enhance the accuracy and speed of score recognition, revealing extraordinary potential in applications such as automatic tagging, recommendation systems, and real-time audio processing.

关键词： Computer technology deep learning algorithms music soundtrack recognition

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deep learning offering resilience from trending cyber-attacks, a review.

Deep learning offering resilience from trending cyber-attack...

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International Conference on Computational Performance Evaluation (ComPE)

作者： Khanday, Shahbaz Ahmad Fatima, Hoor Rakesh, Nitin Sharda Univ Comp Sci Engn Knowledge Pk 3 Greater Noida Uttar Pradesh India Sharda Univ Sch Engn & Technol Knowledge Pk 3 Greater Noida Uttar Pradesh India

ISBN: (纸本)9781665436564

During the Covid-19 pandemic world has witnessed the rise of cyber-attacks, especially during the Lockdown time course announced by the countries throughout the world, when almost every aspect of life changed the routine from offline to online. Protecting and securing information resources during pandemics has been a top priority for the modern computing world, with databases, banking, Ecommerce and mailing services, etc. being the eye-catching credentials to the attackers. Apart from cryptography, machine learning and deep learning can offer an enormous amount of help in testing, training, and extracting negligible information from the data sets. deep learning and machine learning have many methods and models in the account to detect and classify the different versions of cyber-attacks occasionally, from the datasets. Some of the most common deep learning methods inspired by the neural networks are Recurrent Neural Networks, Convolutional Neural Networks, deep Belief Networks, deep Boltzman Networks, Autoencoders, and Stacked Auto-encoders. Also counting machine learning algorithms into the account, there is a vast variety of algorithms that are meant to perform classification and regression. The survey will provide some of the most important deep learning and machine learning architectures used for Cyber-security and can offer protective services against cyberattacks. The paper is a survey about various categories of cyber-attacks with a timeline of different attacks that took place in India and some of the other countries in the world. The final section of the report is about what deep learning methods can offer for developing and improving the security policies and examining vulnerabilities of an information system.

关键词： E-commerce Cryptography Machine learning deep learning algorithms

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