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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Burri, Srinivasa Rao Diallo, Mamadou Yero Sharma, Lakshay Dutt, Vishal Western Union DenverCO United States Sharda University Computer Science & Engineering Greater Noida India Indraprastha Institute of Information Technology Computer Science & Applied Mathematics Delhi India Chandigarh University Department of Cse Punjab Mohali India

ISBN: (纸本)9798350342338

This paper examines how AI has revolutionised drug development and medical research using the ChEMBL dataset. The primary study areas are AI-driven therapeutic target identification., computational approaches in drug development., drug repurposing for COVID-19 therapies., and AI methods for natural leather flaw detection. Target selection must balance novelty and confidence., and AI-driven therapeutic target identification is considered. Structure-based virtual screening and profound learning predictions of ligand properties and target activities are considered for application in scaling up to broader chemical spaces. AI is used to discover new links between drugs., targets., and diseases and treat COVID-19. The paper also highlights this field's enforcement challenges and offers solutions. A Generative Adversarial Network (GAN)-based automatic flaw identification system for natural leather is another topic of study. The results show that the suggested strategy is economical and accurate., despite limitations and biases. AI has revolutionised medical diagnostics., medication development., and precision medicine., making this work meaningful. This paper's findings offer a cross-disciplinary perspective on artificial intelligence's potential in healthcare., revealing knowledge gaps and suggesting further research. © 2023 IEEE.

关键词： Artificial Intelligence Chemical Biology Database (ChEMBL) Dataset Computational Approaches COVID-19 Treatments Defect Identification Drug Discovery Drug Repurposing Generative Adversarial Network (GAN) Precision Medicine Therapeutic Target Identification

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A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

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Intelligent Automation & Soft Computing 2023年第8期37卷 1275-1291页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation *** classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS ***,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual *** ability to extract important features is vital in making RST classification more *** work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction *** used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification *** experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art *** proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt ***,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.

关键词： Road surface type classification deep learning inertial sensor deep pyramidal residual network squeeze-and-excitation module

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Cryptography: Recent research trends of encrypting mathematics

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Materials Today: Proceedings 2022年 56卷 3247-3253页

作者： Kikani, Ria Jatin Verma, Kirti Navalakhe, Rachna Shrivastava, Gourav Shrivastava, Vineeta Department of Information Technology Silver Oak College of Engineering and Technology GTU Gujarat India Department of Engineering Mathematics Lakshmi Narain College of Technology MP Bhopal India Department of Applied Mathematics and Computational Science Shri G.S. Institute of Technology and Science MP Indore India Department of Computer Science and Engineering SAGE University MP Bhopal India Department of Computer Science and Engineering Lakshmi Narain College of Technology MP Bhopal India

In today's world where technology and mathematics are progressing hand in hand there are so many things to be considered and thought of when it comes to network security. Cryptography plays a prominent and an important role when it comes to securing the system from unwanted hazardous attacks. As the Internet has made great strides in different domains it has impacted the human life in several ways and hence data security has become the question of the hour. In such a scenario cryptography plays a big role as it ensures that the data transmitted is received as it is without any alteration or modification. However, we have discussed the algorithms and various methods required in order to maintain the security. In the subsequent sections we shall be discussing about theoretical computer science, cryptography attacks, algorithms and its application in everyday life. © 2021

关键词： Cryptography

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FENCE DECOMPOSITIONS AND CHERRY COVERS IN NON-BINARY PHYLOGENETIC NETWORKS

arXiv

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arXiv 2024年

作者： Pons, Joan Carles López, Pau Vives Murakami, Yukihiro van Iersel, Leo Department of Mathematics and Computer Science Universitat de les Illes Balears Spain Delft Institute of Applied Mathematics Delft University of Technology Netherlands

Reticulate evolution can be modelled using phylogenetic networks. Tree-based networks, which are one of the more general classes of phylogenetic networks, have recently gained eminence for its ability to represent evolutionary histories with an underlying tree structure. To better understand tree-based networks, numerous characterizations have been proposed, based on tree embeddings, matchings, and arc partitions. Here, we build a bridge between two arc partition characterizations, namely maximal fence decompositions and cherry covers. Results on cherry covers have been found for general phylogenetic networks. We first show that the number of cherry covers is the same as the number of support trees (underlying tree structure of tree-based networks) for a given semi-binary network. Maximal fence decompositions have only been defined thus far for binary networks (constraints on vertex degrees). We remedy this by generalizing fence decompositions to non-binary networks, and using this, we characterize semi-binary tree-based networks in terms of forbidden structures. Furthermore, we give an explicit enumeration of cherry covers of semi-binary networks, by studying its fence decomposition. Finally, we prove that it is possible to characterize semi-binary tree-child networks, a subclass of tree-based networks, in terms of the number of their cherry covers. Copyright © 2024, The Authors. All rights reserved.

关键词： Fences

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Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 3371-3385页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayaoThailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Falls are the contributing factor to both fatal and nonfatal injuries in the ***,pre-impact fall detection,which identifies a fall before the body collides with the floor,would be ***,researchers have turned their attention from post-impact fall detection to pre-impact fall ***-impact fall detection solutions typically use either a threshold-based or machine learning-based approach,although the threshold value would be difficult to accu-rately determine in threshold-based ***,while additional features could sometimes assist in categorizing falls and non-falls more precisely,the esti-mated determination of the significant features would be too time-intensive,thus using a significant portion of the algorithm’s operating *** this work,we developed a deep residual network with aggregation transformation called FDSNeXt for a pre-impact fall detection approach employing wearable inertial *** proposed network was introduced to address the limitations of fea-ture extraction,threshold definition,and algorithm *** training on a large-scale motion dataset,the KFall dataset,and straightforward evaluation with standard metrics,the proposed approach identified pre-impact and impact falls with high accuracy of 91.87 and 92.52%,*** addition,we have inves-tigated fall detection’s performances of three state-of-the-art deep learning models such as a convolutional neural network(CNN),a long short-term memory neural network(LSTM),and a hybrid model(CNN-LSTM).The experimental results showed that the proposed FDSNeXt model outperformed these deep learning models(CNN,LSTM,and CNN-LSTM)with significant improvements.

关键词： Pre-impact fall detection deep learning wearable sensor deep residual network

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Adaptive Trust-Based Optimal Route Selection Algorithm in MANET Using Sooty Tern Optimization Algorithm 7

Adaptive Trust-Based Optimal Route Selection Algorithm in MA...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Goswami, Subhrananda Johardar, Subhankar Mondal, Sukumar Das, Chandan Bikash Raja Narendra Lal Khan Women's College Research Centre of Natural and Applied Sciences Department of Computer Science India Haldia Institute of Technology Department of Computer Science India Raja Narendra Lal Khan Women's College Department of Mathematics India Tamralipta Mahavidyalaya Department of Mathematics India

ISBN: (纸本)9798350340600

An infrastructure-free, a self-configuring network of mobile nodes, known as a Mobile Ad hoc Network (MANET), has recently gained popularity due to its simple setup. MANET has become widely applicable in various fields. In recent years, diverse research works have been explored to provide strategies for attaining the optimal path for energy routing in MANET. Thus, it faces several challenges which could not be more effective in the existing approaches. Here, the route maintenance, route setup time, and routing overhead problems become serious consideration issues in the MANET. An energy-efficient routing protocol might perform substantially worse than a standard routing protocol if it is not designed properly. MANET faces several issues that are related to security design, such as restricted resources, shared wireless media, dynamic network topology and open end-to-end networks. Due to the dynamic nature of nodes, it could be difficult to establish a Quality of Service (QoS) while the routing occurs in the MANET. To solve these issues, a trust-based optimal path selection in MANET is designed using the Sooty Tern Optimization Algorithm (STOA). Here, the different multi-objective functions like throughput, path loss, the energy that is still available in the battery, link cost, packet energy cost, end-to-delay, routing overhead ratio, energy consumption, packet delivery ratio and security which help to provide an effective data communication in MANET. In the developed model, security is the main constraint to be considered for the optimal routing scheme in the MANET. The implementation results are validated with the traditional optimal routing schemes in MANET to ensure the effectiveness of the suggested routing approach. © 2023 IEEE.

关键词： Mobile ad hoc networks

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An Efficient ResNetSE Architecture for Smoking Activity Recognition from Smartwatch

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Intelligent Automation & Soft Computing 2023年第1期35卷 1245-1259页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering Image Information and Intelligence LaboratoryFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Smoking is a major cause of cancer,heart disease and other afflictions that lead to early *** effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment *** activities often accompany other activities such as drinking or ***,smoking activity recognition can be a challenging topic in human activity recognition(HAR).A deep learning framework for smoking activity recognition(SAR)employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules(ResNetSE)to increase the effectiveness of the SAR *** proposed model was tested against basic convolutional neural networks(CNNs)and recurrent neural networks(LSTM,BiLSTM,GRU and BiGRU)to recognize smoking and other similar activities such as drinking,eating and walking using the UT-Smoke *** different scenarios were investigated for their recognition performances using standard HAR metrics(accuracy,F1-score and the area under the ROC curve).Our proposed ResNetSE outperformed the other basic deep learning networks,with maximum accuracy of 98.63%.

关键词： Smoking activity recognition deep residual network smartwatch sensors deep learning

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An Energy-Efficient Optimal Routing Model for Mobile Ad Hoc Network Using Mine Blast Algorithm

An Energy-Efficient Optimal Routing Model for Mobile Ad Hoc ...

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2023 International Conference in Advances in Power, Signal, and Information technology, APSIT 2023

作者： Goswami, Subhrananda Mondal, Sukumar Johardar, Subhankar Das, Chandan Bikash Research Centre of Natural and Applied Sciences Raja Narendra Lal Khan Women's College Department of Computer Science India Raja Narendra Lal Khan Women's College Department of Mathematics India Haldia Institute of Technology Department of Computer Science India Tamralipta Mahavidyalaya Department of Mathematics India

ISBN: (纸本)9798350339369

The collection of wireless mobile nodes dynamically in a temporary network is termed a Mobile Ad Hoc Network (MANET). Here, energy consumption is an important complication in MANET because the mobile node doesn't offer a permanent power supply to the network, and they highly depend on batteries. The lifetime of the batteries is exhausted quickly as the nodes are traveled and change their positions frequently in the MANET network. The key factor for MANET is efficient energy to improve the network lifetime. Moreover, the efficacies of MANET are increased by different criteria like residual energy and link stability, which are always required to optimize. The major goal of multi-objective optimization issues is to reduce the energy consumption rate and also enhance the lifetime of the network. Based on the battery-dependent nodes, the efficacy of MANET is reduced. So, it is more need to select multiple optimal node disjoint paths among the destination and source to transfer the data. Further, multi-objective functions are utilized to choose the optimal path that offers the optimum outcome regarding traffic load, hop and energy consumption. So, efficient routing protocols for energy efficiency improvement in MANET are developed by utilizing the Mine Blast Algorithm (MBA). All the sensor nodes of the MANET are taken for experimentation purposes. The shortest paths in the MANET model are optimized by implementing the MBA. The parameters optimized by MBA are link cost, path loss, consumption of energy, an energy that is still available in the battery, throughput, packet energy cost, end-to-delay, routing overhead ratio, and packet delivery ratio to evaluate the overall efficiency of the network. Parametric studies of the proposed MBA are done. The developed energy-efficient MBA-MANET systems are compared with various existing techniques to verify the improved energy efficiency of the developed MBA-MANET model. © 2023 IEEE.

关键词： Sensor nodes

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Hybrid Machine Learning Algorithms for Optimal Diagnosis of Heart Disease with Feature Analysis

Hybrid Machine Learning Algorithms for Optimal Diagnosis of ...

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2023 International Conference on Power, Instrumentation, Energy and Control, PIECON 2023

作者： Ahmad, Ghulab Nabi Fatima, Hira Shafiullah Haris, Mohammad Institute of Applied Sciences Mangalayatan University Aligarh India BRA K.C.T.C College Raxual Bihar University Department of Mathematics Muzaffarpur India Institute of Technology and Management Department of Computer Science and Engineering Aligarh India

ISBN: (纸本)9798350399769

Timely prediction of heart disease and its cause is the most challenging issue in medical science. This paper uses various machine learning algorithms such as Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbourhood, and K-fold cross-validation are used to predict heart diseases. The system uses a K-fold cross-validation technique to enhance the accuracies of algorithms. The UCI Kaggle Cleveland heart disease datasets is used to analyse the performance of the models. It is found in the experiment that the training accuracy of K-Nearest Neighbour is 88.52%, and Recall is 93.30%. The Random Forest produced the highest and most comparable Receiver Operating Characteristics Curve accuracy. Moreover, the experimental results of the recommended techniques are compared with previous heart disease prediction studies, and it is found that among the suggested technique, the performance of K-Nearest Neighbour is best. The fundamental goal of this study is to design a novel and distinctive model-creation approach for resolving practical issues. © 2023 IEEE.

关键词： Adaptive boosting

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A Wild Sheep Chase Through an Orchard

arXiv

引用

arXiv 2024年

作者： Dempsey, Jordan van Iersel, Leo Jones, Mark Murakami, Yukihiro Zeh, Norbert Faculty of Computer Science Dalhousie University Halifax Canada Delft Institute of Applied Mathematics Delft University of Technology Netherlands

Orchards are a biologically relevant class of phylogenetic networks as they can describe treelike evolutionary histories augmented with horizontal transfer events. Moreover, the class has attractive mathematical characterizations that can be exploited algorithmically. On the other hand, undirected orchard networks have hardly been studied yet. Here, we prove that deciding whether an undirected, binary phylogenetic network is an orchard—or equivalently, whether it has an orientation that makes it a rooted orchard—is NP-hard. For this, we introduce a new characterization of undirected orchards which could be useful for proving positive results. Copyright © 2024, The Authors. All rights reserved.

关键词： Orchards

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