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DnnARs: An Artificial Intelligence Technique for Prediction of Antimicrobial Resistant Strains in E. coli Bacteria Causing Urine Tract Infection

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SN computer Science 2024年第8期5卷 1091页

作者： Nayak, Debasish Swapnesh Kumar Priyadarshini, Arpita Mahanta, Pabani Das, Tejaswini Sahoo, Santanu Kumar Swarnkar, Tripti Department of Computer Science and Engineering FET-ITER Siksha ‘O’ Anusandhan (Deemed to Be) University Bhubaneswar India Department of Computer Science and Engineering SOET Centurion University of Technology and Management Odisha Bhubaneswar India Department of Statistics Utkal University Bhubaneswar India Department of Electronics and Communication Engineering FET-ITER Siksha ‘O’ Anusandhan (Deemed to Be) University Bhubaneswar India Department of Computer Application National Institute of Technology Raipur India

The rise of antimicrobial resistance (AMR) in bacteria that cause infectious diseases poses a significant global health challenge, necessitating advanced prediction technology for effective management and response. This study aims to evaluate the predictive capabilities of several machine learning (ML) and deep learning (DL) models in identifying patterns of antibiotic resistance. We have proposed a comprehensive experimental framework that combines multiple machine learning algorithms and deep learning architectures to evaluate their efficacy and accuracy in predicting antibiotic resistance. We have developed DnnARs, an artificial intelligence pipeline designed to identify antibiotic-resistant strains of E. coli bacteria, which are mostly responsible for urinary tract infections (UTIs). DnnARs utilizes Synthetic Minority Over-sampling Technique (SMOTE) and applies feature engineering using principal component analysis (PCA) and t-distributed Stochastic neighbor embedding (t-SNE) on our dataset to evaluate the effectiveness of various AI models. Our model comparison demonstrates that DL models outperform ML models when the dataset is fine-tuned using SMOTE and feature engineering. Our DnnARs achieve a classification accuracy of 97%, a specificity of 91%, and a remarkable sensitivity of 100%. The DL models that were examined exhibit an average classification accuracy that is 3.66% higher than that of the ML models. The findings demonstrate the capability of deep learning methodology to enhance predictive analytics for antimicrobial resistance, offering a robust and efficient alternative to traditional machine learning techniques. This research aims to enhance diagnostic tools and medications, improve the management of infectious diseases, reduce antibiotic resistance, and integrate deep learning models into clinical workflows to enable immediate decision-making. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Antimicrobial resistance Artificial intelligence Deep learning Infectious disease Machine learning Urine tract infections

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An Energy-efficient Ferry-based Routing Algorithm for Scattered FANET Networks

An Energy-efficient Ferry-based Routing Algorithm for Scatte...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Saini, Ashish Kumar, Sanjeev Kumar, Amit Kumar, Satender Kumari, Amrita Quantum University Department of Computer Science & Engineering Roorkee India G.L.Bajaj Institute of Technology and Management Department of Computer Application Greater Noida India

ISBN: (纸本)9798350323917

Flying ad hoc networks (FANETs), have a wide range of applications in both civilian and military settings. Although, prevailing routing protocols are effective in well-connected networks, they struggle in sparse networks. To address this issue in FANETs, a new geographic routing method, utilizing a delay-tolerant routing protocol, is proposed in this paper. The proposed method combines position routing and delay tolerant network (DTN) to minimize routing errors. Here, beacons are transmitted by unmanned aerial vehicles (UA Vs) acting as ferries to inform about their succeeding anchor position to other nodes. The node receiving the beacon determines the closest ferry node and base station to the destination. Using the store-carry and forward approach, each node in the network handles sparse networks. Here, we focused to achieve low latency and have developed a novel communication protocol with two options to emphasize the need for lower latency and routing overhead when delivering data. Data collected by searching UAVs is sent to the base station using the ferry. The proposed model is validated using simulated scenarios, which show improved packet delivery, decreased end-to-end delay, and decreased overhead. The study highlights the importance of using communication methods that have lower latency and routing overhead to deliver data efficiently. © 2023 IEEE.

关键词： Routing protocols

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Anomaly Detection in Medical Images Using Deep Reinforcement Learning 2

Anomaly Detection in Medical Images Using Deep Reinforcement...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rastogi, Divya Sharma, Aparna Yadav, R.K. Varshney, Neeraj Saraswat, Laxmi Computer Science and Application Sharda University UP Greater Noida India Electronics & Communication Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Abes Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350371055

Anomaly detection in clinical pix is vital to identify and diagnose diseases. Deep reinforcement gaining knowledge is a practical gadget-mastering approach implemented for more than a few medical obligations. This technique applies a synthetic intelligence version to research by revealing an iterative procedure to discover a set of top-of-the-line parameters for ultimate selection-making. The cause of this examination is to use deep reinforcement mastering for anomaly detection in medical pix. This approach is used to identify strange areas in clinical pix, including tumors, mental diseases, and other abnormalities. Deep reinforcement mastering is mixed with Convolutional Neural Networks (CNNs) to extract capabilities from the medical pictures and then classify them consistent with their deviation from normal. The detection accuracy is evaluated using a novel assessment metric, referred to as the precision-recollect (PR) curve, which is designed to degree the version's accuracy. The results show that deep reinforcement learning can gain high-performance anomaly detection accuracy compared with conventional methods. © 2024 IEEE.

关键词： Accuracy Anomalous Evaluated Evaluation Measure

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Long Short-Term Memory Auto-Encoder-Based Position Prediction Model for Fixed-Wing UAV during Communication Failure

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 173-181页

作者： Kant, Ravi Saini, Poonam Kumari, Julee Defence Electronics Application Laboratory Defence Research and Development Organisation Dehradun248001 India Department of Computer Science and Engineering Punjab Engineering College Chandigarh148023 India Centre for Development of Advanced Computing Mohali160071 India

Unmanned aerial vehicles (UAV's) safe flight is one of the most important tasks of ground control stations (GCS). However, sometimes due to communication failure between ground data terminal (GDT) and UAV, GCS is not able to ensure a safe flight, which could be hazardous. In such conditions, the accurate UAV position finding prediction becomes essential to serve as sustenance for UAVs. Hence, this article proposes a UAV's position finding prediction model using a deep neural network application for successfully pointing the GDT toward UAV in real time. The proposed work consists of three components, namely data preprocessing, long short-term memory autoencoder (LSTM-AE)-based deep learning model, and mathematical formulation to calculate the ground radar look angle. First, we use different preprocessing techniques on a historical raw dataset for better visualization and generate patterns. To achieve this, Tensor flow along with Keras packages have been used. Next, an LSTM autoencoder deep learning model is applied to the same dataset in order to predict an accurate 4-D position of the UAV. Finally, the output from the LSTM autoencoder model is used in a mathematical model to calculate the GDT look-angles called Azimuth and Elevation. Both the parameters have been used to point the GDT toward the predicted point on the Airspace. We evaluate our proposed model against famous evaluation matrices, namely mean absolute error, mean square error, and root-mean-square error to validate the experimental results. © 2022 IEEE.

关键词： Real time systems

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Enhancing Cloud Infrastructure Security with GraphSAGE-Based Intrusion Detection Systems 1

Enhancing Cloud Infrastructure Security with GraphSAGE-Based...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kumar, G. Nanda Kishor Syed, Shakir Mogal, Amit Karbhari Abid, Tahera Perada, Anvesh Suneetha, Sampathirao University of South Florida United States Malla Reddy University Department of Computer Science and Engineering Hyderabad India Bargersville IN United States Mvp Samaj's Cmcs College Department of Computer Science and Application Nashik India Nawab Shah Alam Khan College of Engineering and Technology Department of Information Technology Hyderabad India Drexel University Department of Electrical and Computer Engineering Philadelphia United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798331529246

Cloud computing provides scalable and adaptable infrastructure however is significantly susceptible to cyber-attacks. This work presents a hybrid methodology that integrates the GOA and GA for feature selection, alongside the Residual GraphSAGE model for intrusion detection. The hybrid GOA-GA method enhances feature selection by optimizing the balance between exploration and exploitation, mitigating overfitting, and augmenting classification performance. The Residual GraphSAGE model alleviates over smoothing while effectively capturing node and edge interactions for precise intrusion classification. Assessment on a benchmark dataset reveals exceptional performance, attaining an accuracy of 91.74%, surpassing established models such as GCN and ResNet. This study emphasizes the need of hybrid optimization and sophisticated graph-based learning in improving cloud infrastructure security. © 2025 IEEE.

关键词： Network intrusion

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Digital Secure Storage for Healthcare Data Information Using Blockchain 3

Digital Secure Storage for Healthcare Data Information Using...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Naveenkumar, H. Ranjana, P. Hindustan Institute of Technology & Science Department of Computer Application Chennai603103 India Hindustan Institute of Technology & Science Department of Computer Science Engineering Chennai603103 India

ISBN: (纸本)9798350399264

Healthcare Blockchains grant access to a modern technique to accumulate healthcare information, achieve healthcare, and make a belief for medical records distribution and records combination in a decentralized unlock healthcare network surroundings. Even though the healthcare blockchain technologies have paid attention to extroverted benefits in addition to a concentration in assiduousness, management as well as university circles, the safety and confidentiality concerns stay behind the center of attention of deliberate while organizing Blockchains for information sharing across company operations and research collaboration in the healthcare sector. Medical data sharing via Blockchains are the subject of research that focuses on security and privacy needs. Together with technological solution techniques and strategies, the research provides a complete evaluation of the safety and confidentiality risks, requirements, and technology solutions. In the beginning, we discuss the security and privacy requirements for using healthcare Blockchains to share electronic medical records. Second, we group current initiatives into three reference blockchain procedure scenarios for the sharing of electronic medical records. We consider the technologies that can be used to implement this secure store for healthcare data properties in the two categories of healthcare Blockchains procedure scenarios, including anonymous signatures, and attribute-based encryption. Source to implement the access control for hospital, patient, doctor, admin. © 2023 IEEE.

关键词： Blockchain

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P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

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Intelligent Automation & Soft Computing 2023年第1期35卷 553-566页

作者： Ayman Altameem Ramesh Chandra Poonia Ankit Kumar Linesh Raja Abdul Khader Jilani Saudagar Department of Computer Science and Engineering College of Applied Studies and Community ServicesKing Saud UniversityRiyadh11533Saudi Arabia Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Engineering and Applications GLA UniversityMathuraUPIndia Department of Computer Application Manipal University JaipurRajasthan303007India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Data clustering is crucial when it comes to data processing and *** new clustering method overcomes the challenge of evaluating and extracting data from big *** or categorical data can be *** clustering methods favor numerical data clustering and ignore categorical data *** recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering ***,these algorithms could not use the concept of categorical data for *** that,suggestions for expanding traditional categorical data processing methods were *** addition to expansions,several new clustering methods and extensions have been proposed in recent *** is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster *** paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster *** parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also *** results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.

关键词： ROCK K-means algorithm clustering approaches unsupervised learning K-histogram

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Enhancing Rule-Based Methods for Pulmonary Effusion Diagnosis 15

Enhancing Rule-Based Methods for Pulmonary Effusion Diagnosi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kharbas, Vikas Kumar Raghu, N. Choudhury, Priyabati Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Arka Jain University Department of Pharmacy Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

The diagnosis of pulmonary effusion is a difficult assignment for scientific practitioners because of the excessive variance in the presentation and manifestation of the circumstance. Maximum current diagnosis structures rely upon the use of either sensory/visible remark of chest radiographs (CRs) by radiologists or handcrafting statistical features through area experts with the use of computer-aided analysis (CAD) tools. However, those methods regularly need better generalization and are constrained via their reliance on conventional heuristic algorithms, as well as the mistake-inclined technique of guide identification of features. This paper explores the capacity of the usage of rule-based total methods as better diagnostic structures for pulmonary effusion. Particularly, a rule-based total device primarily based on supervised mastering-based total fashions is proposed as an alternative to traditional feature identification and model development strategies. The proposed device includes an output layer and an enter layer, which includes photograph features and radiological descriptors. Experiments have been conducted on the LIDC-IDRI dataset, and the proposed system outperformed traditional feature-based approaches in terms of both accuracy and specificity. Furthermore, the use of rule-based methods for diagnosis of pulmonary effusion provides a more reliable diagnosis process, which may reduce the radiological review times and improve the accuracy of non-radiological diagnosis. © 2024 IEEE.

关键词： Conducted Diagnosis Effusion Identification Manifestation Pulmonary Radiographs

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Comparative Analysis of Node-Dependent and Node-Independent Graph Matrices for Brain Connectivity Network 1

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Kose, Mangesh Ramaji Ahirwal, Mitul Kumar Atulkar, Mithilesh Department of Computer Application NIT C.G. Raipur492010 India Department of Computer Science and Engineering MANIT M.P. Bhopal462003 India

ISBN: (数字)9789819900855

ISBN: (纸本)9789819900848

Brain connectivity network (BCN) represents an advanced approach for brain functionality analysis. In BCN, the human brain is modeled as a graph, containing vertices and connectivity links. The electroencephalogram (EEG) signals recorded from the diseased as well as healthy subjects are used to construct a BCN. The graph matrices are a quantitative approach for comparing the network of diseased and healthy subjects. The selection of graph matrices to be extracted from the network depends on whether all the networks have the same or a different number of nodes. In this paper, analysis of matrices dependent to numbers of nodes (DNN) and matrices independent to numbers of nodes (INN) has been performed. These matrices are calculated as features from schizophrenia disease EEG datasets having 16 and 19 channels. Analysis of these matrices has been done based on distance and similarity measures. From the results, it is observed that the INN features show less variability as compared to DNN. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electroencephalography

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Evaluating the Impact of Different Features on the Efficiency of Emotion Recognition Algorithms in Text 2

Evaluating the Impact of Different Features on the Efficienc...

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2nd International Conference on Artificial Intelligence and Machine Learning applications, AIMLA 2024

作者： Sharma, Sunil Ezhilarasan, G. Ghosh, Rajkumari Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India ARKA JAIN University Department of English Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

The effectiveness today's emotion recognition algorithms in texts has become increasingly more critical in current years today's its programs in supporting users gauge actual-time reactions from text-based totally conversations. On this paper, we advise a fixed state-of-the-art experiments to assess the effect that different functions, along with syntactic and semantic functions, have at the efficiency contemporary emotion reputation algorithms in textual content. We use a manually annotated dataset from the automatic have an effect on recognition in text (AART) task to create characteristic units consultant of various ranges latest functions. Those feature units are then used to assess the overall performance latest emotion recognition algorithms - Recursive Neural Networks and Convolutional Neural Networks. The results display that incorporating syntactic and semantic functions can improve the accuracy present day emotion popularity algorithms at a charge ultra-modern 6-10% while as compared with the baseline overall performance. From the findings modern this test, we conclude that incorporating syntactic and semantic features can drastically enhance the overall performance trendy emotion popularity algorithms in text-based totally conversations. © 2024 IEEE.

关键词： Semantics

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