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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Tyagi, Anshuman Singh, Pawan Dev, Harsh Amity University Amity School of Engineering and Technology Lucknow Department of Computer Science and Engineering Uttar Pradesh India Pranveer Singh Institute of Technology Department of Computer Science & Engineering Uttar Pradesh Kanpur India

ISBN: (纸本)9798350335095

Human Activity Recognition is a recognition technique which identify and name the human activities by AI. It collects activity data from devices such as wearable sensors and smart devices etc. Literally, it classifies human activities on the basis of interaction, motion and action. Nowadays, its usability is spread in various applications so, the research on Human Activity Recognition is still ongoing with viable enhancements. Though, this technique needs to solve limitations such as collection and processing of data, stability of hardware and approaches, complexity in activity detection and messed up activities. This work is based on Human Activity Recognition which is trained by the hybrid model of classifiers such as LSTM and Bi-GRU via proposed Self-Improved Namib Beetle Optimization model (SINBO). The activity recognition is detected by two stages: (1) Preprocessing, (2) Feature Extraction and (3) Classification. The extracted features areImproved bag of visual words, Local texton XOR pattern and SLIF are given as an input in classification stage. The hybrid model of LSTM and Bi-GRU is enhanced by optimal tuning of weights via SINBO algorithm. © 2023 IEEE.

关键词： Feature extraction

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Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

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computers, Materials & Continua 2024年第1期78卷 1095-1113页

作者： Fang Xu Songhao Jiang Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China School of Computer Science and Information Engineering Hubei UniversityWuhan430062China Artificial Intelligence Industrial Technology Research Institute Hubei Engineering UniversityXiaogan432000China

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

关键词： SIoT data forwarding social attributes social relations community

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Self-Improved Beluga whale optimization algorithm based Optimal Trained Deep Maxout Model for Glaucoma Detection 14

Self-Improved Beluga whale optimization algorithm based Opti...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Piyush Bhushan Singh, Pawan Dev, Harsh Amity University Amity School of Engineering and Technology Lucknow Department of Computer Science and Engineering Uttar Pradesh India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Kanpur India

ISBN: (纸本)9798350335095

Glaucoma is a fast-growing retinal disease, which is a chronic and neurodegenerative disease that affects patients progressively. The neuro-retinal nerve called the optic nerve, which connects the eye to the brain damages the eye due to lack of proper diagnosis. To diagnose this disease, ophthalmologists need to perform various tests. However, taking more tests requires a lot of time and that is expensive. Also, the early stage of glaucoma may not show any vision loss to the affected people. For detecting glaucoma, this paper intends to propose the glaucoma detection model that follows four phases including preprocessing, segmentation, Feature extraction, and glaucoma detection. In the Preprocessing phase, the retinal images are processed by median filtering. Subsequently, subjecting the preprocessed image to the segmentation, where the optic cup will be segmented by the improved U-net model. Subsequently, the features such as cup-to-disc ratio, inferior superior nasal temporal features, fractal features, and proposed Local Gabor features are derived from the segmented image. These extracted features are trained to the optimized DeepMaxout classifier for detection purposes. Finally, a novel self-improved Beluga whale optimization algorithm is proposed for the optimal training of DMN by tuning its optimal weights. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Enhancing Patient Care: A Data-Driven Approach Using Decision Tree-ELM Models for Improved Safety and Quality 3

Enhancing Patient Care: A Data-Driven Approach Using Decisio...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Telsang, Supriya Shrikant Singh, Akhya Sanjay Kaur, Simarpreet Hazela, Bramah Singh, Shikha Chittapragada, Hemanand Vishwakarma Institute of Technology Bibwewadi Maharashtra Pune India Shah & Anchor Kutchhi Engineering College Mumbai India Amity University Amity School of Engineering and Technology Lucknow Department of Computer Science and Engineering Uttar Pradesh India Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Andhra Pradesh Madanapalle India

ISBN: (纸本)9798350354171

In recent years, educational initiatives that seek to combine teaching medical students about patient safety and quality improvement (QI) have received funding from both the World Health Organization and the American Association of Medical Colleges. When developing curriculum, it is important to keep in mind the interests and values of the target audience. The proposed used a poll to find out how students felt about lessons on patient safety and quality improvement. It would be more efficient to incorporate patient safety and quality improvement into clinical education rather than teach it in separate computer modules or during pre-clinical lectures. This proposed approach consists of three stages, which are feature selection, model training, and preprocessing. Before doing thorough text processing, stop words are typically filtered out in conventional methods of natural language processing and information retrieval. Unigram and bigram selection are part of feature selection. During the entire model training process, a Decision Tree-ELM Ensemble was utilized. This innovative approach outperforms ELM and Decision Tree with an average accuracy of 94.36%. © 2024 IEEE.

关键词： Students

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Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

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computers, Materials & Continua 2023年第4期75卷 891-909页

作者： Aswathy Ravikumar Harini Sriraman School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this ***-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.

关键词： Pneumonia prediction distributed deep learning data parallel model ensemble deep learning class imbalance skewed data

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Estimation of Reliability Parameters of a Thermal Power Plant Through Perfect Switching Over the Device Using Boolean Laws of Algebra 2nd

Estimation of Reliability Parameters of a Thermal Power Plan...

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2nd Biennial International Symposium on Fluids and Thermal engineering, FLUTE 2023

作者： Joyal, Jeez Gupta, Surbhi Sharma, Ajay Dixit, Shubhra Amity Institute of Applied Science Amity University Noida India Amity School of Engineering and Technology Amity University Noida India

ISBN: (纸本)9789819724802

For a country to grow socioeconomically, it is essential that there be reliable, sufficient power. Power companies must place a high priority on offering consumers electricity that is both reasonably priced and incredibly dependable in today competitive market. On a global scale, nations capacity to quickly develop their industrial and commercial sectors depends on the availability of dependable electric power. Continuous access to reliable, high-quality energy is now essential for growth in both industrialised and developing nations. As a result, it is advised to perform a thorough study to balance service quality and cost. Any system planning, scheduling and maintenance now need the use of reliability analysis. An efficient power system with high standards of quality and dependability is anticipated to deliver electricity. It is no longer believed that deterministic reliability indices are useful for producing reserve capacity. As a result, the dependability characteristics necessary for thermal power plants to operate successfully are thoroughly analysed in this paper. The study concludes that a probabilistic strategy for evaluating thermal power plants yields better outcomes than a deterministic one. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Thermoelectric power plants

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Efficient Gait Analysis Using Deep Learning Techniques

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computers, Materials & Continua 2023年第3期74卷 6229-6249页

作者： K.M.Monica R.Parvathi School of Computer Science and Engineering Vellore Institute of TechnologyChennai600117India

Human Activity Recognition(HAR)has always been a difficult task to *** is mainly used in security surveillance,human-computer interaction,and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things(IoT).Human Activity Recognition data can be recorded with the help of sensors,images,or *** daily routine-based human activities such as walking,standing,sitting,etc.,could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network(2D CNN)MODEL,Long Short Term Memory(LSTM)Model,Bidirectional long short-term memory(Bi-LSTM)are used for the *** has been demonstrated that recognizing the daily routine-based on human activities can be extremely accurate,with almost all activities accurately getting recognized over 90%of the ***,because all the examples are generated from only 20 s of data,these actions can be recognised *** from classification,the work extended to verify and investigate the need for wearable sensing devices in individually walking patients with Cerebral Palsy(CP)for the evaluation of chosen Spatio-temporal features based on 3D foot ***-control research was conducted with 35 persons with CP ranging in weight from 25 to 65 *** Motion Capture(OMC)equipment was used as the referral method to assess the functionality and quality of the foot-worn *** average accuracy±precision for stride length,cadence,and step length was 3.5±4.3,4.1±3.8,and 0.6±2.7 cm *** cadence,stride length,swing,and step length,people with CP had considerably high inter-stride ***-worn sensing devices made it easier to examine Gait Spatio-temporal data even without a laboratory set up with high accuracy and precision about gait abnormalities in people who have CP during linear walking.

关键词： Human activity recognition neural networks gait analysis wearable sensors kinetic analysis cerebral palsy

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Intrusion detection and prevention system for an IoT environment

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Digital Communications and Networks 2022年第4期8卷 540-551页

作者： Ajay Kumar K.Abhishek M.R.Ghalib A.Shankar X.Cheng Department of Computer Science and Engineering NIT PatnaBiharIndia Faculty of Science Engineering Computing(SEC)De Montfort UniversityDubaiUnited Arab Emirates Department of CSE Amity School of Engineering and TechnologyAmity UniversityUttar PradeshIndia Department of Computer Science Middlesex UniversityLondonUK

Internet of Things(IoT)security is the act of securing IoT devices and *** devices,including industrial machines,smart energy grids,and building automation,are extremely *** the goal of shielding network systems from illegal access in cloud servers and IoT systems,Intrusion Detection Systems(IDSs)and Network-based Intrusion Prevention Systems(NBIPSs)are proposed in this *** intrusion prevention system is proposed to realize NBIPS to safeguard top to bottom *** proposed NBIPS inspects network activity streams to identify and counteract misuse *** NBIPS is usually located specifically behind a firewall,and it provides a reciprocal layer of investigation that adversely chooses unsafe *** IPS sensors can be installed either in an inline or a passive *** inline sensor is installed to monitor the traffic passing through *** sensors are installed to stop attacks by blocking the traffic using an IoT signature-based protocol.

关键词： IDS and IPS Cloud Firewall Denial of service Smart environment Internet of things NBIPS

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Modified MMS: Minimization Approach for Model Subset Selection

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computers, Materials & Continua 2023年第10期77卷 733-756页

作者： C.Rajathi P.Rukmani School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Considering the recent developments in the digital environment,ensuring a higher level of security for networking systems is *** security approaches are being constantly developed to protect against evolving *** ensemble model for the intrusion classification system yielded promising results based on the knowledge of many prior *** research work aimed to create a more diverse and effective ensemble *** this end,selected six classification models,Logistic Regression(LR),Naive Bayes(NB),K-Nearest Neighbor(KNN),Decision Tree(DT),Support Vector Machine(SVM),and Random Forest(RF)from existing study to run as independent *** the individual models were trained,a Correlation-Based Diversity Matrix(CDM)was created by determining their *** models for the ensemble were chosen by the proposed Modified Minimization Approach for Model Subset Selection(Modified-MMS)from Lower triangular-CDM(L-CDM)as *** proposed algorithm performance was assessed using the Network Security Laboratory—Knowledge Discovery in Databases(NSL-KDD)dataset,and several performance metrics,including accuracy,precision,recall,and *** selecting a diverse set of models,the proposed system enhances the performance of an ensemble by reducing overfitting and increasing prediction *** proposed work achieved an impressive accuracy of 99.26%,using only two classification models in an ensemble,which surpasses the performance of a larger ensemble that employs six classification models.

关键词： Ensemble learning intrusion detection minimization model diversity

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Progress on the Fault Diagnosis Approach for Lithium-ion Battery Systems:Advances,Challenges,and Prospects

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Protection and Control of Modern Power Systems 2024年第5期9卷 16-41页

作者： Hanxiao Liu Luan Zhang Liwei Li School of Control Science and Engineering Shandong UniversityJinan 250061China School of Computer Science and Technology Shandong UniversityQingdao 266237China

Because of their advantages of high energy and power density,low self-discharge rate,and long lifespan,lithium-ion batteries(LIBs)have been widely used in many applications such as electric vehicles,energy storage systems,smart grids,***,lithium-ion battery systems(LIBSs)frequently malfunction because of complex working conditions,harsh operating environment,battery inconsistency,and inherent defects in battery ***,safety of LIBSs has become a prominent problem and has attracted wide ***,efficient and accurate fault diagnosis for LIBs is very *** paper provides a comprehensive review of the latest research progress in fault diagnosis for ***,the types of battery faults are comprehensively introduced and the characteristics of each fault are ***,the fault diagnosis methods are systematically elaborated,including model-based,data processing-based,machine learning-based and knowledge-based *** latest research is discussed and existing issues and challenges are presented,while future developments are also *** aim is to promote further researches into efficient and advanced fault diagnosis methods for more reliable and safer LIBs.

关键词： Battery management system battery safety fault diagnosis lithiumion battery system

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