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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Srivastava, Animesh Tiwari, Shweta Saini, Parveen Kumar Sawan, Vikash Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India University Institute of Engineering Chandigarh University Department Computer Science & Engineering Department Punjab Mohali India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Gla University Department of Computer Engineering and Applications Mathura India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350325799

As the Internet of Things (IoT) increases along with the cost of sensors decrease, more and more people are interested in the concept of the Internet of Things (IoT). Denial of service (DoS) and distributed denial of service (DDoS) attacks could be launched against these IoT devices. When there are many different types of IoT devices, security risks increase. There is currently no protocol in place to guarantee the security of IoT gadgets. However, resilience only works when there is constant observation and flexible planning. This can dynamically and flexibly deal with security threats to IoT devices without putting any extra load on the IoT devices themselves. This study proposes a strategy for quickly detecting and counteracting suspicious activity and malicious attacks. In Machine Learning, both Linear SVM and Non-Linear SVM techniques are employed, but the latter yields more accurate results. The accuracy of linear SVM is 93%. However, the accuracy increases to 97.8% when non-linear SVM is used. © 2023 IEEE.

关键词： Denial-of-service attack

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Risk Assessment and Safety-as-a-Service Provisioning in IoV Networks: A Roadmap 5

Risk Assessment and Safety-as-a-Service Provisioning in IoV ...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Pradhan, Patrali Chattopadhyay, Samiran Chowdhury, Subrata Rout, Suchismita Mishra, Manoj Ranjan Patra, Sudhansu Shekhar Haldia Institute of Technology Dept. of Computer Science and Engineering Haldia India Techno India University Kolkata India Sreenivasa Institute of Tech. and Management Studies Dept. of Computer Science and Engineering Andhra Pradesh Chittoor India Kiit Deemed to be University School of Computer Engineering Bhubaneswar India Kiit Deemed to be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350376685

This paper presents a framework for a Safety recommender system that offers Safety-as-a-Service (Safe-aaS) to on-road vehicles in IoT-based transportation systems. The proposed safety recommendation system integrates several phases, including preparing sensed data, self-supervised learning from the data, and predicting personalized safety plans. The customized safety plans, generated by analyzing on-road decision parameters such as dynamic road conditions, weather patterns, and driving assistance parameters, are suggested to registered users in real time through the Safe-aaS platform tailored to their specific *** a Safe-aaS enabled road transportation system, sensor nodes produce large volumes of data in a dynamic environment. Efficient analysis and storage of sensed data in edge devices poses a major challenge for service providers. Conversely, generating safety-related decisions with lower processing time ensures driver safety. The Safe-aaS-based recommender system provides safety-related decisions with low latency and processing delay. In the proposed recommendation system, a safety score is computed from the pre-processed sensed data across various parameters, followed by a user-specific similarity score and a corresponding risk score. We apply a fuzzy rule-based learning technique to predict optimized safety plans. To showcase the real-time scenario of our framework, we develop a user-friendly application interface at the primary stage. Extensive analysis shows that our system effectively detects on-road risks and adapts safety recommendations in dynamic weather and road conditions. © 2024 IEEE.

关键词： Sensor nodes

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An Improved Blockchain Based Security Framework for IoT Enabled Wireless Network

An Improved Blockchain Based Security Framework for IoT Enab...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Shrivastava, Kapil Singh, Sandeep Chaudhary, Preeti Singh, Ruchi Saraswat, Birendra Kumar Garg, Ashish Gla University Department Of Computer Engineering And Applications UP Mathura India Gl Bajaj Institute Of Technology And Management Department Of Information Technology UP Greater Noida India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Srm Institute Of Science And Technology Department Of Management UP Modinagar India Raj Kumar Goel Institute Of Technology Computer Science & Engineering UP Ghaziabad India Graphic Era Deemed To Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

The net of factors (Iota) has opened new opportunities for connecting a diverse range of devices and making them handy over the net. However, it has additionally accelerated the vulnerability of these gadgets to cyberassaults. One capability technique to cope with It trouble is implementing Block chain generation, which offers allotted and relaxed facts storage and transaction validation. It paper advocates a progressed Block chain-based safety framework for Iota-enabled wireless networks. Our framework uses clever contracts, which might be self-executing virtual contracts that implement the agreed-upon policies and conditions among parties. Those clever contracts ease communication among Iota gadgets and the Wi-Fi community, ensuring that the most effective legal and demonstrated devices can access the network. Our framework incorporates a consensus mechanism for validating transactions on the Block chain, ensuring the integrity and immutability of statistics. Our proposed framework contains a dynamic key control gadget, which often updates and rotates keys to prevent unauthorized admission to the community. It selection also permits relaxed communication among devices, even in the case of a compromised key. In conclusion, our improved Block chainbased total protection framework presents a strong answer for securing Iota-enabled Wi-Fi networks. © 2024 IEEE.

关键词： Wi-Fi

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Automated Knowledge Transfer for Medical Image Segmentation Using Deep Learning

Automated Knowledge Transfer for Medical Image Segmentation ...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Tyagi, Priya Masson, Palak Jain, Ayushi Varshney, Neeraj Verma, Aditya Nitra Technical Campus Department Of CSE-AIML UP Ghaziabad India Raj Kumar Goel Institute Of Technology Computer Science & Engineering UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Gla University Department Of Computer Engineering And Applications UP Mathura India Graphic Era Deemed To Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

The usage of deep trends for scientific photograph segmentation has seen a speedy increase in recognition in recent years of today's capability to generate accurate photo segmentation fashions. However, the development and deployment of these fashions may be time-eating and highly-priced state-of-the-art to engineer features and architectures for each project manually. Automatic knowledge transfer (AKT) can address this difficulty by presenting a manner to switch knowledge throughout distinct duties while requiring minimal guide attempts. AKT can obtain better effects than guide engineering procedures by extracting the most pertinent knowledge from present fashions and applying it to the goal project. It leads to a generalizable and fee-effective answer. These paper overviews the AKT and talk capability programs for medical picture segmentation. We survey the latest publications on the subject and discuss modern trends and challenges inside the place. We provide a top-level view of the latest three unique processes to AKT- transfer latest, meta-today, and self-supervised modern-day - and highlight how they can be used to obtain successful medical photo segmentation. Finally, we discuss future guidelines for research in this discipline. © 2024 IEEE.

关键词： Deep learning

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Credit Risk Customers Categorization with Random Forest Classifier using Various Searching Techniques

Credit Risk Customers Categorization with Random Forest Clas...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Samprith, S. Rajesh, T.M. Babu, Tina Nair, Rekha R Kishore, S. Dayananda Sagar Univerity School of Engineering Department of Computer Science & Engineering Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Science & Engineering Bengaluru India Alliance Univerity Department of Computer Science and Engineering Bengaluru India Nitte University Nitte Institute of Professional Education Department of Computer Applications Karnataka India

ISBN: (纸本)9798350313413

Effective credit risk assessment holds significant importance for financial institutions and businesses extending credit to customers. It is crucial to evaluate customers' creditworthiness to minimize default risks and manage financial exposure. This study focuses on enhancing credit risk customer classification through optimized feature selection and classification techniques. The dataset is preprocessed using Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and enhance performance. Relevant features are then extracted utilizing Cfs Subset evaluation along with various search algorithms. The optimized features are subsequently employed for classification using the Random Forest (RF) classifier. Experimental results reveal that, in credit customer classification, Evolutionary, Tabu, and Scatter search algorithms combined with RF classifier demonstrate a commendable accuracy of 90.31%. The proposed framework also showcases comparable outcomes in terms of other performance metrics. This automated credit customer classification method proves to be highly effective in identifying fraudulent users. © 2023 IEEE.

关键词： classifier Credit risk Detection methods Search Techniques

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Impact of Model Selection on Pulmonary Effusion Diagnosis Using Prediction Analysis Algorithms

Impact of Model Selection on Pulmonary Effusion Diagnosis Us...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Mani, Chandra Pachauri, Divya Gupta, Richa Varshney, Neeraj Manwal, Manika Raj Kumar Goel Institute Of Technology Computer Science & Engineering UP Ghaziabad India Nitra Technical Campus Department Of CSE-AIML UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Gla University Department Of Computer Engineering And Applications UP Mathura India Graphic Era Deemed To Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

Model selection is of utmost significance for accurate analysis and treatment of pulmonary effusion. With the increasing complexity of scientific statistics and the advancement of prediction analysis algorithms in the clinical field, model selection has emerge as a critical step in a hit prediction evaluation. In this text, we have a look at the effect of version choice on pulmonary effusion prognosis the use of a hard and fast of prediction evaluation algorithms. We compare the overall performance of different algorithms in phrases of accuracy, kind-I and sort-II errors, and interpretability. We demonstrate that version selection can have an effect on the accuracy and interpretability of the diagnosis and for this reason the remedy plan installed for the patient. We in addition talk the consequences for practitioners, clinicians, and researchers, who have to don't forget the deserves of model choice whilst the use of prediction analysis algorithms for the analysis of pulmonary effusion. Version choice is a crucial a part of the predictive evaluation pipeline, as the selection of version for given software may have a large effect on accuracy and performance. In the context of pulmonary effusion (PE) analysis, an appropriate choice of a predictive evaluation set of rules can enhance the detection of PE abnormalities and facilitate scientific decision making. Numerous algorithms were carried out to broaden models for PE prognosis, which includes synthetic neural networks, help vector machines, logistic regression, and decision trees. © 2024 IEEE.

关键词： Forecasting

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Custom Transformer-Based Approach for Enhanced Bengali Automatic Speech Recognition 1

Custom Transformer-Based Approach for Enhanced Bengali Autom...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Surendran, R. SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Anna University St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Automated speech recognition (ASR) systems struggle with Bengali, which is the fifth most spoken language. Bengali has a varied morphology, many dialects, and limited high-quality annotated voice data. Traditional voice recognition techniques work well however struggle with Bengali phonetics and syntax. This method eliminates the need for large amounts of tagged data and the difficulties of differentiating sounds in different languages, both issues are addressed by this procedure. The research in this paper offers an Intellectual Bengali Speech Recognition System using Deep Learning Techniques (IBSRS-DLT). The model in this system uses transformer architecture and attention processes to manage Bengali speech pattern long-range dependencies. The IBSRS-DLT outperforms standard models in recognition accuracy using a pre-trained transformer model modified for phonetic and lexical correctness and fine-tuned on Bengali datasets. After training on a vast Bengali speech corpus, real-world datasets and rigorous simulations confirm the system. IBSRS-DLT can be used for automatic transcription, voice-controlled devices, and Bengali virtual assistants, such applications are efficient and accessible. The simulation showed a considerable boost in recognition speed and a decrease in keyword mistakes compared to current methods. This paper shows that deep learning can solve linguistic issues in ASR systems, enabling scalable regional language detection. © 2024 IEEE.

关键词： Deep learning

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An Intellectual Diffused Configuration for High-Level Edge Network Elasticity 3

An Intellectual Diffused Configuration for High-Level Edge N...

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

作者： Karthigeyan, A. Sathish Kumar, M. Ali Sabah, Hawraa Fouad, Laith Abbas Alwan, Adil Fouad, Laith Hindustan Institute of Technology & Science Department of Computer Applications Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Chennai India National University of Science and Technology Collage of Engineering Dhi Qar Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad Iraq

ISBN: (纸本)9798350399264

The Network Distribution System is a rapid evolution of the Internet of Everything (IOE). This system enabled the deployment of new solutions that reduce low-latency edge applications close to cloud servers and resilient edge applications to edge networks close to IoT devices. The network edge must make intelligent decisions that require fast response times and a wide range of edge application requirements. It also requires a heterogeneous dynamic edge network with constrained resources. Increasing the edge network capacity level increases the frequency and latency of many edge devices such as smart cars, robots, and smart IoT devices. With dynamic edge response time, applications automatically adapt to resilience requirements. This paper describes the role of edge networks. It also employs a lightweight, self-adapting, decentralised mechanism for deploying edge applications. It explains optimization methods for edge applications based on IoT technology. Finally, this paper increases the performance of smart waste management system by using the elasticity by reducing the latency of edge network. © 2023 IEEE.

关键词： Elasticity

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Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12895-12920页

作者： Saif, Mufeed Ahmed Naji Niranjan, S.K. Murshed, Belal Abdullah Hezam Al-ariki, Hasib Daowd Esmail Abdulwahab, Hudhaifa Mohammed Department of Computer Applications Sri Jayachamarajendra College of Engineering VTU Mysore India Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science College of Eng. &IT University of Amran Amran Yemen Department of Information Technology Faculty of Engineering and Information Technology Al-ata’a University for Science and Technology Taiz Yemen Department of Computer Networks Engineering and Technologies Sana’a Community College Sana’a Yemen Department of Computer Application Ramaiah Institute of Technology VTU Bangalore India

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare

关键词： Quality of service

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Analysing of diverse Stress pattern in ECG using Machine Learning Techniques 2

Analysing of diverse Stress pattern in ECG using Machine Lea...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Anbarasi, S. Dhanalakshmi, B. Department of Computer Applications BS Abdur Rahman Crescent Institute of Science and Technology GST Road vandalur Tamil Nadu Chennai India Department of Computer Science and Engineering BS Abdur Rahman Crescent Institute of Science and Technology GST Road vandalur Tamil Nadu Chennai India

ISBN: (数字)9781837242535

ISBN: (纸本)9781837242672

ECG signals are commonly used to diagnose various cardiac conditions, and the classification of these signals is an important task in Stress identification. With the advancement of ML techniques, several ML models have been developed to classify ECG signals with high accuracy. This paper aims to review recent studies that use ML models for ECG classification, equivalence to the working nature of different prototype, and discuss the challenges and limitations of these models. The study found that deep learning models, such as CNNs and RNNs, outperformed traditional ML models in terms of closeness and validity. The results also showed that the combination of multiple models could improve the classification performance. However, the study also highlighted the need for large annotated ECG datasets and the requirement of domain expertise for feature engineering. Overall, this review provides a complete analysis of the current state of ECG classification using ML models and identifies areas for future research and improvement. Even though the accuracy of ML models in ECG classification is not always perfect, they can still provide valuable information and support to healthcare professionals. In some cases, even a lower accuracy can still be useful, especially in the context of large-scale screening and monitoring programs, where the goal is to identify individuals who require further evaluation. In these cases, the high computational speed and scalability of ML models make them an attractive option, as they can quickly process large amounts of data and provide preliminary results that can be followed up with more comprehensive and accurate tests. Additionally, the results from ML models can also be used as a starting point for further analysis by healthcare professionals, who can use their domain knowledge to interpret the results and make informed decisions. Therefore, despite their limitations, ML models can still play an vital role in the field of Electrocardiogram cla

关键词： Convolutional neural networks

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