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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kamalakkannan, S. Yasmin, A. Arunkumar, R. Kavitha, P. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Information Technology Chennai Pallavaram India Jbas College for Women Department of Computer Science Chennai India University of South Wales Faculty of Computing Engineering and Science Treforest/Newport Campus United Kingdom School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai Pallavaram India

ISBN: (纸本)9798350300857

Stock market investments are highly rewarding but also high in risk. Modern investors use variety of tools to take informed investment decisions. In the current era of digital world, financial service industry has generated huge volume and immense verities of data with extreme speed. Due to the rapid growth in data collection and the heterogeneous nature and complexity of the data, there is a need for Big Data analytical solution that would be able to deal with the stock market data. Large volumes of unstructured, heterogeneous raw data can be stored in a massively scalable manner using data lakes, which are the ideal solution to the big data storage conundrum. The ability of a data lake to preserve data in its original format while processing it at runtime using a schema on-read technique is its key feature. The challenge faced in the data lake is performing analytics which is a significant tool to calculate and analyze the stock market. The proposed architecture of Azure Databricks DeltaLake (ADDL) with Azure DataLake Storage Generation 2 (ADLSG2) is used for analytical processes like Fibonacci retracement for better stock analysis, which aid in forecasting the market price for better investment. As a result, the research focus is to produce a storage having read as well as write capabilities by taking into consideration the Extract-Load-Transform (ELT) operation on the datasource. In this experimental databricks implementation, runtime is performed using open source of Apache Spark API and a highly improved execution engine, which results in a significant performance improvement when comparing to the standard source of Apache Spark available on the ADLS platform. Additionally, the Fibonacci retracement level calculation is achieved with the analytics and forecasting of test close price with various ML and DL techniques such as KNN, LSTM are compared with original price of the test data for better prediction of forecast close price. © 2023 IEEE.

关键词： Big data

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Assessing Insider Threats in Ai-Enhanced Cyber Security for Digital Medical Platforms

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SSRN 2025年

作者： Manasa, R. Jayanthiladevi, A. Rasmi, A. Murugan, Sivaram Faculty of Computer Applications Marwadi University Gujarat Rajkot India Marwadi University Gujarat Rajkot India Information Science and Engineering RR Institute of Technology Bangalore India Department of Computer Engineering Faculty of Engineering and Natural Sciences Sivas University of Science and Technology Sivas58000 Turkey

technology-fuelled medical premises equipped using AI-promoted suggestions have slowly changed clinical touch through movement-based examination, clinician improvement, and practical efficiency, and they bring all around land mega one-of-a-kind cyber observation, particularly inward threats due to ambitions or inadvertent leads individuals as getting jumbo access needed. Patient privacy, patient record integrity, and system utility can all be compromised by insider threats, which are issues created by people with some knowledge or access to the system. We outline aspects of insider threats, risk factors, and automation defenses against insider risks in AI-augmented cyber, defined within digital healthcare systems, focusing on the interplay between human and AI systems. © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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Source redundancy management and host intrusion detection in wireless sensor networks

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Recent Advances in computer Science and Communications 2021年第1期14卷 43-47页

作者： Singh, Vijander Sharma, Gourav Poonia, Ramesh C. Trivedi, Narendra K. Raja, Linesh Department of Computer Science & Engineering Manipal University Jaipur India Amity Institute of Information Technology Amity University Jaipur Rajasthan India Department of Computer Applications Faculty of Science Manipal University Jaipur Rajasthan India

Background: Intrusion Detection System (IDS) is a Software application which gives the facility to monitor the traffic of network, event or activities on network and finds out any malicious operation if present. Objective: In this paper, a new protocol was developed that can detect the Wireless Network Attack based on the reference of TCP/IP Model. In the proposed system the new feature is integrated in the IDS which are built in the router itself. Methods: If any intruder tries to connect with router, intruder has to authenticate himself/herself. To find the authentication key the intruder attacks on the router to matches the authentication key with the key which he/she has. The intruder has a file with the multiple different keys in it and with that file intruder applies a brute-force attack on the router, the brute-force checks every key of the file by applying them on the router when a key matches with the authenticated key the brute-force software inform the intruder about the key matching. The IDS of the router will checks the rapid tries arriving from the same MAC address, if any MAC address tries the false key many of time than the IDS will identify the MAC as intruder and inform the system administrator about the intrusion by popping up a message on the system of the administrator. Results: Simulation of the two different scenarios is done by using the Network simulator (NS 2) and NAM (Network animator). In scenario 1 the node 1 is intruder and the IDS protocols have figure it out. The intruder is labeled as 2. In scenario 2 node 1 is the sentinel node and it gets connected to router after authentication. Conclusion: The mechanism can detect a false node in the network which is major threat in WSNs. Result has been evaluated the performance of IDS protocol by using Ad-hoc On Demand Distance Vector (AODV) Routing Protocol for routing. © 2021 Bentham Science Publishers.

关键词： Intrusion detection

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A Deep Convolution Network-Based Pneumonia Identification from Thoracic X-Ray Imagery Scans 6th

A Deep Convolution Network-Based Pneumonia Identification fr...

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6th World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2022

作者： Upreti, Kamal Mishra, Prateet Maheshwari, Manish Vats, Prashant Dhankar, Aisha Batra, Reenu Yadav, Saneh Lata Chawla, Tanvi Bhardwaj, Jayant Department of Computer Science and Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management Delhi New Delhi India Department of Computer Science and Information Technology Symbiosis University of Applied Sciences MP Indore India Department of Computer Science and Applications Makhanlal Chaturvedi National University of Journalism and Communication MCNUJC Madhya Pradesh Bhopal India Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India Department of Electronics and Communication Bhagwan Parshuram Institute of Technology GGSIPU New Delhi India

ISBN: (纸本)9789811976599

Pneumonia is an infectious ailment which affects the respiratory system and is caused by microbes that infect the human by affecting their lung air sacs and fill them with fluid. Using chest X-ray is the most popular approach for detecting pneumonia, and the results must be calculated and examined by medical experts. The inconvenient way of detecting pneumonia results in a loss of human life owing to incorrect approach and treatment. With the advancement of computer and information technology, developing an autonomous system that can help in the diagnosis and prevention of pneumonia is now viable, especially if the patient is in a place where the medical facilities and expert doctors are limited. In order to solve this challenge, this project uses deep learning technologies. The neural network was customized to accomplish the difficult task of detecting diseases such as pneumonia in order to aid medical experts in their diagnosis and treatment options. CNN applies a filter to a photo in order to extract data from a chest X-ray. The above-mentioned goal was achieved using Python programming language, as well as deep learning and neural network methodologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chest X-rays Deep CNN Recognition of pneumonia Rectified linear function Thoracic X-ray component

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SCSF: Supply Chain Sustainability Framework by Bayesian Theory and Markov Model for Risk Analysis 9th

SCSF: Supply Chain Sustainability Framework by Bayesian Theo...

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9th International Conference on Frontiers in Intelligent Computing: Theory and applications, FICTA 2021

作者： Mukherjee, Pratyusa Patra, Sudhansu Shekhar Samantaray, Swati Barik, Lalbihari Barik, Rabindra Kumar School of Computer Engineering KIIT Deemed To Be University Bhubaneswar India School of Computer Applications KIIT Deemed To Be University Bhubaneswar India School of Humanities KIIT Deemed To Be University Bhubaneswar India Department of Information Systems Faculty of Computing & Information Technology in Rabigh King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9789811666155

Industry 4.0 is a predominantly used terminology to denote the current fourth Industrial Revolution in progress. The use of new technology such as IoT, cloud computing, robotic additive manufacturing and artificial intelligence, and how industry and other working areas are performed facilitates this high-tech metamorphosis. Simultaneously accelerating the changes exponentially, the current technological changeover egresses sustainability as one of the major issues in the domestic and international markets. Sustainability strategies should consider the ambivalence level and the future risks that organizational decisions may impose on the natural and social environments and the obligatory investment costs to make the supply chains more sustainable. The system's performance may be affected by the ripple effect caused by supplier base disruption propagating en route for the supply chain. Although the vulnerability assessments in single-echelon-single-event disruptions are critical for many firms, nevertheless the ripple effect modelling for multi-echelon-correlated events is becoming important. In this paper, two models have been discussed Markov Chain in conjunction with Bayesian Network to quantify the ripple effect. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Supply chains

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Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

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computers, Materials & Continua 2022年第1期70卷 1247-1261页

作者： J.V.Anchitaalagammai T.Jayasankar P.Selvaraj Mohamed Yacin Sikkandar M.Zakarya Mohamed Elhoseny K.Shankar Department of Computer Science and Engineering Velammal College of Engineering and TechnologyMadurai625009India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Information Technology SRMInstitute of Science and TechnologyKattankulathur603203Tamil NaduIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityP.O.Box 9004Abha61413Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt College of Computer Information Technology American University in the EmiratesDubai503000United Arab Emirates Department of Computer Applications Alagappa UniversityKaraikudiIndia

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern *** generally refers to a network of gadgets linked via wireless network and communicates via *** management,especially energy management,is a critical issue when designing IoT *** studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT *** this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT *** proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance *** proposed EECBRMmodel was validated through a series of experiments and the results were verified under several *** model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network *** simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant ***,the efficiency of the proposed model is established.

关键词： IoT environment clustering routing resource management energy efficiency

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A novel hybrid algorithm for lucky imaging

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Research in Astronomy and Astrophysics 2021年第5期21卷 155-167页

作者： Jin-Liang Wang Bin-Hua Li Xi-Liang Zhang Faculty of information engineering and automation Kunming University of Science and TechnologyKunming 650500China Key Laboratory of Applications of Computer Technologies of the Yunnan Province Kunming University of Science and TechnologyKunming 650500China Yunnan Observatories Chinese Academy of SciencesKunming 650011China

Lucky imaging is a high-resolution astronomical image recovery technique with two classic implementation algorithms,*** selecting,shifting and adding in image space,and data selecting and image synthesizing in Fourier *** paper proposes a novel lucky imaging algorithm where with space-domain and frequency-domain selection rates as a link,the two classic algorithms are combined successfully,making each algorithm a proper subset of the novel hybrid *** results show that with the same experiment dataset and platform,the high-resolution image obtained by the proposed algorithm is superior to that obtained by the two classic *** paper also proposes a new lucky image selection and storage scheme,which can greatly save computer memory and enable lucky imaging algorithm to be implemented in a common desktop or laptop with small memory and to process astronomical images with more frames and larger *** addition,through simulation analysis,this paper discusses the binary star detection limits of the novel lucky imaging algorithm and traditional ones under different atmospheric conditions.

关键词： methods:data analysis techniques:high angular resolution techniques:image processing

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Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm

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Research on Biomedical Engineering 2023年第1期39卷 115-127页

作者： Awotunde, Joseph Bamidele Panigrahi, Ranjit Khandelwal, Bidita Garg, Amik Bhoi, Akash Kumar Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Majitar737136 India Department of General Medicine Sikkim Manipal Institute of Medical Sciences Sikkim Manipal University Sikkim Gangtok737102 India Sikkim Manipal University Sikkim Gangtok737102 India KIET Group of Institutions Delhi-NCR Ghaziabad201206 India eSupport for Research Sambalpur Burla768018 India

Purpose: One of the leading causes of death among women is breast cancer. However, it has been established that early diagnosis with accurate results can ensure the prolonged survival of patients even with the illness. Deep learning (DL) and expert systems have been proven beneficial and gaining popularity in breast cancer diagnosis because of their effective taxonomy and high diagnostic capability. Method: This paper proposes a DL-based breast cancer model empowered with a rule-based hybrid feature selection mechanism to remove irrelevant features, thus proving to be a catalyst for improving diagnostic accuracy. The DL-based enabled feature selection helps in key attributes that are relevant to the diagnosis of breast cancer. The model has been tested utilizing the well-known Wisconsin Breast Cancer Dataset (WBCD) and validated through performance measures such as accuracy, sensitivity, specificity, F-score, and ROC curves. Results: The experimental results revealed that the DL-based enabled with feature selection performed excellently when compared with existing models on breast cancer using the same dataset. The findings show a greater diagnostic accuracy of 99.5% and detect five insightful features with a significant clue for better diagnosis. Conclusion: The proposed model can predict the presence of breast cancer by identifying the most relevant features in the diagnosis of breast cancer. The system looks promising when compared to other existing models for breast cancer. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.

关键词： Diagnosis

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Optimizing Deep Learning Models through Spatial Attentive Architectural Enhancements and Binarization Techniques

Optimizing Deep Learning Models through Spatial Attentive Ar...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Bishnu Pada Saha Satya Ranjan Dash Mainak Bandyopadhyay Paola Barra Vinh Truong Hoang School of Computer Engineering KIIT D.U. Bhubaneswar India School of Computer Applications KIIT D.U. Bhubaneswar India Department of Science and Technology University of Naples “Parthenope” Italy Faculty of Information Technology HCMC Open University Vietnam

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Attention mechanisms in vision applications of the computer world dynamically adjust feature weights to amplify the model’s capability to perceive information. This adaptive recalibration improves the overall effectiveness of the model. Spatial attention has seen widespread use to boost the efficiency of convolutional neural networks by enhancing their ability to model spatial information. This approach significantly improves CNNs’ spatial representation capabilities. Along this line of research, a novel binarized-attention module is drafted by taking spatial attention into consideration. Binary neural networks, which significantly reduce storage and computational requirements, are a promising approach for deploying deep models on resource-constrained devices. This technique offers efficiency in scenarios with limited resources. In this paper, we hereby implemented our algorithm of binarizing the spatial attention module and obtained diverse findings, demonstrating significant improvements in accuracies with fewer classes and degradation of accuracies with higher classes. Various experimental findings reveal that our technique delivers better accuracy with dataset having 10 classes but low accuracy with the one having 100 classes. Additionally, architectural optimizations, including the adoption of deeper networks like ResNet50, contribute to better handling of complex data patterns. While binarization offers computational efficiency, it often results in a trade-off with accuracy. However, selective binarization of specific network layers can mitigate this issue, maintaining a balance between performance and resource utilization. This study provides insights into the synergistic effects of these techniques, offering a pathway for developing efficient and accurate deep learning models suitable for deployment in resource-constrained environments.

关键词： Deep learning Adaptation models Visualization Attention mechanisms Accuracy Computational modeling Convolutional neural networks Resource management Optimization Residual neural networks

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Analysis and Optimization of Neural Networks for Image and Speech Recognition

Analysis and Optimization of Neural Networks for Image and S...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Komala, C.R. Purushothaman, R. Ande, Pavan Kumar Dinesh, P.M. Anitha Rani, K.S. Yamuna, P. HKBK College of Engineering Department of Information Science and Engineering Karnataka Bengaluru560045 India Sri Ramachandra Institute of Higher Education and Research Faculty of Engineering and Technology Tamil Nadu Chennai600116 India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur522502 India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem636005 India REVA University School of Computer Science and Applications Karnataka Bengaluru560064 India Acharya Institute of Graduate Studies Department of Computer Applications Karnataka Soladevanahalli 560107 India

ISBN: (纸本)9798350319200

The strategy of transforming vocal discourse signals into text using records is known as programmed discourse acknowledgment (ASR). In the ongoing time of the PC unrest, the ASR has a huge impact in further developing the client experience while communicating with the PCs in a characteristic manner. Conventional instruments like a console and mouse can't be utilized, and the client might do a limitless number of undertakings like controlling of contraptions and correspondence with client care. This study presents an air terminal enquiry framework in view of ASR. The program was made solely for the Telugu language. In view of the most generally posed inquiries about air terminals, a data set was created. Convolutional Brain Organization (CNN) has been utilized for preparing and testing the information base in view of its extraordinary exhibition. Convolution brain organizations (CNNs) have as of late been demonstrated to perform better on the telephone discovery test than regular completely associated profound brain networks when utilized in a half and half profound brain organization/stowed away Markov model (DNN/Gee) design. © 2023 IEEE.

关键词： Image Networks Neural Optimization Speech Recognition

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