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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Vijiyakumar, K. Muthunagai, R. Bhanusree, A. Nikhilsowrabh, K. School of Computing Srm Institute of Science and Technology Department of Computing Technology Chenngalpet district Kattankulathur India Manakula Vinayagar Institute of Technology Department of Electrical & Electronics Engineering Puducherry India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331510022

Automated detection of defects in medical imaging has emerged as a crucial domain within various diagnostic applications, especially with MRI regarding tumor detection. 'While accuracy in the detection of tumors is important in providing vital information regarding tissues which have deviated from their normal state, it's even more important for effective treatment planning'. Currently, the diagnosis of tumors from brain MRI scans relies on human assessment, which is infeasible for large image data and error-prone based on judgments of humans. Thus, there is a pressing need for robust and efficient automated classification systems that can overcome these bottlenecks. The core idea of our project is the development of the most advanced tumor detection methods, which saves precious time for radiologists but at a very high accuracy and, thus, reduces mortality levels in patients. In our approach, we have used several CNNs - VGG19, ResNet50, and MobileNetV2, which we have applied through transfer learning for detecting tumors from brain MRIs. We also incorporate into our model an ensemble of these varied CNN architectures that leverages their strengths since the nature and diversity of tumors are intricate. In fact, the ensemble method is really intended to boost the wide-ranging accuracy and strength of the procedure in the detection of tumors. The proposed system utilizes deep learning techniques in combination with image classification to accurately and efficiently identify cancerous cells in brain MRI scans. After that, the web application, developed using Flask, will provide a user-friendly interface for uploading MRI images and then instantaneously return predictions to a doctor on the potential presence of a tumor. © 2024 IEEE.

关键词： Convolutional Neural Networks (CNNs) Deep Learning EnsembleMethod Flask Web Application MRI Imaging TransferLearning Tumor Detection

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Leveraging Blockchain with Optimal Deep Learning-Based Drug Supply Chain Management for Pharmaceutical Industries

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Computers, Materials & Continua 2023年第11期77卷 2341-2357页

作者： Shanthi Perumalsamy Venkatesh Kaliyamurthy Department of Networking and Communications School of ComputingSRM Institute of Science and TechnologyKattankulathurTamil Nadu603203India

Due to its complexity and involvement of numerous stakeholders,the pharmaceutical supply chain presents many challenges that companies must overcome to deliver necessary medications to patients *** pharmaceutical supply chain poses different challenging issues,encompasses supply chain visibility,cold-chain shipping,drug counterfeiting,and rising prescription drug prices,which can considerably surge out-of-pocket patient ***(BC)offers the technical base for such a scheme,as it could track legitimate drugs and avoid fake *** designers presented the procedure of BC with fabric for creating a secured drug supplychain management(DSCM)*** this motivation,the study presents a new blockchain with optimal deep learning-enabled DSCM and recommendation scheme(BCODL-DSCMRS)for Pharmaceutical ***,Hyperledger fabric is used for DSC management,enabling effective tracking processes in the smart pharmaceutical *** addition,a hybrid deep belief network(HDBN)model is used to suggest the best or top-rated medicines to healthcare providers and *** spotted hyena optimizer(SHO)algorithm is used to optimize the performance of the HDBN *** design of the HSO algorithm for tuning the HDBN model demonstrates the novelty of the *** presented model is tested on the UCI repository’s open-access drug reviews database.

关键词： Drug supply chain pharmaceutical industry deep learning blockchain hyper ledger fabric security drug recommendation

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IoT and CNN Based Fire Detection And Prevention

IoT and CNN Based Fire Detection And Prevention

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2024 IEEE International Conference on Intelligent computing and Emerging Communication technologies, ICEC 2024

作者： Kavyasri, V.M. Krishnan Sudhakar, Nakul Malleswari, Tyj Naga Raghavendra, Sreesh Sundareshwaran, R.Kavin Venkat Phaneesh, Y. SRM Institute of Science and Technology Department of Networking and Communications India SRM Institute of Science and Technology Department of Computing Technologies India

ISBN: (纸本)9798331508432

Fire incidents pose significant risks to life and property, necessitating the development of advanced fire detection and suppression systems. This paper presents an innovative approach that combines a Convolutional Neural Network (CNN)- based image processing model with hardware integration for real- time fire detection and immediate response actions. The model is based on Yolo-v4 and techniques Cross validation and Grey Level Co-occurrence and median filtering are used for training of the model. The system utilizes a webcam for image capture, employing a CNN model trained on a large dataset to accurately identify fire conditions. Hardware components including temperature and humidity sensors (DHT11) and flame sensors complement the image processing for enhanced accuracy and reliability. The smoke sensors are usually used widely, but the flame sensors have a quicker response time. The proposed system demonstrates exceptional performance, achieving a detection accuracy 98%. Upon detecting fire, the system triggers an alarm through a buzzer for immediate evacuation alerts and activates a water motor for fire suppression. This integrated approach ensures rapid response to fire incidents, minimizing potential damages and enhancing overall safety. Key system features include scalability, adaptability, and user-friendliness, suitable for diverse environments like residential buildings, industries, and public spaces. Future upgrades might integrate advanced cameras, use machine learning for adaptive responses, and implement continuous monitoring for false positives, enhancing system capabilities. Overall, the developed system represents a significant advancement in leveraging technology for fire safety, with promising implications for mitigating fire-related risks effectively. © 2024 IEEE.

关键词： Median filters

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Longest Common Substring in Pattern Matching 8

Longest Common Substring in Pattern Matching

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Gupta, Maahin Senthil Kumar, K. Joshi, Ayush Srm Institute of Science and Technology Kattankulathur India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350376425

The longest common substring (LCS) identification has many applications in Pattern matching, Automata Theory, Bioinformatics, especially in DNA arrangement examination. The LCS issue looks for the longest shared substring over a given set of strings. We will look at strategies beginning with the conventional approach utilizing generalized addition trees increased with least common precursor inquiries, taken after by a novel strategy utilizing postfix trees combined with productive union-find information structures, advertising a direct linear-time calculation versatile to space-efficient compressed postfix tree representations. Furthermore, we will cover variations of these strategies pointed at lessening space utilization whereas keeping up competitive running times, dissecting how they use compressed records and union-find structures to possibly outflank prior arrangements in terms of time and space complexity. All through the assessment, we will consider each method utilize of headways in compressed content ordering methods, evaluating their execution, versatility, and versatility to large-scale genomic datasets where space productivity is vital. By comparing these approaches, we point to distinguish the most viable and effective arrangement for the LCS issue, highlighting the strategy that best equalizations execution, space effectiveness, and flexibility for different bioinformatics applications. © 2024 IEEE.

关键词： Pattern matching

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Mobile Health Service Adoption: Reducing the Digital Gap and Age’s Moderating Influence

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile technologies 2024年第17期18卷 139-153页

作者： Muruganantham, B. Karim, Hezerul Abdul Mansor, Sarina Binti Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Faculty of Engineering Multimedia University Cyberjaya Malaysia

In the past few years, mobile health services (MHS)—a growing topic in the healthcare sector—have received increasing attention. technologies related to mobile health, or m-health, have several advantages for people, businesses, and medical professionals. Indeed, using m-health can help older people become more proactive patients and lessen the strain on health systems and financial resources. However, the potentially revolutionary impact of m-health is constrained since a large number of senior citizens oppose its adoption, which creates an age-based digital divide. Few studies have examined how public consumers of MHS adopt MHS, even though prior study on this topic is very substantial. We discover that user attitudes towards MHS are positively impacted by perceived benefits and usefulness, perceived barriers, and external cues. Perceived service availability also has a major impact on perceived benefits and usefulness, which, when combined with attitude, directly increases intention. Additionally, we discover that the MHS’s utilization intent has moderating effects. The final section discusses the implications for mobile health marketing. The results indicate that perceived ease of use, vulnerability, and severity are more significant factors for middle-aged and older users to use MHS. The moderator analysis confirmed that different age groups have specific moderating effects on the adoption of mobile health services. © 2024 by the authors of this article.

关键词： Electronic health record

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A Neural Network-Based Approach to Forecasting Bankruptcy Using Feature Importance Analysis 3

A Neural Network-Based Approach to Forecasting Bankruptcy Us...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Aatmaja, Aditi Bhagwat, Dhruv Prakash, B. Nallarasan, V. SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India SRM Institute of Science and Technology School of Computing Department of Networking and Communications Chengalpattu Tamilnadu Kattankulathur India

ISBN: (纸本)9798350396157

This research work addresses the critical problem of developing a reliable bankruptcy prediction model. Accurately predicting bankruptcy is essential to the stability of the economy, and existing methods have limitations that may lead to business failure. In this research, we aim to overcome these limitations and develop a more accurate bankruptcy forecasting method. To do this, we discuss the issue of unequal class sizes and the relative importance of various forms of misclassification. After identifying the most important financial characteristics using feature importance analysis, we employ neural networks to predict if a company will fail in the next years. To improve the model's performance, we adjust the parameters using Grid Search Cross Validation. The accuracy of bankruptcy predictions might be greatly increased with our suggested methodology, which would also increase economic stability. © 2024 IEEE.

关键词： Forecasting

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Modified BIRCH and Hybrid Deep Framework for Tomato Leaf Disease Segmentation and Classification 5

Modified BIRCH and Hybrid Deep Framework for Tomato Leaf Dis...

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5th International Conference on Intelligent Communication technologies and Virtual Mobile Networks, ICICV 2024

作者： Jayanthi, V. Kanchana, M. School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai603 203 India

ISBN: (纸本)9798350385649

Identifying disease at an early stage contribute s to improving productivity and quality of crops. Farmer's misidentification can lead to ineffective treatment strategy, adversely impacting tomato plants and agriculture. In this work, a MBIRCH algorithm is introduced for segmenting process along with hybrid model for classification process. Preprocessing, segmentation, extraction of features and classification are the four processing steps for categorizing leaf diseases on tomato crop. The input image is primarily under preprocessing, median filter is utilized in this stage. Consequently, the obtained image after preprocessing is fed to segmentation stage, where MBIRCH framework is introduced for proper segmentation process. Then various features, including MBP, GLCM, and shape -based features, are extracted from the segmented outcome. Finally, the hybrid classification system, which is a combination of BiLSTM and CNN, receives the obtained features as input. The hybrid model classifies tomato leaf diseases by calculating the averag e output of two classifiers. The classified tomato leaf diseases include bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, two-spotted spider mite, target spot, tomat o mosaic viral, and tomato yellow leaf curl viral. © 2024 IEEE.

关键词： Median filters

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A Novel QIA Protocol Based on Bell States Position by Random Selection 1

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International Conference on Data Analytics and Management, ICDAM 2022

作者： Devendar Rao, B. Jayaraman, Ramkumar Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9789811976155

ISBN: (纸本)9789811976148

Identifying trusted user plays an important role before initiating secure communication;therefore, a quantum authentication schemes is proposed based on Bell pair. Two trusted party initial shared a common secret key (known as pre-shared key) which only known to them. Various existing protocol uses quantum resources with memory for Authentication process but the storage time of qubits in 3 ns. In proposed protocol, authentication process had done without strong the Bell states by trusted parties. Sending party selects 4 classical bits to form Bell pair in any one of the positions {(1, 2)(3, 4)} or {(1, 3)(2, 4)} based on the consecutive bits in pre-shared key. The receiving party uses the consecutive bits in pre-shared key to decode the information about authentication key using Bell state measurement. Adversary has no knowledge about pre-shared key and guessing incorrect position makes entangle swapping of Bell states. Adversary incorrect position leads to its identification by trusted parties by verifying the authentication key in classical channel. The security of proposed protocol is analyzed under intercept measure and resend attack (IR). In addition, proposed protocol can prevent the adversary to fetch the information about the pre-shared key. Various existing protocol is compared with the proposed one with quantum resources and memory requirement. The proposed protocol is implemented in IBM Quantum Lab, and circuit simulation is shown visually in IBM quantum composer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authentication

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Secured Data Cluster Distribution and Propagation using IBBE and Attribute-based CPRE 7

Secured Data Cluster Distribution and Propagation using IBBE...

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7th International Conference on computing Methodologies and Communication, ICCMC 2023

作者： Vinoth, N.A.S. Sindhuja, M. Maria Nancy, A. Renuka Devi, R. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9781665464086

Cloud services are one of the key factors in many real time applications. It involves huge amounts of data transfer and thereby data privacy and security are the major concern in cloud computing applications. To address this issue, Secured Data Cluster Distribution and Propagation using IBBE and Attribute-Based CPRE (SDCDP), a model has been proposed in this manuscript where the data is being shared with a lot of users in groups with proprietary distribution data in which the owners of the data can simultaneously transmit the data that is being encrypted to a recipient batch by specifying the recipient's identity like a secure and easy way. The encryption technique used is a proxy kind of encryption which is a conditional based on merit and release time to ensure that only data distributors have access to the data access policy and distribute to other groups after release by providing a rewrite key to the cloud server. Data owners will have complete and timed-release controls used over distributed cipher texts, which are generated by the encryption conditions associated with the attributes and release time. The integrity and adequacy of proposed approach was demonstrated through theoretical analysis and experimental results to test this exhibit at each stage. Theoretical analysis and experimental results to assess the exhibition at each stage to show the adequacy of the proposed plan. To maintain the guarantee of the private resource batch transfer, Identity-Based Broadcast Encryption (IBBE) process will be used to perform data transmission. The owners of the data can publish their data that is encrypted for batch when the particular time arrives and the key that is needed that is given to the person will get an email or number that is different from others and name for a particular user. Therefore, there is a need to prove that the owner of the resource will transfer resources with other batch people in an easy and secure way. © 2023 IEEE.

关键词： Data transfer

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A novel feature selection approach with integrated feature sensitivity and feature correlation for improved prediction of heart disease

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Journal of Ambient Intelligence and Humanized computing 2023年第9期14卷 12005-12019页

作者： Saranya, G. Pravin, A. Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai600119 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai603203 India

This paper presents a random forest-feature sensitivity and feature correlation (RF-FSFC) technique for enhanced heart disease prediction. The proposed methodology is implemented using the Cleveland heart disease dataset which comprises a total of 120 heart disease patient records. Data imputation was utilized for missing values, and min–max normalization was utilized for data transformation. We attempted to construct an RF-based classifier for coronary heart disease in this paper by combining feature sensitivity and correlation analysis. The sensitivity-based feature selection process ranks features according to their value in assessing CHD risk, and the feature correlation analysis phase analyses if there are any correlations between features. The heart disease prediction accuracy of 81.16% was obtained using the proposed RF-FSFCA technique by omitting five features (sex, hemoglobin, TD, CRF, and cirrhosis). When compared to the Naïve Bayes, decision tree, regression analysis, and support vector machine models, the proposed model offered a higher accuracy of 86.141% without omitting any features. It also offered sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) scores of 87.321%, 87.364%, 91.23, and 91.02 respectively. Experiment findings demonstrated that the proposed RF-FSFC approach significantly improves prediction accuracy as compared to other approaches that do not use the integrated Feature selection method. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sensitivity analysis

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