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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Ida Seraphim, B. Nagarajan, Esha Pathak, Shatakshi Srm Institute Of Science And Technology School Of Computing Dept. Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350381665

Air pollution is a rising concern, particularly in urban areas like Delhi, India, where the Air Quality Index (AQI) serves as a vital measure of environmental health. In the ongoing pursuit of refining air quality prediction, we are embarking on an innovative approach to predict Air Quality Index, integrating the power of ensemble learning. Unlike traditional single-model approaches, our work centers on the utilization of a Meta Classifier, a sophisticated ensemble technique that amalgamates the unique predictive abilities of each constituent model, synthesizing their outputs to achieve a superior performance. We employ a comprehensive methodology that includes data pre-processing, feature engineering, model training, and evaluation. The findings demonstrate a high degree of accuracy and reliability in forecasting AQI categories. This cutting-edge approach involves harnessing the collective intelligence of multiple machine learning models to enhance prediction accuracy and reliability. This research aims to contribute to improving air quality monitoring, offering valuable insights for environmental and public health management. © 2024 IEEE.

关键词： Time series analysis

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Detection of Alzheimer’s Disease from Brain MRI Images Using Convolutional Neural Network 6th

Detection of Alzheimer’s Disease from Brain MRI Images Usin...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Santosh, Nomula Khan, Patan Imran Saranya, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819774227

Alzheimer's disease (AD) ranks as the predominant factor leading to the onset of dementia. AD leads to a steady decline in memory, reasoning, behavior, and social abilities. These changes affect a person's personality. Alzheimer's disease is most common in older adults. Conventional diagnostics methods for Alzheimer's disease face challenges like invasiveness, cost, imprecision, and patient discomfort. The aim is to develop an innovative AI model offering a non-invasive, cost-effective solution. In this suggested system, we use a convolutional neural network to classify AD into four classes: Normal Phase, First Phase, Second Phase, and Third Phase. The dataset size is 6000 MRI (Magnetic Resonance Imaging) images. AD causes the brain regions to shrink, and connections between the network of neurons may break down. Hence, accurate and timely Alzheimer's disease classification is crucial for effective treatment and better patient outcomes. The primary goal of the suggested model is to classify AD with greater accuracy using a deep learning model. The work follows several preprocessing steps for the input images, such as image resizing, image pixel resizing, and batching images to enhance the input features, and for disease classification, the Convolutional Neural Network model is used. The suggested model was evaluated using Kaggle dataset images, and a maximum accuracy of 97.25% was obtained using the Kaggle dataset. This research addresses the critical need for precise AD classification, potentially revolutionizing patient outcomes through early detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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On the Security of Smart Home Systems:A Survey

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Journal of Computer science & technology 2023年第2期38卷 228-247页

作者：袁斌万俊吴宇晗邹德清金海 School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Distributed System Security Huazhong University of Science and Technology Wuhan 430074China Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan 430074China National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Lab Huazhong University of Science and TechnologyWuhan 430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518057China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Among the plethora of IoT(Internet of Things)applications,the smart home is one of the ***,the rapid development of the smart home has also made smart home systems a target for ***,researchers have made many efforts to investigate and enhance the security of smart home *** a more secure smart home ecosystem,we present a detailed literature review on the security of smart home ***,we categorize smart home systems’security issues into the platform,device,and communication *** exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security ***,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.

关键词： IoT(Internet of Things) smart home IoT security smart home security

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An EETR Approach for Therapeutic Response Prediction Using Gene Expression and Drug Properties 3rd

An EETR Approach for Therapeutic Response Prediction Using G...

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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Selvi Rajendran, P. Jawahar, Janiel Computer Science and Engineering School of Computing Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819726707

In recent years, the role of computational methods such as machine learning and deep learning has evolved to help better understand an individual’s response to drugs. Through advancements in the discipline of precision medicine, cancer therapies are made based on an individual's pharmacogenomics data. In this way, doctors can make informed decisions about patient outcomes and reduce treatment costs. Beyond these advancements, there is enough void to precisely predict drug response. A drug’s response to therapy is dependent on a number of factors such as the drug’s physicochemical properties, pharmacokinetics, metabolism, protein, mutation, somatic variation, environment, and much more. Studying the correlation of more of these factors and their association with the drug is one of the promising ways to bridge the gap in improving results. Extra tree regression is an ensemble-based algorithm and is less prone to overfitting than other ensemble learning algorithms particularly when low bias and reduced overfitting are desired, and computational efficiency is important. In this paper, we have applied the drug’s physicochemical properties to predict the Inhibition values using an enhanced extra tree regression (EETR) algorithm. The EETR algorithm prediction outperformed both the random forest and original ETR algorithm in terms of both MSE and R2 score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Effective Scene Graph Generation by Statistical Relation Distillation

Effective Scene Graph Generation by Statistical Relation Dis...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Nguyen, Thanh-Son Yang, Hong Fernando, Basura Institute of High Performance Computing Agency for Science Technology and Research Singapore Agency for Science Technology and Research Singapore

ISBN: (纸本)9798331510831

Annotating scene graphs for images is a time-consuming task, resulting in many instances of missing relations within existing datasets. In this paper, we introduce the Statistical Relation Distillation (SRD) method to enhance scene graph datasets. SRD leverages human-annotated relations alongside object-to-object and predicate-to-predicate similarities to reinforce the existence likelihood of scene graph relations. Moreover, SRD can augment relational frequency using relations of non-selected object and predicate categories that are usually omitted by scene graph generation (SGG) task. The output from SRD derives the prior probability which is combined with model-predicted probabilities to annotate missing relations in training images and subsequently re-train SGG models on the augmented dataset. We evaluate our proposed method on Visual Genome and GQA-200 datasets. Experimental results show that training on the augmented dataset enhances the performance of prominent scene-graph generation models. The implementation code is at https://***/LUNAProject22/SRD. © 2025 IEEE.

关键词： Visualization

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Modified Neural Network Used for Host Utilization Predication in Cloud computing Environment

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Computers, Materials & Continua 2025年第3期82卷 5185-5204页

作者： Arif Ullah Siti Fatimah Abdul Razak Sumendra Yogarayan Md Shohel Sayeed Centre for Intelligent Cloud Computing Multimedia UniversityMelaka75450Malaysia Faculty of Information Science and Technology Multimedia UniversityMelaka75450Malaysia

Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client *** essential aspect of cloud computing that improves resource allocation techniques is host load *** difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response *** solve this issue and accurately predict cloud capacity,cloud data centers use prediction *** permits dynamic cloud scalability while maintaining superior service *** host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this ***,the suggested hybrid model is input is subjected to the vector auto regression *** data in many variables that,prior to analysis,has been filtered to eliminate linear *** that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing *** next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series *** key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant *** systems require accurate prediction due to the increasing degrees of unpredictability in data *** of this,two actual load traces were used in this study’s assessment of the *** example of the load trace is in the typical dispersed *** comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.

关键词： Cloud computing datacenter virtual machine(VM) predication algorithm

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Deep Learning-Based Productivity Analysis for Oryza sativa with Decision Support System 3rd

Deep Learning-Based Productivity Analysis for Oryza sativa w...

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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Soren, Nikita Selvi Rajendran, P. School of Computing Science Hindustan Institute of Technology and Science Tamil Nadu Kelambakkam India

ISBN: (纸本)9789819726707

Out of the executive collection of crop diversity, Oryza sativa is one of the vital staples in our daily life. The plant O. sativa, better known as rice, is crucial to human society, culture, and nourishment, and it plays a key role in our daily life. O. sativa's (rice's) growth is intricately linked to the interaction of three important environmental elements: soil, rainfall, and temperature. These elements work together to provide the circumstances required for productive rice farming, underscoring the critical role they play in the development of this crucial crop. Farmers grow O. sativa in millions of hectares throughout the region, and many landless workers derive income from working on these farms. Soil, rainfall, and temperature play an important role for farmers in the proper growth of O. sativa. An ambient intelligent technology is introduced for agriculture where deep learning is used to classify the diseased leaves and then the loop will be executed to check the cause of the disease and fluctuation the fertilizer dose accordingly. We have used Raspberry Pi3 to monitor the soil variation and to check the disease and pest moments YOLO v3 algorithm is implemented along with the hue saturation value with radial basis function network for soil variation and fertilizer dose check. In order to give farmers, agronomists, and other stakeholder’s timely and accurate information for making informed decisions about crop management, resource allocation, risk assessment, and overall farm productivity, it combines a variety of data sources, analytical models, and computational algorithms. We have implemented ambient intelligence and analyzed the correlation or O. sativa with soil, humidity, temperature, and pH. Value is to understand the growth and disease of the crop in the research-related area. With the AgriDSS, we have received the accuracy of 99. 20% at 50 epochs with the loss variation of 0.0601%. Our research will help the farmers to take the precautionary measur

关键词： Fertilizers

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Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals

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Computers, Materials & Continua 2023年第5期75卷 4101-4118页

作者： Srikanth Cherukuvada R.Kayalvizhi Department of Networking and Communications School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India

The term Epilepsy refers to a most commonly occurring brain disorder after a *** identification of incoming seizures significantly impacts the lives of people with *** detection of epileptic seizures(ES)has dramatically improved the life quality of the *** Electroencephalogram(EEG)related seizure detection mechanisms encountered several difficulties in *** EEGs are the non-stationary signal,and seizure patternswould changewith patients and recording ***,EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of *** intelligence(AI)methods in the domain of ES analysis use traditional deep learning(DL),and machine learning(ML)*** article introduces an Oppositional Aquila Optimizer-based Feature Selection with Deep Belief Network for Epileptic Seizure Detection(OAOFS-DBNECD)technique using EEG *** primary aim of the presented OAOFS-DBNECD system is to categorize and classify the presence of *** suggested OAOFS-DBNECD technique transforms the EEG signals *** format at the initial ***,the OAOFS technique selects an optimal subset of features using the preprocessed *** seizure classification,the presented OAOFS-DBNECD technique applies Artificial Ecosystem Optimizer(AEO)with a deep belief network(DBN)*** extensive range of simulations was performed on the benchmark dataset to ensure the enhanced performance of the presented OAOFS-DBNECD *** comparison study shows the significant outcomes of the OAOFS-DBNECD approach over other *** addition,the result of the suggested approach has been evaluated using the CHB-MIT database,and the findings demonstrate accuracy of 97.81%.These findings confirmed the best seizure categorization accuracy on the EEG data considered.

关键词： Seizure detection EEG signals machine learning deep learning feature selection

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Enhancing Efficiency of Wind Farm Energy using Digital Twin technology 15

Enhancing Efficiency of Wind Farm Energy using Digital Twin ...

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

作者： Daniel, Jesline Kulkarni, Harshit Ravindra Mahajan, Sanchi Bhardwaj, Sugam Rahman, Habibur Agrawal, Shikhar SRM Institute of Science and Technology School of Computing Dept. of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350370249

Using digital twin technology, the projectpresents a novel technique for wind farm monitoring and maintenance. This technique seeks to revolutionize wind farm management by assuring optimal performance, efficiency, and lifespan. The use of digital twin technology provides a virtual duplicate of the complete wind farm that isconstantly updated with real-Time data from sensors located around the facility. This allows for a highly accurate depiction of physical assets and their operating condition, supporting real-Time performance evaluation, predictivemaintenance, problem detection, energy optimization, and environmental impact reduction. The digital twin provides instantaneous performance evaluations by using sensor data, detecting underperforming turbines and forecasting possible breakdowns. It also plans maintenance tasks based on historical and real-Time data, reducing downtime and operational costs while increasing dependability and safety. The digital twin improves defect detection by leveraging modern analytics and machine learning, hence increasing theoverall dependability of the wind farm. © 2024 IEEE.

关键词： Predictive maintenance

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An Optimised Defensive Technique to Recognize Adversarial Iris Images Using Curvelet Transform

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Intelligent Automation & Soft computing 2023年第1期35卷 627-643页

作者： K.Meenakshi G.Maragatham School of Computing Department of Networking and CommunicationsSRM Institute of Science and TechnologyKattankulathurChennai603203India School of Computing Department of Computational IntelligenceSRM Institute of Science and TechnologyKattankulathurChennai603203India

Deep Learning is one of the most popular computer science techniques,with applications in natural language processing,image processing,pattern iden-tification,and various *** the success of these deep learning algorithms in multiple scenarios,such as spam detection,malware detection,object detection and tracking,face recognition,and automatic driving,these algo-rithms and their associated training data are rather vulnerable to numerous security *** threats ultimately result in significant performance ***,the supervised based learning models are affected by manipulated data known as adversarial examples,which are images with a particular level of noise that is invisible to *** inputs are introduced to purposefully confuse a neural network,restricting its use in sensitive application areas such as bio-metrics *** this paper,an optimized defending approach is proposed to recognize the adversarial iris examples *** Curvelet Transform Denoising method is used in this defense strategy,which examines every sub-band of the adversarial images and reproduces the image that has been changed by the *** salient iris features are retrieved from the reconstructed iris image by using a pretrained Convolutional Neural Network model(VGG 16)followed by Multiclass *** classification is performed by using Support Vector Machine(SVM)which uses Particle Swarm Optimization method(PSO-SVM).The proposed system is tested when classifying the adversarial iris images affected by various adversarial attacks such as FGSM,iGSM,and Deep-fool *** experimental result on benchmark iris dataset,namely IITD,produces excellent outcomes with the highest accuracy of 95.8%on average.

关键词： Adversarial attacks biometrics curvelet transform CNN particle swarm optimization adversarial iris recognition

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