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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Priya, S. Anu bhat, Niyati Kanna, b. Rajesh Rajalakshmi, S. Jeyavathana, R. beaulah Srimathi, S. Department of Computer Applications Chennai600117 India University of Mumbai B.E Computer Engineering Mumbai India Alagappa Chettiyar College of Engineering and Technology Department of Electronics and Communication Engineering Karaikudi India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering India Srm Institute of Science and Technology Computational Intelligence Kattankulathur Chennai India Simats Saveetha School of Engineering Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350307757

Prob.ems have arisen in allocating and managing network resources effectively because of the proliferation of IoT gadgets. This study aims to provide a deep learning-based strategy for forecasting and analyzing IoT traffic patterns to solve this prob.em. The model uses recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) cells to capture the complex temporal dependencies in IoT data streams. Following careful data gathering from various IoT sources, a thorough preprocessing phase is performed to account for missing values and outliers. effective modeling is enab.ed by applying feature engineering approaches to distill meaningful information and by temporally aggregating the dataset. The heart of the study is the creation of a sequence-to-sequence architecture that allows for reliab.e forecasting of future IoT traffic trends. Mean Squared error (MSe) and other performance metrics measure the quality of the proposed model's predictions. In addition to its predictive abilities, the model's analytical section reveals interesting facts about the fundamental habits underpinning IoT traffic. This dual purpose helps network managers better comprehend the ever-changing IoT ecosystem and allocate scarce resources. The results of this study have important implications for network optimization, providing a preventative strategy for dealing with the difficulties posed by the exponential growth of IoT data traffic. Regarding accuracy and interpretability, the suggested deep learning model significantly contributes to the development of intelligent network resource management in the IoT era. © 2024 Ieee.

关键词： Time series analysis

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brain Tumor Detector Using SVM Algorithm

Brain Tumor Detector Using SVM Algorithm

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International Conference on Computational science and Technology (ICCST)

作者： Aravintharaj. S Mithileshan. M b. Priya B.E Computer Science Engineering B.E Computer Science Engineering Sri Sairam Engineering College Chennai Tamil Nadu Computer Science Department Sri Sairam Engineering College Chennai TamilNadu

ISBN: (纸本)9781665476560

In the healthcare sector, disease prediction must be extremely precise to allow for early, effective treatment. Our overall purpose is to use a machine learning model to predict brain tumour's at an early stage. The brain is the most crucial organ of the human body since it regulates the operation of all other organs and aids in decision-making. It is basically the central nervous system's command post and is in charge of carrying out the body's regular voluntary and involuntary functions. The tumour is an uncontrolled proliferation of undesirab.e tissue that has formed a fibrous mesh inside of our brain. The primary goal is to use this study to detect brain tumours at an initial stages. The study would provide a forecast indicating whether a tumour is present or not. This tool can be used as a significant asset to any medical facility dealing with brain Tumor. Using machine learning and deep learning algorithms enab.es the detection of brain tumour's. We use the algorithm Support Vector Machine (a supervised machine learning approach that can be utilized for both regression and classification prob.ems issues) out of all the machine learning algorithms since it has a greater precision and is simple to train on big datasets. In this study, the histogram of oriented gradient is used to collect the feature information from medical pictures of patients tumour's stored within the database. SVM is then used to separate these photos into tumour and non-tumor pictures. by using this method, it is possib.e to predict brain tumour's quickly and with a high degree of accuracy (95%), which aids in patient treatment.

关键词： Support vector machines Machine learning algorithms Databases Scientific computing Magnetic resonance imaging Predictive models Classification algorithms

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A Transmission and Transformation Fault Detection Algorithm based on Improved YOLOv5

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computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign ob.ects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separab.e convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-excitation Networks(SeNet)attention model is fused into the algorithm framework and a new detection scale suitab.e for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SeNet

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Identification of Therapeutic Plants Using Machine Learning

Identification of Therapeutic Plants Using Machine Learning

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emerging Trends in Information Technology and engineering (ic-eTITe), Conference on

作者： Rohini S Keerthana Shree M.U Jayalakshmi R Malathi M L. Jabasheela M.E Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India B.E Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Dept of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

It takes a lot of work to develop a computerized method for classifying plants for medicinal purposes. There are countless kinds of plants found in India, and each one has its own unique set oftherapeutic advantages. background information is necessary for manual identification and categorization because it is difficult for people to recall the proper titles of all plant species and their *** is crucial to preserve these medicinal plants since doing so will benefit several industries, including medicine, botanical research, and taxonomic studies, among others. Current technology cannot match thediversity of medicinal herb species that existin India. Utilizing textural traits that are crucial for recognizing and classifying leaves, the recommended technique makes itsimpler to categorize medicinal plants. The feature extraction, categorization, and picture enhancement stages are the three main components of the suggested method. The qualities that could be matched betweenthe leaf images were gathered using digital image processing methods after cell phones were used to snap the leaf photos. The CNN classifier is then used to create a machine learning classifier.

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An Intelligent Thyroid Diagnosis System Utilizing Multiple ensemb.e and explainab.e Algorithms with Medical Supported Attributes

IEEE Transactions on Artificial Intelligence

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Ieee Transactions on Artificial Intelligence 2024年第6期5卷 2840-2855页

作者： Sutradhar, Ananda Al Rafi, Mustahsin Ghosh, Pronab Shamrat, F M Javed Mehedi Moniruzzaman, Md. Ahmed, Kawsar Azad, A.K.M. bui, Francis M. Chen, Li Moni, Mohammad Ali Daffodil International University Department of Computer Science and Engineering Dhaka1216 Bangladesh Lakehead University Department of Computer Science Thunder BayONP7B 5E1 Canada University of Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Lakehead University Department of Software Engineering Thunder BayONP7B 5E1 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSK S7N 5A9 Canada Imam Mohammad Ibn Saud Islamic University Faculty of Science Department of Mathematics and Statistics Riyadh13318 Saudi Arabia Charles Stuart University AI and Cyber Futures Institute BathurstNSW2795 Australia

The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including feature importance (FIS), information gain selections (IGS), and least absolute shrinkage and selection operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as high-risk factors (HRF). Second, the models, including the three stage hybrid classifier (3SHC) and the three stage hybrid artificial neural network (3SHANN), are used as classifiers on the training data set. Third, a local interpretab.e model-agnostic explanations (LIMe) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a partial dependence plot (PDP). Finally, the proposed system is validated with extensive experiments, where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector. © 2020 Ieee.

关键词： Neural networks

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Unlocking efficiency in Agricultural Supply Chains: A Secure and Transparent Approach through blockchain Technology

Unlocking Efficiency in Agricultural Supply Chains: A Secure...

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2024 Ieee International Conference on emerging Technologies in computer science for Interdisciplinary Applications, ICeTCS 2024

作者： Lohith, J.J. Shreya, S. Hamsa Priya, J.L. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India D.S.C.E Dept. of Information Science and Engineering Bangalore India B.M.S. College of Engineering Dept. of Electronics and Telecommunications Bangalore India

ISBN: (纸本)9798350372502

Supply Chain Management (SCM) involves orchestrating various processes that extend from acquiring raw materials to delivering finished products to end consumers. The key ob.ectives of a proposed supply chain in agriculture, emphasizes information-driven processes, cost reduction, and customer impact. It creates a versatile system capab.e of managing the supply chain for diverse commodities within a country, while also addressing the crucial need for increased transparency and traceability in data throughout the chain of activities. In our proposed approach to agricultural supply chain management (SCM), the focus is on creating a secure, transparent, and traceab.e SCM. To achieve this, we leverage cutting-edge technologies like the ethereum blockchain and the Interplanetary File System (IPFS). The resulting software is a Decentralized Application (DApp) that orchestrates a series of activities within the agricultural industry, using cryptocurrencies to establish a seamless and efficient supply chain management system. This software platform is fortified by the secure algorithms of blockchain, providing an added layer of reliability. Additionally, our application incorporates transparency and traceability, ensuring user-friendly functionality and making it more accessib.e for users. © 2024 Ieee.

关键词： blockchain

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enhancing wireless sensor network longevity and security: A quad-leach approach

Enhancing wireless sensor network longevity and security: A ...

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2024 Ieee International Conference on emerging Technologies in computer science for Interdisciplinary Applications, ICeTCS 2024

作者： Lohith, J.J. Shreya, S. Hamsa Priya, J.L. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India D.S.C.E. Dept. of Information Science and Engineering Bangalore India B.M.S. College of Engineering Dept. of Electronics and Telecommunications Bangalore India

ISBN: (纸本)9798350372502

Researchers have increasingly focused on Wireless Sensor Networks (WSNs) in recent years, recognizing their versatile applications. The primary focus within WSNs is enhancing network longevity for efficient resource utilization, coupled with a strong emphasis on security and authentication. This paper specifically delves into the implementation of Quad LeACH for homogeneous networks, aiming to substantially improve network lifetime, stability, and throughput. The proposed protocol achieves this by employing a cluster head selection process and integrating security and authentication measures between cluster heads in the Quad-LeACH routing protocol. The research addresses the integration of a security agent within WSNs using the NS-2 tool, contributing to enhanced authentication. The implementation is carried out through the simulation software NS 2, where the security agent is seamlessly combined with the WSN infrastructure. The ob.ective of this research is to incorporate a reliab.e authentication and security agent into NS-2 in conjunction with the LeACH protocol, which is the standard for wireless sensors networks (WSNs). The efficacy of the suggested methodology will be validated through simulated outcomes. © 2024 Ieee.

关键词： Wireless sensor networks

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enhancing Credit Card Security: A Machine Learning Approach for Fraud Detection

Enhancing Credit Card Security: A Machine Learning Approach ...

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International Conference on Computing Communication Control and Automation (ICCUbeA)

作者： Komal Karkhile Sanskruti Raskar Rachana Patil Vaibhav bhangare Atharva Sarode B.E Computer Engineering Pimpri Chinchwad College of Engineering Pune India Pimpri Chinchwad College of Engineering Pune India

Transactions increase in frequency as the world swiftly adopts digitisation and a cashless economy. The use of credit cards has dramatically increased in recent years. Financial institutions face a large loss as a result of the expanding related fraud activities. As a result, we must evaluate transactions to determine which are real and which are fraudulent. In this essay, we present a comprehensive study of the various methods for detecting credit card fraud. These methods make use of bayesian belief Networks, algorithms, Decision Trees, Logistic Regression, Genetic Random Forests, Markov Models, and Hidden Support Vector Machines (SVM). There is a thorough examination of several approaches offered. Following analysis, we put the SVM and LR models into practice, along with addressing data imbalances and feature extraction. We summarise the advantages and disadvantages of the same as stated in the respective publications as our conclusion to the paper. We want to use a credit card approach that is effective.

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Leveraging ensemb.e Learning Model for Human Gait Recognition in Human computer Interface 2

Leveraging Ensemble Learning Model for Human Gait Recognitio...

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2nd International Conference on Automation, Computing and Renewab.e Systems, ICACRS 2023

作者： Pant, Suman bhat, Niyati Shobana, G. Sindhuja, R. Lekha, D. Srimathi, S. School of CA & IT Shri Guru Ram Rai University India B.E Computer engineering University of Mumbai Mumbai India Department of Computer Applications College of Science and Humanities SRM Institute of Science and Technology Kattankulathur India Department of Medical Electronics Saveetha Engineering college India CSE Department R.M.K. College of Engineering and Technology India Department of Bio Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9798350340235

Human Gait Recognition (HGR) is a biometric system that is exploited for security purpose over the past few years. The efficiency of gait detection is affected by different features namely walking surfaces, wearing clothes, and carrying a bag. Moreover, detection from divergent views is a complicated prob.em in HGR. Numerous approaches were proposed for HGR using classical and DL methods but the conventional technique is not appropriate for massive data. This study leverages ensemb.e Learning Model for Human Gait Recognition in Human computer Interface (eLMHGA-HCI) technique. The eLMHGA-HCI technique intends to recognize various kinds of human gaits to assist HCI applications. In the eLMHGA-HCI technique, Time Domain (TD) and Frequency Domain (FD) features from the input signals. For gait recognition, an ensemb.e of three DL models takes place namely bidirectional Long Short Term Memory (biLSTM), Gated Recurrent Unit (GRU) and Recurrent Neural Network (RNN). To exhibit the enhancedgait recognition results of the eLMHGA-HCI technique, a widespread simulation analysis is done. The simulation outcomes pointed out the enhanced performance of the eLMHGA-HCI approach over other existing techniques. © 2023 Ieee.

关键词： Activity recognition Deep learning ensemb.e learning Gait recognition Human computer interface

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employing a deep learning technique to categorize Internet of Things (IoT) traffic in a smart city context

Employing a deep learning technique to categorize Internet o...

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2023 Ieee International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATe 2023

作者： Anu Priya, S. Rajesh kanna, b. beaulah Jeyavathana, R. bhat, Niyati Rajalakshmi, S. Srimathi, S. Chennai600117 India Department of Electronics and Communication Engineering Alagappa Chettiyar College of Engineering and Technology Karaikudi India Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India B.E Computer Engineering University of Mumbai Mumbai India Department of Electronics and Communication Engineering RMK College of Engineering and Technology India Department of Bio Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9798350305708

The exponential increase in quantity of cars on the roads of smart cities has led to traffic jams, air contamination, and delays in the delivery of essential goods. every day road accidents continue to be a leading cause of death and serious injury. The Internet of Things-based Traffic Management System is utilized to manage traffic flow, ensure safe data transfer, and spot accidents. Autonomous vehicles and smart devices have sensors built into an IoT-based ITM system to help with identification, data collection, and transmission. Connectivity options for the Internet of Things are being severely tested by the proliferation of IoT devices and applications. This can lead to severe difficulties in the allocation of the spectrum, the resources needed to analyze the IoT data, and the allowab.e delay for important spare situations throughout the end-to-end communication chain. Therefore, accurate categorization or forecasting of the IoT devices' time-varying traffic characteristics is essential. However, the difficulty of making this sort of categorization remains open. Current approaches tend to be based on machine learning methods, which have a high computational cost despite not taking into account the traffic's fine-grained flow features. In this research, we address this issue by developing a two-stage categorization system based on deep learning for IoT devices in a smart city that draws on both connectivity and statistical information. In order to excerpt the internet and statistical characteristics set for various IoT strategies, we must do data cleaning and preprocessing. based on the results of the tests, it appears that the proposed categorization has a 99% chance of being correct. © 2023 Ieee.

关键词： Autonomous Vehicle Deep Learning IoT Traffic Data Smart-City Traffic Management

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