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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Chaitra HV Mohammad Shadaab Khushi Dubey Ritika Subudhi Simran Maurya Computer Science & Engineering Nitte Meenakshi Institute of Technology Bangalore India

This paper aims at building an AI solution that can recognize clothes and recommend outfits based on the user's preferences (color, style, type, etc). The idea is to utilize machine learning models for image segmentation and classifying a picture of clothing according to a set of predetermined features (Example: tops, trouser, skirt, etc). It then uses a recommendation system to provide a few combinations of outfits according to the parameters (Example: color, occasion, sleeve length, etc) set by the user. We plan on building a ML model for automatic categorization of fashion apparels. Deep Fashion2 dataset is being used totrain U-net model for classification of input image segments into top, bottom, dress etc on the segmented parts of the image.

关键词： Image segmentation Image color analysis Buildings Indium tin oxide Clothing Object detection Machine learning

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SINN Based Federated Learning Model for Intrusion Detection with Blockchain technology in Digital Forensic

SINN Based Federated Learning Model for Intrusion Detection ...

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Data science and Network Security (ICDSNS), International Conference on

作者： Priyanka Pramod Pawar Deepak Kumar R Krupa Piyush Kumar Pareek H M Manoj K S Deepika Information Technology University of the cumberlands Cumberlands United States Mechanical Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Today, smart devices permeate every part of our lives, ushering in a new technological era known as Internet of Things (IoT). There ration of cybersecurity risks due to the proliferation of smart devices in IoT contexts and the massive volumes of sensitive data they store. In order to determine the time and place of these attacks and to collect evidence that can lead to the identification of the perpetrators, digital forensics is essential. Research into digital forensics in an IoT setting is fraught with difficulties owing to factors such as the dispersed nature of data storage, the need to be able to track and verify the provenance of evidence, the difficulty in obtaining data from many sources, and the lack of visibility into the evidence collection procedure. This is why the research suggested merging two potential technologies to offer a complete answer. The research employed federated learning to train representations locally using data kept by IoT devices, with the help of a dataset that was created to mimic attacks on the IoT ecosystem. The study utilises the BoT-IoT dataset and employs a classification method called Shepard Interpolation Neural Network (SINN) with the Shuffled Frog-Leaping algorithm (SFLA). After that, in order to make the blockchain lightweight, the study aggregated data using blockchain by gathering parameters from the IoT gateway

关键词： Interpolation Privacy Federated learning Scalability Digital forensics Ecosystems Blockchains Internet of Things Resource management Smart devices

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Firefly and Aquila Optimization Based Clustering and Routing in IoT Assisted Wireless Sensor Network 2

Firefly and Aquila Optimization Based Clustering and Routing...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Soni, Mukesh Sunil, G. Rajesh, N. Alsalami, Zaid Dutta, Papiya Graphic Era Hill University Graphic Era Deemed to be University Department of Cse Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Bharat Institute of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350372892

Wireless Sensor Networks (WSNs) are a substantial part of the Internet of Things (IoT) utilized for cluster and routing path applications to monitor computational fields. The major challenge of the Firefly Algorithm (FA) is its sensitivity to parameters such as the attractiveness coefficient and the randomization factor. Selecting appropriate parameter values can be challenging and may require fine-tuning. The proposed method utilizes energy levels in the IoT to efficiently manage routing data transmission, enhancing information transfer through the cluster router. Aquila Optimization (AO) often involves trade-offs between different parameters. Improving one aspect may negatively impact another, and finding the right balance can be difficult. Although the proposed FA and AO method exhibit high performance compared to existing methods such as Black Widow Optimization (BWO), Fixed Parameter Tractability (FPT), and Multipath Link Routing Protocol (MLRP). The proposed FA and AO method yield high results, achieving a Network Lifetime 1900 rounds, Packet Delivery Ratio (PDR) of 0.98, and an End-To-End Delay (ETED) of 0.80 ms. These outcomes outperform existing methods in the context of WSNs. © 2024 IEEE.

关键词： Wireless sensor networks

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Improved Whale Optimization Algorithm Based Energy Efficient Secure Routing in Mobile Wireless Sensor Networks

Improved Whale Optimization Algorithm Based Energy Efficient...

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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Naga Raju, M. Sunil, G. Almoussawi, Zainab Abed Ravi Kiran Varma, P. Chakraborty, Subhra Department of CSE GST GITAM Bengaluru Karnataka Nagadenahalli561203 India School of Computer Science and Artificial Intelligence SR University Warangal India College of Islamic Science Ahl Al Bayt University Karbala Iraq Department of CSE Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9789819778751

Mobile Wireless Sensor Networks (MWSN) have gathered significant attention due to their diverse applications across military, industrial infrastructure, automation, health, traffic, and various consumer domains. The primary challenges in MWSN encompass energy consumption, connectivity, scalability, and security. Introducing mobility further amplifies the complexity, necessitating the identification of efficient routing protocols to enhance the overall performance of MWSNs. In this research, Improved Whale Optimization Algorithm (IWOA) is proposed for obtaining the energy efficient secure routing in MWSN. The consumption of energy in WSN is achieved by the optimal routing method by utilizing the optimization algorithm. The optimization-based routing approach is developed for designing the optimal transmission path by Cluster Head to destination. The performance of proposed method achieves better results by utilizing the performance metrices of First Node Dead (FND) of 2070, Half Node Dead (HND) of 14,568 and Last Node Dead (LND) of 19,894 when compared to the existing methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Fault Detection and Classification in Power Grids Using Shannon Entropy with Convolution Distributed Autoencoder 4

Fault Detection and Classification in Power Grids Using Shan...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Veeramachaneni, Vinod Husain, Saef Obad Khanna, Virat Pareek, Piyush Kumar Bhuvaneshwari, P. Colorado Technical University Department of Information Technology Colorado Springs United States College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq University Centre for Research & Development Chandigarh University Mohali India Nitte Meenakshi Institute of Technology Department of Aiml & Ipr Cell Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Nowadays, Power grids have undergone significant transformations as the demand on electricity increased tremendously. Power grids are the primary sources for electricity generation and power distribution to the consumers need, but it faces numerous faults during this process. The traditional approaches for detection of faults in power grids had faced challenges, that includes accurately detecting and classifying which type of faults in the power grid. Therefore, this research proposes Shannon Entropy-Convolution Distributed Autoencoder (SE-CDAE) for fault detection and classification in power grids. The proposed method employed on Power Grid dataset which provides the current signals data that includes normal current signals and fault current signals. Then, the gathered data is pre-processed by fast wavelet transform which converts the time-domain signals into wavelet coefficient by removing the noise and dimensionality. These pre-processed data optimal features are extracted by using SE that effectively eliminates the irregular patterns in data. Finally, CDAE is employed for detecting and classifying the faults from extracted optimal features. The proposed SE-CDAE effectively detected and classified the faults in power grids and succeeded with high results including accuracy (97.98%), precision (94.83%), recall (96.28%), F1-score (97.35%) when compared with existing Semi-ProtoPNet Visual Geometry Group-19 (SPPN-VGG-19). © 2024 IEEE.

关键词： Power grids

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Synergistic applications of nanostructured boron nitride in photocatalysis and targeted drug delivery

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Nanotechnology for Environmental engineering 2024年第2期9卷 179-188页

作者： Mali, Priyadarshani Shivakumar Jagtap, Bhagyashree Krishnarao Gowda V, Dankan Ashreetha, B. Vasanthakumar, G.U. kaur, Mandeep Hariram, Venkatesan Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth’s college of Engineering Kolhapur Maharashtra416013 India Department of Electronics and Computer Engineering Sharad Institute of Technology college of Engineering Yadrav Maharashtra Ichalkaranji Kolhapur416115 India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka560064 India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Yelahanka Bengaluru560064 India Department of Electronics and Communication Engineering Punjabi University Punjab Patiala147002 India Department of Practice of Medicine Vinayaka Mission’s Homoeopathic Medical College and Hospital A Constituent College of Vinayaka Mission’s Research Foundation Deemed to be University Tamilnadu Salem636 308 India

This study explores the applications of nanostructured boron nitride (BN) in photocatalysis and targeted drug delivery, highlighting its notable thermal conductivity, chemical stability, and wide bandgap. Through the synthesis and detailed characterization of BN nanoparticles, employing Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), the material’s efficacy in light-induced chemical reactions and potential as a nanocarrier for drug delivery systems are investigated. Photocatalytic experiments reveal an enhanced ability of BN to absorb light and facilitate charge separation, suggesting a promising approach for environmental detoxification and sustainable energy generation. In parallel, investigations into BN’s drug loading capacity, release kinetics, and targeting efficiency, particularly in the context of cancer therapy, are conducted. Findings indicate that BN nanoparticles significantly enhance photocatalytic performance and offer a safe, effective platform for controlled drug release. This research underscores the dual utility of BN in addressing critical challenges in environmental engineering and medical treatment, pointing towards novel interdisciplinary applications of this versatile nanomaterial. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Photocatalysis

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Algorithmic Trading and Quantitative Analysis of Stocks using Data science: A Study 2

Algorithmic Trading and Quantitative Analysis of Stocks usin...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Vishwambhari, Kush Saini, Abhishek Kaif, Muhammad Pandey, Sanket Prashanth, B.S. Kumar M V, Manoj Shiva Darshan, S.L. Thomas, Likewin Nitte-Meenakshi Department of Information Science & Engineering Karnataka Bangalore560064 India Manipal Institute of Technology Department of Information and Communication Technology Karnataka Bengaluru560 064 India Pesitm Department of Computer Science & Engineering Karnataka Shimoga577204 India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Algorithmic Trading and Quantitative Analysis of Stock is a system that uses complex algorithms created with the use of mathematical models and technical indicators to completely automate the transaction, from stock selection to order placement. The majority of algorithmic trading setup providers have very high costs, and the existing research in this area is insufficient in terms of ROI (return on investment) and ethical viability. The majority of the past literature work just used simple mathematical models and indicators to develop their algorithms, but the results were not good enough. The drawbacks of algorithmic trading include the need for ongoing observation and the inconsistency in grasping the irrational market. With the aid of a combination of machine learning algorithms and data science principles implemented in python, this proposed work aims to develop various algorithms based on the requirements of traders or investors. Our approach outperforms the returns of the index and yields an appropriate return for the investor on his long-term investments. © 2022 IEEE.

关键词： Commerce

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Ant Colony Optimization in MANET for Decreasing Energy Consumption with ML Approaches

Ant Colony Optimization in MANET for Decreasing Energy Consu...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Suresha, P. Math, Gayatri Arvind, Siddapuram Bhaskar, M. Naveen, I.G. S.E.A College of Engineering and Technology Department of Mechanical Engineering Bengaluru India HKE Society's B.V. Bhoomaraddi College of Arts Science and Commerce Degree College Department of Electronics & Communication Engg Bidar India Hyderabad Institute of Technology and Management Department of Computer Science & Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

To coordinate access from the active nodes, MAC protocols are used in ad hoc networks. Many media access control (MAC) protocols have been suggested for use in ad hoc networks, each with its own unique set of goals. The goal of many ad hoc networks is to increase the amount of time that its individual nodes, and by extension, the network, can function. Ad-hoc nodes are considered dead when their batteries run out, hence it is crucial to reduce power drain. Because to the nature of MANETs, in which nodes must depend on limited, portable power sources, energy efficiency is a significant barrier to their widespread implementation. In multi-hop systems, where nodes must additionally expend energy to route packets for other nodes and assure the connection of the network, energy saving is difficult. Since energy-efficient routing is a relatively new area of study, there are still many open questions that need to be investigated and no widely accepted standards in place. Several routing protocols used the shortest route as a criterion for path selection;however, this is inappropriate due to the topology's significant and unexpected variability and does not assume the most efficient use of energy. Simulation is the primary tool for testing the efficacy of several routing protocols in MANETs, including RIP, RIPng, PS-RIP, PSLLC-RIP, PS-RIPng, PSLLC-RIPng, FLBUDTCRIP, and ANFISBUDTCRIP. Exata 5.4 GUI network simulator and OPNET tools are used to analyse the efficiency of the suggested protocols. All the simulation results show \that the suggested protocols perform better than the current RIP protocols. © 2023 IEEE.

关键词： multi-protocol label switching

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Risk Prediction and Management for Effective Cyber Security Using Weighted Fuzzy C Means Clustering 3

Risk Prediction and Management for Effective Cyber Security ...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pillai, Sanjaikanth E. Vadakkethil Somanathan Polimetla, Kiran Prakash, Chidurala Sai Pareek, Piyush Kumar Zanke, Pankaj School Of Electrical Engineering And Computer Science University Of North Dakota Grand Forks United States Adobe Inc. Dept. Of Information Technology AustinTX United States Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Aiml And Ipr Cell Bengaluru India Sapiens International Corporation United States

ISBN: (纸本)9798350318609

The necessity of the continuous risk assessment as well as management is attracting the people due to the requirement of protecting the risk. The management of the risk plays a significant part in solving the cyber threats within Cyber-Physical System (CPS). However, because of the enhanced complexity of CPS, the cyber-attacks are the most urbane as well as low predictable, which made the risk management tasks more challenging. This research proposes the efficient Cyber Security Risk Management (CSRM) repetition by the utilization of the Weighted Fuzzy C Means (WFCM) clustering for the prediction and management of the cyber risk. Initially, VCDB dataset is collected to estimate the effectiveness of the model and Term Frequency-Inverse Document Frequency (TF-IDF) approach is utilized for the extraction of feature from the collected data. Then, feature selection is performed by using Principal Component Analysis (PCA) and prediction is performed by the utilization of the WFCM. The effectiveness of the WFCM is estimated by using various performance metrices and it achieves the accuracy of 84.2% and precision of 0.746 when compared to the existing methods such as fuzzy and DeepSpamPhisNet (DSPN). © 2024 IEEE.

关键词： Risk management

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Stock Price Prediction using Bi-directional Long-Short Term Memory based Deep Neural Network

Stock Price Prediction using Bi-directional Long-Short Term ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sowmya, M. Al-Farouni, Mohammed Reddy Panga, Naresh Kumar Indira, R. Saranya, N. Naga Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India The Islamic University Engineering College of Technical Engineering Department of Computers Techniques Najaf Iraq Virtusa Corporation New York United States Panimalar Engineering College Department of Ece Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350360660

In the area of finance, forecasting of stock price is becoming very popular increasingly. Predicting the stock price of a firm is very important to increase speculators interest in investing for a company and for growth of shareholders in that stock. In addition to a unique investment portfolio model based on the projections produced, this article suggests a novel method for time-series stock price prediction. To achieve this, a Bidirectional Long-Short Term Memory-based Deep Neural Network (Bi-LSTM-DNN) is equipped with novel regression technique. Once made, the predictions are utilized to build investment portfolio, more precisely, a projected portfolio. Numerous tests have been conducted using NIFTY-50 stock data that was acquired from Indian National Stock Exchange. The outcomes verify that suggested model performs better than a number of conventional prediction models and a number of conventional portfolio optimization algorithms. Reducing investing risks and increasing rewards are possible with an accurate stock price prediction. The Root Mean Square Error (RMSE) value for the proposed deep learning network for stock prediction using Bi-LSTM-DNN is 0.81. © 2024 IEEE.

关键词： Prediction models

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