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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Sureka R. Gondkar Siva Balan R V Kavitha S Raju Ramakrishna Gondkar Department of Electronic and Communication Engineering BMS Institute of Technology and Management Bangalore India Department of Computer Science CHRIST (Deemed-to-be-University) Bangalore India Department of MCA Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

In an era characterized by pervasive interconnectivity facilitated by the widespread adoption of Internet of Things (IoT) devices across diverse domains, novel cybersecurity challenges have emerged, underscoring the imperative for robust intrusion detection systems. Conventional security frameworks, constrained by their closed-system architecture, struggle to adapt to the dynamic threat landscape marked by the continual emergence of unprecedented attacks. This paper presents a methodology aimed at mitigating the open set recognition (OSR) challenge within IoT-specific Network Intrusion Detection Schemes (NIDS). Leveraging image-based representations of data, our approach focuses on extracting geographical traffic patterns. We observe that the Recurrent Neural Network exhibits suboptimal classification accuracy and lacks parallelizability for attack analysis tasks. Our investigation concludes that the Sparrow Search Optimization Algorithm (SSOA) serves as a foundation for constructing an effective assault classification model. This research contributes significantly to the field of network security by emphasizing the importance and ramifications of meticulous hyperparameter tuning. It represents a critical stride toward developing IDSs capable of effectively navigating the evolving cyber threat landscape. In the experimental analysis of proposed model reached the accuracy and 0.963% respectively.

关键词： Training Refining Logic gates Traffic control Search problems Internet of Things Task analysis

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Hybrid Optimization Based BERT Model for Drug Detection in NLP

Hybrid Optimization Based BERT Model for Drug Detection in N...

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Mysore Sub Section International Conference (MysuruCon), IEEE

作者： C G Kruthika N N Namitha Devi Vijayananda V Madlur K Asha T M Aruna Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Vidya Vikas Institute of Engineering & Technology Mysuru India Department of Computer Science & Engineering Mangalore Institute of Technology & Engineering Moodabidri Department of Computer Science & Engineering Reva University Bengaluru India

When it comes to natural language processing (NLP), sentence embedding is a hot topic because of the knowledge it transfers to subsequent tasks. Recent studies have shown that sequential neural networks, which can be trained on large quantities of data, can be used to encode info through a one-way analysis of text sequences, making them a powerful tool for drug detection. The purpose of this study is to smear cutting-edge deep learning techniques to publicly available data in order to accurately detect and identify adverse medication reactions that have not been recorded by medical professionals (open data). This study presents a deep learning-based strategy employing Enhanced Encoder on a dataset of 10,000 reviews culled from WebMD and *** and labelled by hand (IBERT). The model is created by integrating the BERT with many parallel blocks of a single-layer deep Convolutional Neural Network (CNN) that use varying kernel sizes and filters. To deal with ambiguity, the biggest obstacle to NLP, such a combination is helpful. Cuckoo Search (CS) and Antlion Optimization (ALO) are coupled to create a hybrid optimization strategy for determining the best values for the proposed model’s hyper-parameters. Results from a number of different classification methods demonstration that the suggested model meaningfully outdoes the state-of-the-art models.

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A Patient-Centric Blockchain Framework for Transparent and Secure Medical Data Sharing Using Modified AES

A Patient-Centric Blockchain Framework for Transparent and S...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Priyanka Pramod Pawar Deepak Kumar Mohan Kumar Meesala Piyush Kumar Pareek Santosh Reddy Addula K S Shwetha Department of Information Technology University of the Cumberlands Williamsburg USA Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Due to its decentralised, accessible, and secured structure, blockchain—a framework that has historically been viewed with suspicion—has developed into a groundbreaking breakthrough. Reliable automated scripting and static data records are also a part of this system. The intricacy and expertise required to operate with blockchain technology is one of its disadvantages. Machine Learning (ML) frameworks are the key fixing this problem. It has the potential to make Blockchain more user-friendly. In this research paper, we reviewed the current state of blockchain-based cloud computing, machine learning, and safe data exchange. In this study, we suggest using a blockchain technology to provide a safe space where health entities can share medical information transparently. A modified advanced encryption standard (MAES) based blockchain encryption model is developed by us. This method is based on engineering and AI principles, and its main objective is to design useful artefacts that improve their respective domains.

关键词： Privacy Ecosystems Smart contracts Medical services Machine learning Blockchains Encryption Security Reliability Standards

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Classification of EEG signals using Machine learning algorithms 2

Classification of EEG signals using Machine learning algorit...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Suganyadevi, S. Shanmuga Priya, S. Kiruba, B. Gomathi, M. Kalshetty, Jagadevi. N. Kpr Institute of Engineering and Technology Department of ECE Coimbatore India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

An alternative to human expert-performed manual identification is automatic detection of epilepsy using electroencephalogram (EEG) data. Automatic epilepsy detection from EEG data need high classification performance in order to eliminate false positives. A classification strategy for automated epilepsy identification using EEG data is being proposed in this work. The signals generated form the EEG device were transformed using the DWT before feature extraction was carried out. Based on various statistical parameters and crossing frequency features, an algorithm dubbed GBMs fusion was developed to identify EEG data. As an added bonus, the significant traits were first selected using a genetic algorithm. EEG data from the University of Bonn has been used to test the suggested method's ability to distinguish between normal and ictal EEG patterns. Experimentation has shown that the suggested GBMs fusion may increase the EEG classification performance. It is also possible to identify epilepsy from EEG data with a 100% accuracy using the suggested GBMs fusion. © 2022 IEEE.

关键词： Electroencephalography

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Retraction Note: Sub-network modeling and integration for low-light enhancement of aerial images

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Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Uganya, G. Devi, C. H. Sarada Chaturvedi, Abhay Shankar, B. B. Ramesh, Janjhyam Venkata Naga Kiran, Ajmeera Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Meenakshi College of Engineering Chennai India Department of Electronics and Communication Engineering GLA University Mathura India Department of Electronics and Communication Engineering NITTE NITTE (Deemed to Be University) NMAM Institute of Technology (NMAMIT) Karkala India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad India

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Wide-Deep Neural Networks with Randomized Sparse Backpropagation Simplify Demand-Side Load Event Detection

Wide-Deep Neural Networks with Randomized Sparse Backpropaga...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Adusumilli, Manasa Ramesh, G.P. Katkam, Vijayrani Bhargavi Pareek, Piyush Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India St. Peter's Institute for Higher Education and Research Department of Electronics and Communication Engineering Chennai India Ballari Institute of Technology and Management Department of e and Ce Bellary India Hyderabad Institute of Technology and Management Department of Cse Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350301595

Identifying demand-side load events is a critical step in lowering energy use in a number of situations. Using wide-deep neural networks and randomised sparse backpropagation algorithms, this study presents a unique way to identify demand-side load events. To detect subtleties in the input data, we employ randomised sparse backpropagation and both shallow and deep network topologies. We also use the ReLU and sigmoid activation functions to improve the performance of our model. We use the Adam optimizer to train the proposed model, using a learning rate of 0.001 per batch and a batch size of 64. The results were compared to those of other popular technologies, such as support vector machines. According to the findings of our studies, the technique we utilised is more precise and efficient than any other option we investigated. The proposed approach combines wide-deep neural networks with randomised sparse backpropagation to simplify demand-side load event identification. © 2023 IEEE.

关键词： Energy efficiency

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Prediction of Floods in Kerala using Hybrid Model of CNN and LSTM

Prediction of Floods in Kerala using Hybrid Model of CNN and...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Piyush Kumar Pareek Chetana Srinivas Nayana S Manasa D S Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

Floods are a widespread natural calamity that have far-reaching effects on human society, economics, and culture. Researchers have been hard at work for years on flood prediction models. Predicting the condition of flooding is a difficult task that requires in-depth research of the causes of floods. Changes in LULC are of paramount importance to decision planners and environmentalists because of unsustainable development's impact on ecological systems. Therefore, the purpose of this research is to determine the LULC for the Vembanad Lake System (VLS), Kerala. In this research, we combine the CNN and the LSTM network to create a hybrid CNN-–LSTM classical for flood prognostication, which takes full advantage of the fact that the CNN can efficiently extract features while the LSTM can accurately reflect the longer-term historical procedure of the time series data. Four models are then constructed for flood prediction: a LSTM perfect, a LSTM perfect, a CNN-–LSTM perfect, and a CNN-LSTM model. Finally, these models are evaluated using a variety of metrics, and the suggested CNN-LSTM model is exposed to be the most effective because to its low error and fast training time

关键词： Training Biological system modeling Time series analysis Training data Predictive models Lakes Feature extraction

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Prediction of Rainfall in Karnataka Region using optimised MVC-LSTM Model

Prediction of Rainfall in Karnataka Region using optimised M...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Piyush Kumar Pareek Achyutha Prasad N Chetana Srinivas Jagadeesh B N Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

Forecasting precipitation is a prominent topic of study in meteorology. Predictions of precipitation using statistical analysis, learning methods are only a few of the methods that have been offered in the past. Organizations tasked with preventing natural catastrophes might benefit from using meteorological time series data prediction in their decision-making processes. The volume, dimension, and frequency of updates to Time Series data are quite high. The study of period sequence data for forecasting is a crucial part of the practical application. Time series data analysis allows for more precise rainfall forecasts, which is useful for assessing the severity of potential droughts and floods. Precipitation forecasting publications have employed a wide range of approaches. This improved forecast accuracy may be attributed to these methods. In this research, we apply a deep learning technique to examine the precipitation records from the Karnataka Division. This article presents a network consisting of a generator and a predictor for predicting spatial-temporal rainfall data. To imprisonment spatial correlations and build high-resolution data from sparse comments, it uses a multi-layer perceptron (MLP) in its generative module. A Multivariate Convolutional (MVC-LSTM) network is used to create the prediction unit; this network is able to capture the interplay between many variables and their temporal and spatial correlations. The Honey Badger approach finds the appropriate weight for the LSTM, which improves the model's ability to classify data. Additionally, this report suggests several avenues for further study in the fields of rainfall prediction and time series data analysis.

关键词： Deep learning Data analysis Correlation Time series analysis Droughts Predictive models Data models

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Efficient Spectrum Sensing in Cognitive Radio Network Using Multi-Objective Improved Salp Swarm Algorithm

Efficient Spectrum Sensing in Cognitive Radio Network Using ...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Sureka N Vasanthakumar G U P Swathypriyadharsini Haider Alabdeli Bhupathi Prashanthi Department of Biomedical Engineering Agni college of Technology Chennai India Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer science and Engineering Bannari Amman institute of technology Sathyamangalam Tamilnadu India The Islamic university Najaf Iraq Department of Computer Science and Engineering GRIET Hyderabad Telangana India

The Cognitive Radio (CR) has been established as an emerging technique which enables underused or unused spectrum for dynamically enhance the spectral effectively. To enhance the CR performance which is extensively significant to reconfigure or adapt the model parameters. The Multi-Objective Improved Salp Swarm Algorithm (MOISSA) is proposed for spectrum sensing in CR networks. The objective functions such as throughput, energy efficiency and interference are formulated to define the spectrum sensing performance in CR networks. In this paper, the parametric alteration of CR is accomplished through SLSSA which exploits the areas near to individual location to attain its global optimum. The SSA is used to avoid local optima and define the suitable optimal solution evaluation in whole optimization process. In traditional SSA, the Self Learning (SL) rule is applied to provides every learner opportunity to enhance its individual experience through search. The performance of MOISSA is estimated by various metrics like delay, Packet Delivery Ratio (PDR), energy consumption and network lifetime are used with 20, 40, 60, 80 and 100 nodes. The MOISSA attains delay of 13.4s, PDR of 59.7%, 8.55J of energy consumption and 27.5 of network lifetime for 100 nodes which is better when compared to existing method such as Improved Whale Optimization Algorithm with Convolutional Neural Network (IWOA-CNN).

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IoT-driven Transformation of Poultry Farming: Ensuring Health, Hygiene, and Efficiency

IoT-driven Transformation of Poultry Farming: Ensuring Healt...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： Priyanka K N Sridhara Prasanna Kumar M J Vijay Kumar M S Information Science & Engineering Department Nitte Meenakshi Institute of Technology Bangalore India Computer Science & Engineering Department BGS Institute of Technology Adichunchanagiri University BG Nagara India Department of ISE MIT Mysore

This paper presents an innovative approach to poultry farming, leveraging cutting-edge technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and edge computing. Unlike conventional manual monitoring systems, the proposed system deploys an array of smart sensors for continuous real-time monitoring of critical environmental parameters, including temperature, humidity, gas levels, and lighting. The data collected by these sensors are processed through edge computing, enabling autonomous control of farm actuators to optimize conditions for poultry health and production. A significant emphasis is placed on ensuring food and water hygiene within the poultry environment. To mitigate the risk of contamination and potential disease spread, the system incorporates precise food measurement mechanisms tailored to the specific age of the chicks. This meticulous approach guarantees the dispensing of the appropriate amount of fresh and uncontaminated food during each feeding cycle, thereby maintaining a high standard of hygiene at the feeder. Ultimately, this proactive system significantly contributes to safeguarding poultry health and enhancing overall farm productivity while prioritizing disease control. By implementing these health-conscious practices, the proposed system ensures the production of robust and healthy chickens, meeting the stringent standards demanded by consumers and reshaping poultry farming practices for greater sustainability and efficiency.

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