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Transformer Encoder and Decoder Method for Forest Estimation and Change Detection 2

Transformer Encoder and Decoder Method for Forest Estimation...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kotin, Kiran Kashinath Kumar, Sharan Alabdeli, Haider Kumar, Gotte Ranjith Ramachandra, A.C. Smt. Kamala and Sri Venkappa M. Agadi College of Engineering and Technology Department of Ece Karnataka Lakshmeshwar India H.K.E. Society's S.M.V. College of Engineering Department of Ece Karnataka Raichur India The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

Forest Change Detection (FCD) is difficult component of natural assess monitoring and conservation strategy, enabled informing decision-making. Different techniques using strength of Artificial Intelligence (AI) has implemented to detect and classify modifications in forest cover by Remote Sensing (RS) data. In this research, proposed a Transformer Encoder and Decoder (TED) method for forest estimation and change detection. The Multi Feature Fusion (MFF) is used for effective combine features of various scales. Next, Multilevel decoding named as Multi Scale and Multi Decoder (MSMD) is used at fine the scales much excessively. At last, loss is function is used for minimizing loss of MSMD. The performance of proposed method is analysed with performance measures of Intersection over Union (IoU) and Accuracy. The proposed method attained IoU of 86.23% and 87.45% in forest and background Attained mIoU of 85.21% and 88.92% in forest and background respectively. The proposed method attained accuracy of 92.89% and 92.33% in forest and background. Attained mAcc of 90.54% and 91.48% in forest and background respectively. The proposed method performed effective than previous methods like Multiscale Channelwise Cross Attention Network (MCCANet), UNet and Generative Adversarial Networks (GAN). © 2024 IEEE.

关键词： Forest Change Detection Multi Feature Fusion Multi Scale and Multi Decoder Remote Sensing and Transformer Encoder and Decoder

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An Efficient Attendance Management with Deep Learning

An Efficient Attendance Management with Deep Learning

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Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), International Conference on

作者： Shivanna K Hema H P Janardhana D R Srinivas P M Department of Computer Science and Engineering Sahyadri College of Engineering and Management Mangaluru India Department of Electronics and Communication Engineering D S Dinakar Polytechnic College Shivamogga India Department of Computer Scince and Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517908

Accurate attendance recording is critical in educational institutions to ensure students' and teachers' consistency and discipline. Traditional manual attendance systems are frequently inefficient and prone to errors, such as proxy attendance and missed entries, which may impact the accuracy of attendance records. To address these challenges, an automatic attendance system that uses camera-based facial recognition technology is proposed. The latest technology records accurate attendance by taking visuals of students as soon as the class starts and comparing them to an already-existing database. Face recognition happens through human detection. The concept used here is deep learning. For students, this automated solution streamlines attendance management by ensuring that records are accurate and up to date without the need for manual entry. The system efficiently keeps track of the presence of teachers, notifying the Head of Department (HOD) right away in the case of an excused absence, therefore preventing disruptions during scheduled lessons. This technology significantly increases the reliability and efficiency of attendance tracking by automating the process. Enhancing record accuracy and accountability leads to a more regulated and effective learning environment that benefits teachers and students alike. This technological method offers greater precision and oversight, leading to improved institutional administration and educational outcomes.

关键词： Deep learning Visualization Accuracy Mechatronics Face recognition Manuals Recording Visual databases Reliability Intelligent systems

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LDWPSO based Bi-LSTM Model for Predicting the Missing Data in PHRs

LDWPSO based Bi-LSTM Model for Predicting the Missing Data i...

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

作者： Piyush Kumar Pareek Anoop N Prasad Gangambika G Nayana C P 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

In medical and health datasets, it's common for some information to be missing. When you look at datasets that are missing some information, you might get skewed results or less statistical power. Because of this, some data may be missing, which hurts the analysis of the data. These missing data should be replaced with appropriate values. This article suggests a way to estimate missing data using an optimised Bi-Directional Long Short-Term Memory (OBi-LSTM) applied to big data in healthcare. A memory gate is used in the suggested method to estimate missing data that comes up when collecting and processing data. In this study, information from the Korean National Health Nutrition Examination Survey (KNHNES) by the Korea Centers for Disease Control and Prevention (KCDC) is used. As typical South Korean health care records, they have a lot in common with personal health records (PHRs). On the basis of this, models can be made to estimate the missing data in PHRs. PHRs are also multimodal, which means that data can be collected from many different sources for a single object. The Bi-LSTM model is used in a multimodal setting because of this. By preprocessing, a KNHNES data set is made that has no missing values. In data set-based learning, a label is set as the first piece of data. The parameters of Bi-computational LSTM affect how fast the model works. Linearly decreasing weight particle swarm optimization is used in the research to deal with these problems (LDWPSO). There is a big difference in the number of parameters between the two models, but there isn't much difference in how accurate they are. A multi-modal approach can also cut down on the time it takes to process large amounts of data in places like hospitals and institutions.

关键词： Support vector machines Radio frequency Noise reduction Machine learning Predictive models Data models Particle swarm optimization

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Improved Gated Recurrent Unit Based Network Traffic Anomaly Detection 3

Improved Gated Recurrent Unit Based Network Traffic Anomaly ...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Jasim, Layth Hussein Nagarathna, P. Sunitha, Yada Lekha, V. Padmavathy, R. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Computer Science Bengaluru India Cvr College of Engineering Department of Computer Science and Engineering Hyderabad India Dhanalakshmi Srinivasan College of Engineering Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331508456

The detection of unpredicted events or rare patterns in traffic data plays an important role in intelligence traffic management, that offers alerts of incidents. To address the problem of less accuracy in industrial network traffic anomaly detection, in this manuscript developed the Improved Gated Recurrent Unit (GRU) for multi-type anomaly detection. The pre-processing included label encoding and non-string numerical process. The Synthetic Minority Over-Sampling Technique with Edited Nearest Neighbors (SMOTE-ENN) technique is utilized for balancing the data by oversampling and under sampling. The improved GRU is assigned for extracting linear, non-linear and temporal features from network traffic data. These features are given to classifier developed based on Softmax for obtaining high performance detection on traffic attacks. The efficiency is evaluated based on UNSW-B15 dataset with the detection accuracy of 0.97 for DoS attacks, 0.96 for Fuzzers attacks and 0.94 for Worms attacks while compared to existing algorithms like conventional GRU for network traffic anomaly detection. © 2025 IEEE.

关键词： Telecommunication traffic

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Improved Sail Fish Optimizer for Energy Efficient Routing in Wireless Sensor Network

Improved Sail Fish Optimizer for Energy Efficient Routing in...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Aluvala, Srinivas Reddy, Geetha Al-Jawahry, Hassan M. Ramadan, Ghazi Mohamad Pareek, Piyush Kumar SR University School of Computer Science & Artificial Intelligence Warangal India CSE Malla Reddy Engineering College for Women Hyderabad India The Islamic University Najaf Iraq Ahl Al Bayt University College of Mlt Karbala Iraq Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350315455

A Wireless Sensor Network (WSN) is a collection of nodes fitted with small sensors and transceiver elements that are utilized for sensing, tracking, and data collection in a variety of situations. Sensor energy is considered a significant resource due to the difficulty in providing battery a backup to sensors when they are deployed in an uncontrolled or wild area. Integrating clustering & routing strategies are the optimal techniques for saving sensor node energy. In this research, the Improved Sail Fish Optimizer (ISFO) is proposed for the energy efficient routing path selection in WSN to improve the network lifetime. The ISFO algorithm is utilized to identify an accurate routing to sink node for data transmission. The performance of proposed algorithm is evaluated with performance metrics of Packet Delivery Ratio (PDR), Throughput, energy consumption and delay. The proposed algorithm attained high PDR of 96%, high throughput of 97%, less energy consumption of 0.4J and less delay of 3ms which is superior than other existing algorithms. © 2023 IEEE.

关键词： Clustering Improved Sail Fish Optimizer Routing protocol Sensor Nodes and Wireless Sensor Network

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Deep Learning-Based Dynamic Scheduling for Semiconductor Manufacturing with High Uncertainty of Automated Material Handling System Capability 2

Deep Learning-Based Dynamic Scheduling for Semiconductor Man...

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

作者： Dineshkumar, R. Al-Attabi, Kassem Kumar, Komuravelly Sudheer Madhura, G.K. Sharma, Jaya Saveetha Institute Of Medical And Technical Sciences Saveetha University Saveetha School Of Engineering Department Of Ece Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Sr University School Of Computer Science & Artificial Intelligence Telangana Warangal India Nitte Meenakshi Institute Of Technology Department Of Artificial Intelligence And Machine Learning Bengaluru India Gitam University Department Of Cse Bengaluru India

ISBN: (纸本)9798350381665

To improve the dynamic responsiveness and production efficiency of manufacturing workshops to personalized orders, a multi-agent manufacturing system. However, the previous methods cannot work well in a changeable workshop environment when encountering a large number of stochastic disturbances of orders and resources. Develop a robust scheduling approach for dynamic workshop environments, effectively managing stochastic disturbances in orders and resources to optimize production efficiency and minimize disruptions. Dynamic scheduling approach for semiconductor manufacturing using Proximal Policy Optimization (PPO) and Autoencoder (AE) for feature extraction, defining state space to capture task and production attributes. Action space design employs dispatching rules for operation selection, while the reward function adapts production to demand, trained in action and state phases. Autoencoders compress high-dimensional input data into a lower-dimensional latent space while preserving important features. The obtained results demonstrate that the proposed method achieves better performance Delivery Data Rate (DDR) of 41.20 %, Mean Cycle Time (MCT) of 51.66%, Mean of 12.85%, and Execution time of 5.07s. These results ensure superior dynamic scheduling performance compared to other existing methods, such as Dynamic Scheduling Using Flexible Job Shop Scheduling problem (FJSP), and Multiple-DynamicScheduling-Rule (MDSR). © 2024 IEEE.

关键词： Production efficiency

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A Software Defect Avoidance Technique for E-commerce Website Development 6th

A Software Defect Avoidance Technique for E-commerce Website...

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6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Sudarshan, R. Srivatsa, S.K. Chaithra, K.N. Mohan Kumar, K.N. Department of Computer Science and Engineering VELS University Chennai600117 India Department of Electronics Engineering Anna University Chennai600044 India Department of Electronics and Communication Nitte Meenakshi Institute of Technology Bengaluru560064 India Department of Computer Science and Engineering MS Ramaiah University of Applied Sciences Bengaluru560058 India

ISBN: (纸本)9789811613418

As per the literature study, the development of e-commerce websites is done by adopting different methodologies, which does not give due importance to the logical sequencing of the functionality of each module to be developed. This work proposes a technique that helps in producing defect-free e-commerce applications by developing a module dependency functionality matrix, mainly on ATG (Art technology Group) framework, using rational unified process (RUP). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic commerce

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Intrusion Detection System Using Improved Gloden Jackal optimization for Security and Privacy in Internet of Things

Intrusion Detection System Using Improved Gloden Jackal opti...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： Naresh Kumar Bhagavatham Mohammed Al-Farouni Komuravelly Sudheer Kumar S meenakshi Sundaram R P Puneeth Dept of Computer Science and Engineering Vignana Bharathi Institute of Technology Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of AI&DS Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering NMAM Institute of Technology Affiliated to NITTE (Deemed to be University) Karnataka India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

technology plays a primary role in rapid growth of service and identifying the quality of life. Recent technology such as Internet of Things (IoT) determines an impressive performance in the development of fast-forward. Intrusion Detection System (IDS) is employed as a lifeline to prevent attacks by categorizing activities as suspicious or normal. However, identifying the most relevant features from large data is challenging to effectively distinguish among suspicious or normal behavior. This research proposes the Improved Gloden Jackal optimization (IGJO) to select the appropriate features in IDS for security and privacy. GJO is improved by using tent mapping reverse learning for initializing populations, sine and cosine techniques are performed for updating prey positions which increases the ability of global exploration, and Cauchy mutation is used to acquire the optimal solutions. Initially, the CIC-IDS2017 dataset is used to evaluate the model performance and min-max normalization is utilized for normalizing the data. Then, IGJO is performed to select the appropriate features and Long Short-Term Memory (LSTM) is employed to classify IDS. The IGJO achieves a high accuracy of 99.95% compared to existing techniques respectively.

关键词： Privacy Accuracy Sociology Intrusion detection Data models Internet of Things Security

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Human Activity Classification using G-XGB

Human Activity Classification using G-XGB

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

作者： Kataria, Aman Puri, Vikram Pareek, Piyush Kumar Rani, Sita Amity Institute of Defence Technology Amity University Noida Campus Sector-125 Noida India Duy Tan University Center of Visualization and Simulation Da Nang Viet Nam Cell Nitte Meenakshi Institute of Technology Dept. of Artificial Intelligence and Machine Learning and Ipr Bengaluru India Guru Nanak Dev Engineering College Department of Computer Science and Engineering Ludhiana India

ISBN: (纸本)9798350301595

Wearable body sensors have recently gained popularity because they monitor user behaviors and vital indicators. These sensors are designed to monitor various physiological factors, such as motion, temperature, and heart rate, among others. Making sense of the data gathered by these sensors requires separating the dependent and independent features. The dependent parameters are those that body-worn sensors can read in this scenario. Examples of such elements include blood oxygen levels, heart rate, temperature, mobility, and airflow. These sensors capture data about a person's activity and health. The independent parameters in wearable body sensors are related to the dependent parameters, whereas the dependent parameters are those that the sensors can measure directly. Such characteristics include age, sex, weight, and environmental influences. For example, humidity and airflow might impair a temperature sensor's accuracy. The user's age or level of physical fitness may also influence the accuracy of a heart rate monitor. Activity monitors are used in this study to capture the actions of various people. Static and dynamic properties are derived from the activity monitor recording. The performance evaluations of the eight distinct machine-learning models that were created using the retrieved features are discussed in this paper. The Grid Search method is used to enhance the performance of the XGB model. © 2023 IEEE.

关键词： Heart

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A Novel Image Encryption Optimization Technique

A Novel Image Encryption Optimization Technique

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2021 International Conference on Forensics, Analytics, Big Data, Security, FABS 2021

作者： Gollagi, Shantappa G Srividya, R Santhosh Kumar, G Pareek, Piyush Kumar Hirasugar Institute of Technology Computer Science and Engineering Nidasoshi India Cmr Institute of Technology Information Science and Engineering Bengaluru India East West College of Engineering Bengaluru India Nitte Meenakshi Institute of Technology Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665420051

Image file confidentiality is a difficult issue these days for a variety of reasons in a range of industries, including medical, defence, academia, and many more. A range of practical ways have been studied in recent years to safeguard image data, however owing to technological improvements, such existing solutions have significant limits in current systems. Several academics have investigated a number of methodologies and strategies for maintaining picture secrecy and shielding top-secret information from attackers while images are sent via communications infrastructure. In this paper, an unique picture encryption technique based on a chaotic map and DNA encoding is jointly used for image encryption in a more pragmatic approach, as necessary in today's world to safeguard private data. This approach was tried and confirmed on a personal laptop with a 64-bit operating system, Windows 10, with 16 GB RAM and the MATLAB R2020a version loaded (Random Access Memory). The results of this suggested approach reflect the best NPCR (Number of Pixel Change Rate) and UACI (Unified Average Changed Intensity) scores for a variety of selected photos for testing. This method works with a variety of image formats and is in great demand in today's world for safeguarding sensitive data or information while being transmitted across a communications infrastructure. More research is needed in the future to identify practical answers, as current communication networks are quickly changing throughout the world owing to technology breakthroughs. © 2021 IEEE.

关键词： Cryptography

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