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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Bindra, Sandeep Singh Aggarwal, Alankrita Ait Chandigarh University Computer Science & Engineering Department Punjab India

ISBN: (纸本)9798350306446

Today's key industries, including energy, healthcare, and transportation, rely heavily on Cyber-Physical Systems (CPS) as their foundation. All the same, these systems are seriously threatened by the wide range and growing complexity of cyberattacks. Traditional security measures typically fail when identifying intricate, multi-phase, and unique threats. This research presents a deep learning-based approach to improve CPS's resistance against cyber threats. This work aims to detect multiple network attackers with distinct attack natures and provide a novel hybrid architecture. To analyze the performance of the proposed architecture, a publicly available dataset OPC UA is used, which contains Denial of Service (DoS), Man-in-the-middle (MITM), and spoofing attack data. The performance is compared with existing detection frameworks. The findings show a notable increase in detection accuracy over current approaches, underscoring the promise of deep learning strategies in protecting CPS against cyberattacks. This research contributes to cyber-physical security and provides valuable information for implementing intelligent attack detection systems in CPS. © 2024 IEEE.

关键词： Cyber Physical System

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Survivalence of Rouge Wild Animals Using Image Processing and IOT 1

Survivalence of Rouge Wild Animals Using Image Processing an...

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1st IEEE International Conference on Advances in Information Technology, ICait 2019

作者： Harish, S.S. Rao, Sudesh Chethan, P. Chandra Naik, G. AIT Department of Computer Science and Engineering Chikkamagaluru Karnataka India SSE Department of Computer Science and Engineering Mukka Karnataka India

ISBN: (纸本)9781728132419

Human and Wild creature strife is consistently expanding issue in the regions of Forest zone and farming field which results in huge loss of assets and may put human life at risk. So this zone must be checked to keep the creatures off or to prevent any undesirable interruption. In our proposed undertaking, we have structured a framework which identifies the movement of creatures by utilizing PIR sensors, then camera will take picture of interruption and the Picture is sent to the Picture handling sensors where it is characterized as a creature or a human using content based image classification. In case of bogus alert no advances are prepared in order to solve the issue. We have come up with a new method in which we can revert the animal by using IOT and Image Processing technique. In our proposed method we have used sensor which will identify the moments and the image taken will be compared to identify the animals and at the same time message to be sent to the farmers. © 2019 IEEE.

关键词： Animals

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A novel, lightweight, and cost-effective mechanism to secure the sensor-gateway communication in IoT 7th

A novel, lightweight, and cost-effective mechanism to secure...

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7th computer science On-line Conference, CSOC 2018

作者： Patil, Shamshekhar S. Sunitha, N.R. Department of Computer Science and Engineering Dr. AIT Bengaluru India Department of Computer Science and Engineering SIT TumkurKarnataka India

ISBN: (纸本)9783319911915

Ensuring highest degree of resistance against potential adversaries in Internet-of-Thing (IoT) is still an open challenge, and the prime reason behind this is the computational complexities associated with designing security algorithms and its corresponding transformation between the cloud and Wireless Sensor Network (WSN) via gateway node. Hence, the proposed system develops a lightweight and highly responsive encryption technique to offer minimal resource consumption from the resource-constraint sensor nodes. The significant contribution is also to introduce a novel bootstrapping key mechanism with the unique generation of the secret key to maintain both forward and backward secrecy. The study outcome shows that proposed system is highly practical to offer reduced resource consumption and faster algorithm processing time in the presence of dynamic scenario of IoT. © 2019, Springer International Publishing AG, part of Springer Nature.

关键词： Internet of things

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Enhancing Quadcopter Navigation with Energy-Efficient Obstacle Avoidance Using the KNN Algorithm and Distance Deviation 15

Enhancing Quadcopter Navigation with Energy-Efficient Obstac...

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

作者： Singh, Nongmeikapam Thoiba Paliwal, Girish Shrimal, Vijay Mohan Datta, Parul Wadhwa, Manoj Chauhan, Kunal Kumar, Sonu Chandigarh University Department of Computer Science And Engineering Punjab India Chandigarh University AIT- Computer Science And Engineering Punjab India

ISBN: (纸本)9798350370249

One particular kind of UAV that is frequently employed for searching and rescue, reconnaissance, and other purposes is the quadcopter. To reduce the amount of human oversight during quadcopter navigation, an obstacle avoidance system is required. With obstacle dimension information, obstacle avoidance systems can be designed to select avoidance paths. Energy efficiency must be taken into account while determining the avoidance direction of quadcopter flights due to power limits. The obstacle avoidance system in this study assesses an object's dimensions, energy consumption, and distance from the quadcopter before determining which direction to avoid it-upward, leftward, or rightward. The quadcopter navigates in a three-dimensional environment. With 98.7% accuracy and 0.0062 s of computing time, KNN (K-Nearest Neighbor) generates effective avoidance judgments. According to the simulation, the quadcopter may arrive at the desired location without running into any stationary obstructions. When impediments are detected, the quadcopter can also select the most effective avoidance direction. © 2024 IEEE.

关键词： avoidance direction KNN machine learning obstacle avoidance quadcopter unmanned aerial vehicle

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A comparison of audio and tactile displays for non-visual target selection tasks

A comparison of audio and tactile displays for non-visual ta...

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1st International Conference on User science and engineering 2010, iUSEr 2010

作者： Eakachai Janecek, Paul Dailey, Matthew N. Suchato, Atiwong Charoenchaimonkon Department of Computer Science and Information Management AIT Pathumthani 12120 Thailand Department of Computer Science and Information Management AIT Pathumthani 12120 Thailand Department of Computer Engineering Chulalongkorn University Bangkok 10330 Thailand

ISBN: (纸本)9781424490493

This paper presents the results of a series of three experiments carried out to evaluate the speed and accuracy of target acquisition tasks using visual, auditory, or tactile feedback modalities. Ten participants conducted a series of 1-D and 2-D pointing tasks using a pen-based input device and either visual, audio or tactile (vibratory) feedback. Fitts' Law was used to analyze the results. Overall, participants generally performed better with tactile displays than auditory displays, and were able to perform 1-D pointing tasks with auditory and tactile displays nearly as well as with visual displays. However, pointing in 2-D is significantly slower and more error-prone with non-visual displays. ©2010 IEEE.

关键词： User interfaces

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A Framework for Encryption and Decryption Using Image Steganography 1

A Framework for Encryption and Decryption Using Image Stegan...

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1st International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2023

作者： Maram, Balajee Chandigarh University AIT Department of Computer Science and Engineering Gharuan Punjab Mohali140055 India

ISBN: (纸本)9798350306637

This research introduces a comprehensive framework that integrates encryption and steganography techniques for secure communication using digital photographs. Encryption obscures the content, while steganography hides encrypted data within selected image pixels, imperceptible to human observers. A pseudorandom number generator aids secure pixel selection. Retrieving the concealed message involves steganography's data extraction followed by decryption using the appropriate key. This approach safeguards sensitive data in various applications, ensuring the confidentiality and authenticity of digital communication. © 2023 IEEE.

关键词： Steganography

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Multi-Plant Leaf Disease Detection and Classification using Convolution Neural Network Aad Image Processing

Multi-Plant Leaf Disease Detection and Classification using ...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Aishwarya, S.S. Ravikumar, Pushpa Chaithra, I.V. Sagar, Deeksha AIT Adichunchanagiri Institute of Technology Karnataka Chikmagalur India AIT Adichunchanagiri Institute of Technology Department of Computer Science and Engineering Karnataka Chikmagalur India

ISBN: (纸本)9798350388602

Every nation's basic need is the agricultural products. Disease-ridden leaves have an effect on the nation's agricultural output and financial resources. This research provides a deep learning based plant leaf disease classification and detection method. The website (Plant Village dataset) provided the photos that were used. Since tomatoes, peppers, and potatoes are the most prevalent plant species worldwide and in Iraq specifically, we have used them as examples of certain plant species in our study. There are 20636 photos of plants and their illnesses in this data set. Plant leaf diseases are sorted into 15 class labels in the proposed system using a convolutional neural network (CNN). Of these classes, 12 are dedicated to diseases. Several various plants that were found, including three groups for healthy leaves and various microorganisms including fungus. Consequently, we achieved exceptional precision in both training and testing;specifically, we achieved 98.29% accuracy in training and 98.029% accuracy in testing across all utilized data sets. Pattern recognition is necessary in order to diagnose these disorders. Using remote sensing techniques to investigate multi- and hyper-spectral picture captures is a frequent approach in this situation. Techniques that use this strategy frequently use digital image processing capabilities to accomplish their objectives. There are numerous kinds of pattern recognition algorithms that provide accurate disease detection. Plant diseases are currently detected using principal component analysis and back propagation. These algorithms are acquired by neural network training supervision. © 2024 IEEE.

关键词： Convolutional neural networks

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AutoCov22: A Customized Deep Learning Framework for COVID-19 Detection

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SN computer science 2023年第5期4卷 1-18页

作者： Bhowmik, Biswajit Varna, Shrinidhi Kumar, Adarsh Kumar, Rahul Ishwarchandra Vidyasagar AIT Lab BRICS Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal 575025 India

The novel coronavirus disease 2019 (COVID-19) spill has spread rapidly and appeared as a pandemic affecting global public health. Due to the severe challenges faced with the increase of suspected cases, more testing methods are explored. These methods, however, have several disadvantages, such as test complexity and associated problems—sensitivity, reproducibility, and specificity. Hence, many of them need help to achieve satisfactory performance. Motivated by these shortcomings, this work proposes a custom deep neural network framework named “AutoCov22” that detects COVID-19 by exploiting medical images—chest X-ray and CT-scan. First, multiple neural networks extract deep features from the input medical images, including popularly used VGG16, ResNet50, DenseNet121, and InceptionResNetV2. Then, the extracted features are fed to different machine-learning techniques to identify COVID-19 cases. One objective of this work is to quicken COVID-19 detection. Another goal is to reduce the number of falsely detected cases by a significant margin. Comprehensive simulation results achieve a classification accuracy of 99.74%, a precision of 99.69%, and a recall of 98.80% on exercising chest X-ray images. Extended experiment results in accuracy, precision, and recall up to 87.18%, 84.98%, and 85.66%, respectively, in processing CT-scan images. Thus, the AutoCov22 approach demonstrates a promising and plausible best solution over several methods in the state-of-the-art. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： COVID-19 CT-scan images Deep neural networks False prediction reduction Test accuracy Transfer learning techniques X-ray images

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Correlation Analysis for Determining Effective Data in Machine Learning: Detection of Heart Failure

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SN computer science 2021年第3期2卷 1-5页

作者： Assegie, Tsehay Admassu Sushma, S.J. Bhavya, B.G. Padmashree, S. Department of Computer Science Faculty of Computing Technology AIT Aksum University Aksum Ethiopia Department of Electronics and Communication Engineering GSSS Institute of Engineering and Technology for Women MYSURU Mysore India Computer Science and Engineering Vidyavardhaka College of Engineering Mysore India

Heart disease is one of the causes for death throughout the world. Heart disease cannot be easily identified by the medical experts and practitioners as the detection of heart disease requires expertise and experience. Hence, developing better performing models for heart disease detection using machine-learning algorithms is crucial for detecting heart disease in an early stage. However, employing machine learning algorithm involves determining the relationship between the heart failure dataset features. In this study, correlation analysis is employed to identify the relationship among the heart failure dataset features and a predictive model for heart failure detection is developed with K-nearest neighbor (KNN). Pearson correlation is employed to identify the relationship between the features in the heart failure dataset and the effect of strong correlation to the target feature on the performance of K-nearest neighbor (KNN) model is analyzed. The experimental result shows that highly correlated feature significantly affected the performance of K-nearest neighbor (KNN) for heart failure detection. Finally, the performance of KNN is evaluated and result reveals that the model has acceptable level of performance with highest accuracy of 97.07% on heart failure prediction. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Correlation analysis Heart failure Heart failure detection KNN Machine learning

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Various Current Mirror Topologies: A Survey 2

Various Current Mirror Topologies: A Survey

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Sabharwal, Srishti Kaur, Jaspreet Shahi, Ashima Chandigarh University Electronics and Communication Engineering Department Punjab Gharuan India Ait Chandigarh University Computer Science Engineering Department Punjab Gharuan India

ISBN: (纸本)9781665437899

In most of the analog and mixed mode circuits, current mirror is the fundamental structure of components included in large electronic systems. High performance of analog structures depends on parameters like output resistance and settling time of current mirrors. High operation speed, small area and less power dissipation are characteristics of circuits built using current mode. In this paper, various current mirror topologies are surveyed based on technology of 90nm. The performance parameters of these technologies like output current, impedance, transconductance and transfer characteristics are also discussed. © 2022 IEEE.

关键词： Topology

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