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Printable Fused Silica Based Microchamber Integrated With Graphene Chemi-Resistive Sensors For Direct On-Chip Soil Testing

Printable Fused Silica Based Microchamber Integrated With Gr...

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2023 IEEE Applied Sensing Conference, APSCON 2023

作者： Jose, Sabitha Ann Atwa, Yahya Mitchell, Neil Shakeel, Hamza Queen's University Belfast School of Electronics Electrical Engineering and Computer Science Belfast United Kingdom Umm Al-Qura University Dept. Electrical Engineering Makkah Saudi Arabia

ISBN: (纸本)9781665461634

In this study, we demonstrate a graphene-based portable gas sensor with an integrated fused silica micro chamber for direct on-chip sample injection to detect a low concentration (-10 ppm) of acetone from a soil sample at room temperature and pressure. A chemiresistive gas sensor was first fabricated by a conformal transfer process using a commercial monolayer graphene film. Printable fused silica micro gas chamber (internal volume of 50 microliter), fabricated separately, was permanently attached with the graphene chemiresistor using an ultra-violet light source. A relative resistance change of 1% (-20 ppm of acetone) was achieved by injection of a headspace sample for acetone calibration. This direct on-chip injection method shows an excellent potential for detecting volatile organic compounds from a soil sample without requiring complex sample extraction steps © 2023 IEEE.

关键词： Fused silica

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Predicting Mobile Prices with Machine Learning Techniques

Predicting Mobile Prices with Machine Learning Techniques

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Computational Intelligence and Computing Applications (ICCICA), International Conference on

作者： Deepak Singh Rana Shiv Ashish Dhondiyal Sumeshwar Singh Sanjeev Kukreti Ashish Dhyani Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Jaypee Institute of Information Technology Department of Hospitality Management Graphic Era Deemed to be University Dehradun Uttrakahnd

ISBN: (数字)9798350306446

ISBN: (纸本)9798350306453

Smartphones are increasingly vital to people on a daily basis. Telephones are utilized in all aspects of life, ranging from personal to professional, due to technological advancements. It serves a function beyond making phone calls. It enables internet connectivity and email reading. when not using the computer. The characteristics of a mobile phone are a crucial consideration when buying *** overall objective of this research is to find the best way to apply machine learning to estimate the retail pricing of smartphones based on their individual specs. Individuals who frequently use their phone are more attentive to selecting features. When purchasing a cell phone, a comparison is done based on the price-performance ratio. Phone features are regarded as performance. This research aims to forecast if mobile phones with certain features are considered economical or *** work is capable of being utilized in various marketing and business contexts to assist in making informed purchasing decisions by maximizing features while minimizing costs.

关键词： Support vector machines Machine learning algorithms Costs Machine learning Pricing Predictive models Prediction algorithms

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Hybrid Software Reliability Prediction Model Using Feature Selection and Support Vector Classifier 4

Hybrid Software Reliability Prediction Model Using Feature S...

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4th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2022

作者： Rath, Suneel Kumar Sahu, Madhusmita Das, Shom Prasad Mohapatra, Srikanta Kumar C. V. Raman Global University Dept. of Computer Science Engineering Odisha Bhubaneswar India Birla Global University Dept. of Computer Science Engineering Odisha Bhubaneswar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665400732

The primary purpose of the software industry is to provide high-quality software. Software system failure is caused by faulty software components. The goal of reliable software is to reduce the amount of software programme failures. Software defect prediction is a crucial aspect of developing high-quality software. One can predict software failures by implement essential prediction metrics and previous fault information. A good software fault prediction model makes testing easier while also improving the quality and consistency of software. For defect prediction systems based on diverse parameters, several methodologies have been proposed. However, none of the models meet the criteria for software reliability defect prediction. So in this article we proposed a hybrid software reliability model using feature selection and support vector classifier. In terms of software reliability defect prediction, the provided methodology is acceptable for different software metrics with experimental approvals utilizing a standard dataset. In the methodology, the NASA Metrics Data Program datasets are used for real-time verification and validation. © 2022 IEEE.

关键词： Forecasting

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Optimizing Convolutional Neural Network Hyperparameters for MNIST Image Classification: Insights and Implications 2

Optimizing Convolutional Neural Network Hyperparameters for ...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Dar, Eman Rana, Khadija Yousaf, Hoor Fatima Asif, Talha Hussain, Muhammad Zunnurain Ijaz, Hussain Hasan, Muhammad Zulkifl Dept. of Computer Engineering Information Technology University Lahore Pakistan Bahria University Lahore Campus Pakistan NCBA&E DHA Campus Pakistan Dept. of Computer Science Bahria University Lahore Campus Pakistan Cloud Engineer Information Technology University Pakistan Researcher Cyber Security Center Universitie Putra Malaysia Malaysia

ISBN: (纸本)9798331541217

The recognition of handwritten digits has been among the most enduring fundamental problems explored in the field of machine learning and computer vision. The objective of this work is to design a state-of-art Convolutional Neural Network (CNN) for the image classification on the MNIST dataset to resolve issues related to data pre-processing and parameter tuning effectively. The work starts with standardizing the image data and then using one-hot encoding for the models that will improve the generalization and representation. The CNN structure followed in this work includes the convolutional layer, batch normalization, pooling layers, and fully connected layers with an efficient integration of features. To measure such performance indicators as accuracy, the given dataset is divided for training and validation. The results are also preprocessed to control for overfitting and convergence using back propagation with early stop to stop training when significant accuracy improvement is not observed and learning rate decay to encourage the model to search for better weights in the final stages of training. The model's performance is tested and evaluated by using different epoch intervals and learning rates, and it is shown how critical it is to select the correct default hyperparameters for achieving efficiency. This paper also presents the approach for the practical application of the preprocessing of images and the systematic assessment in enhancing CNN-based handwritten digit recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Text Filtering Insignificant Word (TFIW) Readability Framework Model to Explain NLP in Sentiment Analysis Using Reinforcement Machine Learning

Text Filtering Insignificant Word (TFIW) Readability Framewo...

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Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： Rajesh Kandakatla V Dhilip Kumar dept. Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai INDIA dept. Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hyderabad INDIA

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

Reading of a text in a given sentence have lot of parameters to consider for expressing an opinion on a given text to conclude nature of the text. The data which need to be formed in the form of data sets based on specific constrains. The Natural Language Processing (NLP) will always presume that Text readability made as the considerable measure for better round spread of any newspaper. The data related to these documents must be collected and store them in data sets. During this process of measuring text readability at the granular level of the text reading is a cumbersome task. This will always implicate a problem of machine readability of the text and explainability of the text taken from the documents mentioned above. In this context human readability will act an important role in predicting the text reading at granular level where the text-based sentiment analysis have been predicted through a machine learning model. The text reading has been evaluated in order to improve the readability through explainable system. The explainable systems will capture and explain the predictions of text reading. In this technical work an explainable system Text Filtering Insignificant Word (TFIW) is introduced where it improves the textual reading at the granule level based on sentiments by prediction the words which need to be filtered. This will improve the explainability of text and text reading and improve the fast text and explainability of the document.

关键词： Sentiment analysis Analytical models Filtering Computational modeling Machine learning Predictive models Market research Feature extraction Microelectronics Context modeling

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Plant Disease Detection Using Machine Learning 2

Plant Disease Detection Using Machine Learning

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Bagban, Shifa Aiman Hosur, Ravi Patil, Sahebgouda BLDEA's V.P.Dr. P. G Halakatti College of Engineering & Technology Dept. of Computer Science & Engineering Karnataka Vijaypura India Karnataka Vijaypura India

ISBN: (纸本)9798350308846

Agricultural productivity is the factor on which many countries' economies heavily rely on. Identifying plant diseases is extremely crucial in the agricultural sector as they can hamper the plant's robustness and health which is important in agricultural productivity. These obstacles are regular in plant production ,and if adequate procedures of prevention are not followed it may inversely affect the cultivation. Farmers are confronting financial loss due to various crop diseases. The traditional method of disease detection is achieved by the opinion of an expert and physical inspection, which takes much time and is expensive in the practical life. And when the cultivated area is big in acres, it becomes very tough for the cultivators to track the crops periodically. That's why an automated computer based disease detection system has become essential. The system developed in this project involves using image processing techniques such as opencv2 that are designed using python to segment the disease part from the leaf while using machine learning techniques like SVM and CNN to categorize the leaves of the plant as healthy or infected. For classification purposes, morphological characteristics and properties such as colour, strength and measurements of the plant leaves are taken into account. © 2023 IEEE.

关键词： Agriculture Canny edge detection Convolution neural network Cultivation Support Vector machine

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Kannada Word Detection in Heterogeneous Scene Images 10

Kannada Word Detection in Heterogeneous Scene Images

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Shikha, N. Pranav, R. Singh, Nidhi R Umadevi, V. Hussain, Muzammil B.M.S. College of Engineering Dept. of Computer Science Bengaluru India B.M.S. College of Engineering Dept. of Electrical and Electronics Bengaluru India B.M.S. College of Engineering Dept. of Medical Electronics Bengaluru India Tika Data Services Pvt. Ltd. Bengaluru India

ISBN: (纸本)9781665490993

Text extraction from scene images has started gaining a lot of traction in recent years in the computer vision field as its applications is manifold. One of its sub-categories is scene text detection. Factors like complex backgrounds, curvature, orientation, image quality, and various font styles and sizes makes it a difficult task. Moreover, building a single language scene text detector in a multilingual setting, especially Indian scripts, is more challenging in contrast to a general text detector. In this Work We attempt the task of Kannada text localization as Well as Word detection in natural scenes, Which is underexplored in comparison to other Well-known Indian languages. To achieve this, We fine tune a You Only Look Once (YOLOv4) based object detection model on our Kannada scene text images dataset collected. Experiments on local boards and sign images captured on smartphone, showed a mean average precision of 93.5% and per image average detection speed of 30 milli-seconds;pointing towards real time use and integration With mobile applications. © 2023 IEEE.

关键词： Object detection

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Towards Non-Invasive Monitoring of Human Bio-physiological Parameters Using an RF-Panel

Towards Non-Invasive Monitoring of Human Bio-physiological P...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2022

作者： Rahman, Md Mirazur Ashif Islam Oni, Md Wolf, Henry Mitra, Dipankar Dey, Shuvashis North Dakota State University Dept. of Electrical and Computer Engineering FargoND United States University of Wisconsin-La Crosse Dept. of Computer Science WI United States

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

This paper presents a novel RF-panel to monitor multiple human physiological parameters non-invasively and accurately without requiring any complex arrangements. The proposed panel consists of three resonators coupled with a transmission line. A finger model was placed on and in between the resonators and the responses of the resonators were observed. The results indicate that there will be a clear correlation between the responses received and different physiological aspects intended to be measured. This outcome also helps envision that the incorporation of smart sensing materials and machine learning based analysis will make this RF panel capable of sensing physiological parameters by just placing a finger on any position of the panel. © 2022 IEEE.

关键词： Resonators

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Selective Attention UNet for Segmenting Liver Tumors

Selective Attention UNet for Segmenting Liver Tumors

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Patil, Darshan Sakarkar, Gopal Khatri, Pearl Khedkar, Atharva Gan, Hong-Seng Ramlee, Muhammad Hanif G. H. Raisoni College of Engineering Dept. of Artificial Intelligence Nagpur India Dr. Vishwanath Karad Mit World Peace University School of Computer Science Kothrud Pune India Banasthali Vidyapith Dept. of Computer Science Jaipur India Xi'an Jiaotong-Liverpool University School of Ai and Advanced Computing Suzhou China Universiti Teknologi Malaysia Dept. of Biomedical Engineering Johor Malaysia

ISBN: (纸本)9798350312492

In order to segregate liver tumours in medical imaging applications, a novel architecture called Selective Attention UNet is proposed in this article. The suggested architecture, which is based on the well-known UNet architecture, has a selective attention module that enables the network to concentrate on crucial tasks while suppressing unnecessary ones. Link skipping between the encoder and decoder routes is another element of the design that enables the network to effectively segment data using both low-level and high-level attributes. On the publicly accessible LiTS dataset, we assessed the performance of the suggested architecture and contrasted it with four fundamental models: FCN, UNet, UNet++, and SegNet. The Dice Similarity Coefficient (DSC) of 0.89 a mean IOU of 0.76 obtained in our experiments demonstrates that the suggested architecture beats all baseline models in terms of accuracy and robustness criteria. The project is accessible at: https://***/darshan8850/Liver-tumor-Segmentation © 2023 IEEE.

关键词： computer vision

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Recurrent Graph Neural Network-Based Identification of Replay Attacks in Power Networks 4

Recurrent Graph Neural Network-Based Identification of Repla...

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4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Joad, Faaiz Atat, Rachad Mbayed, Hayat Takiddin, Abdulrahman Florida State University Dept. of Electrical & Computer Engineering TallahasseeFL United States Lebanese American University Computer Science Department Beirut Lebanon Florida State University Center for Advanced Power Systems TallahasseeFL United States

ISBN: (纸本)9798350391183

As cyber-physical systems, power networks exchange data to ensure smooth operation. Through communication networks, the exchanged data becomes vulnerable to cyberattacks where malicious entities establish malevolent connections to falsify the data, which exploit the data integrity, leading to network instability. Machine learning-based solutions have been proposed to identify such malevolent actions, including false data injection attacks. This paper aims to (a) enhance the attack detection performance and (b) quantify the performance of non-recurrent and recurrent machine learning-based attack detection systems against multiple replay attacks that involve falsifying data via presenting normal operation patterns from previous timestamps. It turns out that capturing temporal aspects from the data using recurrent models outperforms non-recurrent models by 5-32% in accuracy (ACC). Specifically, we propose a recurrent graph neural network (RGNN) model that captures the temporal and spatial aspects of the network data. The RGNN model outperforms non-recurrent models by 14-32% and other recurrent models by 6-13% in ACC. The experiments are conducted on IEEE 14, 39, and 118-bus power networks where the model performance is enhanced (by up to 8% in ACC) in larger systems due to capturing more spatial and temporal features. © 2024 IEEE.

关键词： Cyber attacks

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