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International Conference on Computing and Networking Technology (ICCNT)

作者： Afnaan. K K. V Nagaraja Tripty Singh Bhanu Prakash KN Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Mathematics Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Principal Investigator Research Scientist Clinical Data Analytics and Radiomics Bioinformatic Institute (BII) A*STAR Singapore

computer tomography (CT), also known as computed tomography, is a medical imaging method that generates detailed and precise horizontal or axial images of targeted regions of the body for diagnostic purposes. In this particular study, the focus was on performing image classification of CT scan images of the Kidney. This organ plays an essential role in detoxification, fluid balance, and maintaining electrolyte levels in the human body. The dataset used for the study contained 12,446 images into four labeled classes - Cyst, Tumor, Stone, and Normal. Convolution Neural Network (CNN) model is built for binary (Normal and Tumor) and multi-image Classification to classify the Renal CT images into four classes. CNN uses ReLU as an activation function for classifying these images. The Model is evaluated and compared based on Accuracy and Loss performance metrics by compiling the CNN model with different deep learning TensorFlow Keras optimizers for Version 2.11.0 for Binary Image Classification at 10,20, and 50 epochs. The CNN model is assembled with Adam Optimizer and Follows the Regularized Leader (FTRL) Optimizer. The Testing and training Accuracy and Losses achieved are evaluated with a learning rate of 0.001 and 0.0001. Adam Optimizer outperforms to be the best Optimizer for Binary and Multi-Image Classification.

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GPTR: Gestalt-Perception Transformer for Diagram Object Detection

arXiv

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arXiv 2022年

作者： Hu, Xin Zhang, Lingling Liu, Jun Fan, Jinfu You, Yang Wu, Yaqiang Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University China Department of Control Science and Engineering Tongji University Shanghai China Department of Computer Science National University of Singapore Singapore Lenovo Research Beijing China

Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its visual features are sparser than those of natural images. In addition, diagrams usually express diverse knowledge, in which there are many low-frequency object categories in diagrams. These lead to the fact that traditional data-driven detection model is not suitable for diagrams. In this work, we propose a gestalt-perception transformer model for diagram object detection, which is based on an encoder-decoder architecture. Gestalt perception contains a series of laws to explain human perception, that the human visual system tends to perceive patches in an image that are similar, close or connected without abrupt directional changes as a perceptual whole object. Inspired by these thoughts, we build a gestalt-perception graph in transformer encoder, which is composed of diagram patches as nodes and the relationships between patches as edges. This graph aims to group these patches into objects via laws of similarity, proximity, and smoothness implied in these edges, so that the meaningful objects can be effectively detected. The experimental results demonstrate that the proposed GPTR achieves the best results in the diagram object detection task. Our model also obtains comparable results over the competitors in natural image object detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Received Signal Strength Prediction Using Generative Adversarial Networks for Indoor Localization

Received Signal Strength Prediction Using Generative Adversa...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Haochang Wu Hao Qin Siteng Ma Hans-Dieter Lang Xingqi Zhang School of Electrical and Electronic Engineering University College Dublin Dublin Ireland Insight Centre for Data Analytics School of Computer Science University College Dublin Dublin Ireland OST Eastern Switzerland University of Applied Sciences Rapperswil SG Switzerland Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

The popularity of WiFi networks has led to the adoption of fingerprint-based WiFi localization as one primary method for indoor location tracking. However, this technique requires a significant amount of time and effort to collect data at numerous reference points (RPs) to ensure accuracy. To reduce the cost and improve efficiency, generative models can be used to generate received signal strength (RSS) fingerprints. This paper proposes a Deep Convolutional Generative Adversarial Network (DCGAN) based model for building RSS fingerprint maps that can generate a comprehensive fingerprint database using only the location of a wireless access point. The proposed approach is expected to lower the cost and effort involved in the collection of RSS fingerprints while maintaining a high level of accuracy.

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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Semantic Segmentation for Image Understanding

Semantic Segmentation for Image Understanding

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International Conference on Computing and Networking Technology (ICCNT)

作者： N. Thangarasu Jagmeet Sohal B Umamaheswari Jagtej Singh R Manjunatha Ganesh C. Shelke Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Data analytics and Mathematical Sciences School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Information Technology Vishwakarma Institute of Information Technology Pune INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Semantic segmentation is a way of photo knowhow that entails labeling the objects in a picture on the pixel degree and assigning every pixel within the photograph a category label. This method is utilized in PC imaginative and prescient programs to construct a representation of a scene from digital photographs. It may be used to correctly phase discrete items consisting of vehicles, pedestrians, and timber, as well as more summary standards, which include shadows and reflections. On the way to achieve success, semantic segmentation algorithms require a large amount of designated labeling and accurate pixel-degree statistical analysis. Latest advances in deep mastering strategies have allowed for the improvement of more sophisticated algorithms that could achieve incredible effects on hard-picture segmentation datasets. Those strategies have progressed the accuracy and efficiency of the photograph know-how algorithms, allowing them to carry out duties that include scene understanding, item identification, scene type, and pixel-stage segmentation.

关键词： Accuracy Pedestrians Statistical analysis Semantic segmentation Reflection Labeling Standards

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Augmenting Cybersecurity in WSN: AI-Based Clone Attacks Recognition Framework

Augmenting Cybersecurity in WSN: AI-Based Clone Attacks Reco...

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Communication and Networks (ASIANComNet), Asian Conference on

作者： Seelam Ch Vijaya Bhaskar Gedela Kalyani Ramy Al-Fatlawy Fatima Alsalamy Waleed Hadi Madhloom Kurdi H.M. Al-Aboudy G. Sujatha Department of IT MVSR Engineering College Hyderabad Telangana India Big Data Analytics Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of Computer Technical Engineering College of Technical Engineering The Islamic University Najaf Iraq Medical Laboratories Techniques Department Al-Mustaqbal University Hillah Iraq Department of Computer Science Altoosi University College 2 Iraq Department of Computer Techniques Engineering Mazaya University College DhiQar Iraq Department of ECE Arunai Engineering College Tiruvannamalai Tamil Nadu India

ISBN: (数字)9798350367003

ISBN: (纸本)9798350367225

Applications such as industrial automation, healthcare, and environmental monitoring need the use of wireless sensor networks (WSNs). However, due to their dispersed organizational makeup, they have become vulnerable to security risks, particularly clone assaults. To protect confidentiality, availability, and confidentiality, several attacks must be recognized and prevented. This project aims to offer an effective method for identifying and averting clone assaults. To identify cloned both nationally and internationally use a low-cost verification process. In this study, we offer a new adaptive sea-horse optimized light gradient boosting machine (ASHO-LGBM) technique for protecting the network against node identity duplicates. The ASHO approach is used in the ASHO-LGBM framework to improve the recognition accuracy of the light gradient boosting machine (LGBM) characteristics. The replications with the nodes intrusion detection (ID) are used to choose a most trustworthy communication mode. The procedure is intended to be implemented and used for gathering data through an internet component. Using a Python tool, the suggested technique is simulated and its delay, packet delivery ratio, packet drop, and energy are evaluated. When compared to other approaches, the study’s results show that the ASHO-LGBM strategy’s performance analysis achieves the highest accuracy rate.

关键词： Wireless sensor networks Accuracy Cloning Intrusion detection Medical services Boosting Delays Performance analysis Environmental monitoring Manufacturing automation

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Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series

arXiv

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arXiv 2024年

作者： Altin, Mahsun Cakir, Altan Department of Computer Engineering Istanbul Technical University Sariyer Istanbul34467 Turkey Department of Physics Engineering Istanbul Technical University Sariyer Istanbul34467 Turkey Artificial Intelligence Data Science Research and Application Center Istanbul Technical University Sariyer Istanbul34467 Turkey Parton Big Data Analytics and Consulting ITU Ariteknokent Sariyer Istanbul34467 Turkey Adin.Ai WilmingtonDE19806 United States

This paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models. The study involves a comprehensive evaluation across three different datasets: MSL, SMAP, and SWaT. Each dataset poses unique challenges, allowing for a robust assessment of the models’ capabilities in varied contexts. The dimensionality reduction techniques examined include PCA, UMAP, Random Projection, and t-SNE, each offering distinct advantages in simplifying high-dimensional data. Our findings reveal that dimensionality reduction not only aids in reducing computational complexity but also significantly enhances anomaly detection performance in certain scenarios. Moreover, a remarkable reduction in training times was observed, with reductions by approximately 300% and 650% when dimensionality was halved and minimized to the lowest dimensions, respectively. This efficiency gain underscores the dual benefit of dimensionality reduction in both performance enhancement and operational efficiency. The MUTANT model exhibits notable adaptability, especially with UMAP reduction, while the Anomaly-Transformer demonstrates versatility across various reduction techniques. These insights provide a deeper understanding of the synergistic effects of dimensionality reduction and anomaly detection, contributing valuable perspectives to the field of time series analysis. The study underscores the importance of selecting appropriate dimensionality reduction strategies based on specific model requirements and dataset characteristics, paving the way for more efficient, accurate, and scalable solutions in anomaly detection. © 2024, CC BY.

关键词： Anomaly detection

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AI based Stock Market Analysis and Decision Making System using Design Thinking Approach

AI based Stock Market Analysis and Decision Making System us...

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International Conference on Inventive Systems and Control (ICISC)

作者： M.Venkatesh Kumar M. Umamaheswari C. Hema Bharathi R. Maruthaveni M.Nirmala Devi R. Prasanna Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Business Administration Dr. SNS Rajalakshmi College of Arts and Science Coimbatore Tamilnadu India Department of Physical Science and Information Technology Tamil Nadu Agricultural University Coimbatore Tamilnadu India Department of Computer Science with Data Analytics Dr. SNS Rajalakshmi College of Arts and Science Coimbatore Tamilnadu India Department of Management Studies Dr. SNS Rajalakshmi College of Arts and Science Coimbatore Tamilnadu India

ISBN: (数字)9798350386578

ISBN: (纸本)9798350386585

Stock market is a dynamic and ever-changing environment that can be both exciting and challenging for investors. Equities, primarily referred to as stocks, are traded on stock exchanges around the world and reflect ownership in a business. The stock market is an essential tool for companies to raise capital, and investors use it to grow their wealth. Investing in stocks requires a thorough understanding of the market and the individual companies in which you are interested. The financial health of a company must be taken into account and can be analyzed using financial statements and other data. Another critical factor is the overall economic climate, as market conditions can significantly impact stock prices. It’s crucial to have a long-term outlook and avoid being influenced by momentary market changes when making equity investments. A random walk is a mathematical model used to describe a sequence of steps or movements where each step is determined by chance. It is often used in various fields, including physics, finance, and statistics. Random walk theory has important implications in various fields, including the efficient market hypothesis in finance and the modeling of diffusion processes in physics. To effectively predict the future stock price using conventional and modern machine learning models. Compare the performance of different models and identify a higher accuracy prediction model using the selected important factors from disparate data sources.

关键词： Deep learning Accuracy Biological system modeling Finance Companies Predictive models Mathematical models

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Classification of cow diet based on milk Mid Infrared Spectra: A data analysis competition at the "International Workshop on Spectroscopy and Chemometrics 2022"

Classification of cow diet based on milk Mid Infrared Spectr...

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作者： Frizzarin, Maria Visentin, Giulio Ferragina, Alessandro Hayes, Elena Bevilacqua, Antonio Dhariyal, Bhaskar Domijan, Katarina Khan, Hussain Ifrim, Georgiana Nguyen, Thach Le Meagher, Joe Menchetti, Laura Singh, Ashish Whoriskey, Suzy Williamson, Robert Zappaterra, Martina Casa, Alessandro Teagasc Animal & Grassland Research and Innovation Centre Moorepark Ireland School of Mathematics and Statistics University College Dublin Ireland Department of Veterinary Medical Sciences University of Bologna Italy Teagasc Food Research Centre Ashtown Ireland Teagasc Food Research Centre Moorepark Ireland School of Computer Science University College Dublin Ireland Department of Mathematics and Statistics National University of Ireland Maynooth Ireland Insight Centre for Data Analytics University College Dublin Ireland School of Biosciences and Veterinary Medicine University of Camerino Italy School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Department of Agricultural and Food Sciences University of Bologna Italy Faculty of Economics and Management Free University of Bozen-Bolzano Italy

In April 2022, the Vistamilk SFI Research Centre organized the second edition of the "International Workshop on Spectroscopy and Chemometrics – Applications in Food and Agriculture". Within this event, a data challenge was organized among participants of the workshop. Such data competition aimed at developing a prediction model to discriminate dairy cows’ diet based on milk spectral information collected in the mid-infrared region. In fact, the development of an accurate and reliable discriminant model for dairy cows’ diet can provide important authentication tools for dairy processors to guarantee product origin for dairy food manufacturers from grass-fed animals. Different statistical and machine learning modelling approaches have been employed during the workshop, with different pre-processing steps involved and different degree of complexity. The present paper aims to describe the statistical methods adopted by participants to develop such classification model. © 2023

关键词： Machine learning

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Effective Hybrid Precoder Logic with mm Wave Massive MIMO using Novel Deep Learning Scheme

Effective Hybrid Precoder Logic with mm Wave Massive MIMO us...

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： Ranjith S P Jesu Jayarin A. Chandra Sekar Department of Electronics and Communication Engineering Jeppiaar Engineering College Chennai Tamilnadu India Department of Data Analytics Institute of IT Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences(SIMATS) Chennai India Department of Computer Science Engineering St. Joseph’s College of Engineering Chennai Tamilnadu India

For establishing a high efficient signal communications, a Millimeter-Wave-Massive-MIMO (mmWave-MMIMO) logics are helpful and more speculated. Although current hybrid precoding schemes are computationally complex, however, they are not using all of the spatial details. The combination of analog and digital precoding logics are considered to be a significant methodology to deal with complexities over hardware area and the higher consumption of energy in association with combination of signal elements. But the basic degradation of the traditional hybrid-precoding methodologies has some issues such as processing complexities and has a lacking to explore the spatial data regarding the channels. To address these limitations, this paper suggests a Novel Deep Learning Scheme (NDLS), in which it is enabled by mmWave-MMIMO system for successful Hybrid Precoder Logic (HDL). As well as each Precoder selection is used to obtain the The optimised-decoder is perceived in the Novel Deep Learning Scheme as a mapping relation. To be more precise, a Hybrid Precoder Logic is extracted via Novel Deep Learning Scheme training for optimizing the Millimeter- Wave-Massive-MIMO precoding method. Additionally, a detailed simulation results are presented to demonstrate the proposed scheme's superior efficiency and the resulting section demonstrate that the Novel Deep Learning Scheme based methodology is competent of lowering the Bit-Error- Rate (BER) and increasing the effectiveness of the frequency of Millimeter-Wave Massive-MIMO (mmWave- MMIMO), gaining a competitive advantage in faster convergence as compared to traditional schemes while significantly reducing the necessary computational difficulty.

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