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Medical image enhancement based on histogram algorithms

Procedia computer Science 2019年 163卷 300-311页

作者： Nema Salem Hebatullah Malik Asmaa Shams Assistant Professor Electrical and Computer Engineering Department Effat University Jeddah 21478 KSA

Medical images constitute important information that clinicians need to diagnose and make the suitable treatment decisions. The diagnostic process extremely involves the image visual perception. Unfortunately, the possibility of error existence in perception is not acceptable as it mainly affects the patients’ lives. Image enhancement improves the visual quality of image, helps the clinician in his decision and thus saves the patients’ lives. Histogram is a common tool for improving contrast in medical imaging. It recovers the lost contrast by redistributing the image brightness values that unfortunately may generate undesirable artifacts. Therefore, researchers developed the histogram-based algorithms to overcome this problem. This paper presents a comprehensive study of many histogram-based algorithms. We utilized the powerful MATLAB package to analyze the enhancement performance of these histogram-based algorithms. Moreover, this paper quantitatively compares the results and thus evaluates their performance by three metric parameters, which are the mean square error, standard deviation, and the peak signal to noise ratio.

关键词： Histogram Medical images Enhancement Contrast Metric evaluation parameters Peak signal to noise ratio

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Using Fuzzy Cognitive Maps in Analyzing and Studying International Economic and Political Stability

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IFAC-PapersOnLine 2019年第25期52卷 23-28页

作者： Peter P. Groumpos Emeritus Professor Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras Rion 26504

Challenging Issues of International Economic and Political Stability is considered. Various models describing them are briefly presented and their strong and weak points are considered. Today’s world problem cannot be analysed and understood with present mathematical models. The approach of Fuzzy Cognitive Maps (FCM) is used to model the economic behaviour of a country. A new model is proposed for international affairs using FCM. Two cases are considered, and simulations are conducted. Obtained results are presented and discussed. Future research directions are presented.

关键词： International Stability Fuzzy logic Fuzzy Cognitive Maps Sustainable growth

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Creating New Knowledge through Intelligent & Cognitive Control: Reconsidering Control and Automation Science

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IFAC-PapersOnLine 2019年第25期52卷 551-555页

作者： Peter Groumpos Emeritus Professor Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras Rion

Creating new knowledge is a very complicated process. Understanding the human brain is one of the greatest challenges facing 21st century science. Knowledge acquisition involves complex cognitive processes: perception, communication, and reasoning; while knowledge is also said to be related to the capacity of acknowledgement in human beings. Knowledge is the driving force for all human and physical activities. Different definition of knowledge are provided. Intelligent control (IC) and Cognitive Control (CC) are the main driving forces of creating new knowledge. The concepts of control and automation are re-examined under the combined notion of intelligence and cognition.

关键词： intelligent control cognitive control automation intelligence manufacturing ethical control

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Intelligent Cost-Oriented Manufacturing Applications to Energy Buildings using Fuzzy Experts

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IFAC-PapersOnLine 2019年第25期52卷 523-526页

作者： Groumpos Peter Emeritus Professor Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras Rion 26504

The manufacturing industry faces currently and in the near future due to the information society, very important challenges. New data, network, automation, digital customer interfaces and many new advancements have brought us Industry 4.0. Cost-oriented manufacturing methods and tools play a major role in the smooth and effective operation of an industry. Industry 4.0 or Smart Manufacturing as has been called by some, describes the new era of the “intelligent environment”, in which formerly separate subsystems, components and processes link together through many industrial networking protocols to produce intelligent data and transforms our daily lives. Intelligent cost-oriented concepts and practices are very important for energy savings of a building. The human needs must be taken into consideration when automation is used for Building Energy Management Systems (BEMS).

关键词： cost-oriented manufacturing intelligence energy savings fuzzy experts

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Development of peanut separator and thruster

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IOP Conference Series: Materials Science and engineering 2021年第1期1055卷

作者： M Srinivasan S Nithya Sai Albert Alexander N Prabhakaran C Gokul G Visalaxi Assistant Professor Department of Electrical and Electronics Engineering Kongu Engineering College Erode district-638 060 Tamilnadu India Assistant Professor Department of Electronics and Communication Engineering Kathir College of Engineering Coimbatore district-641 062 Tamilnadu India Student Department of Electrical and Electronics Engineering Kongu Engineering College Erode district-638 060 Tamilnadu India Assistant Professor Department of Electrical and Electronics Engineering Velalar College of Engineering and Technology Erode-638012 India Assistant Professor Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India

Peanuts have been in existence since the beginning of human life. It has a high carbon content and is rich in oil and protein. Peanut oil extraction is more common in most developing countries. Peanut is a commonly used, edible commodity, and oil is used as a cooking medium. Upon oil extraction, the by-product of the peanut cake often contains 43-65 per cent protein and 6-20 per cent fat, which is usually used as feed for cows and goats. There is currently a shortage of farm workers and an increase in harvesting costs. The factors mentioned above mean that farmers are not interested in growing peanuts. The removal of peanut shells is a time-consuming and costly operation. During harvesting, the temperature is very high, making it impossible for humans to work. Peanut thresher is used to solve these problems by removing peanuts that minimize time and expense. Our project focuses mainly on the design and development of a peanut cutting machine with a 0.5 hp motor. The workers removed the peanut capsules from the harvest at the beginning. The output of this method was very poor and did not meet consumer demand because it was a time-consuming operation. The main part of our project is a rotating drum with nails. They feed the peanuts to the rotating drum. Rotating drum tips isolate the pods from field harvest. There are large peanut harvesters on the market, but small farmers cannot afford these harvesters. Our machine is small, light and inexpensive. Farmers with a small farm may afford to use our machine.

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Mercury Detection in Marine Environment using Electrospun Nanofibers as Colorimetric Sensor

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IOP Conference Series: Materials Science and engineering 2021年第1期1084卷

作者： T Blesslin Sheeba V. Nandagopal P. Rajalakshmy T. Kesavan A.G. Aneesh Kumar Professor Department of ECE R.M.K. Engineering College Kavaraipettai Professor Department of Electrical and Electronics Engineering Er.Perumal Manimekalai College of Engineering and Technology Hosur Assistant Professor Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore Assistant Professor Sri Krishna College of Engineering and Technology Coimbatore Deputy Manager VVM Core Reliance Pro& Prop ManSer Ltd.

Potable water from sea/ocean is becoming contaminated at a fast rate and there is need for monitoring this rate of contamination. There are a number of pollutants that cause contamination, harming aquatic and marine environment. Detecting this water pollutant has become a major concern in recent year and this paper proposes a novel solution to identify specific pollutants (Arsenic and Chromium) in water Nanomaterials have been identified to have higher sensitivity, quick recovery, faster response and ease of design when compared to other materials used. The proposed work uses electrospun nanofibers to detect the presence of mercury which is a heavy metal ion, in marine water. Electrospun nanofibers exhibit many features like easy functionalization, porous nature, large surface area, quick response and enhanced selectivity and sensitivity. To detect mercury ion, an electrospun nanofiber-based sensing platforms is devised and the observations are recorded.

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Classification of Malignant Melanoma using Convolutional Neural Networks

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Journal of Physics: Conference Series 2021年第1期1916卷

作者： V Vijeya Kaveri V Meenakshi K A Saran Karthik S Shri Raam Professor Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Assistant Professor Department of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology India UG Student Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Human cancer is one of the world's most deadly diseases caused due to genetic disruption of skin cells and several molecular mutations. Skin cancer remains the most predominant form of cancer in human beings. The major goal is to detect skin cancer in early stages by research and analyse it using various techniques such as segmentation and feature extraction. The diagnosis of malignant melanoma skin cancer is done by dermatologist by examining skin and physical biopsy to determine the accurate stage of melanoma. It is developed because of high accumulation of melanin in the dermis layer of the skin. ABCD law is used in along with dermoscopy technology to detect malignant melanoma skin cancer. Image Acquisition Technique, pre-processing, segmentation, distinguishing function for skin Feature Selection, which specifies lesion characterization and classification methods are all conducted in this project for melanoma skin lesion characterization. We used symmetry detection, border detection, colour and diameter detection, as well as feature extraction to remove texture based features using a digital image processing technique. The deep Neural Network was proposed here to characterize the benign or malignant stage.

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Data dimensional reduction and principal components analysis

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Procedia computer Science 2019年 163卷 292-299页

作者： Nema Salem Sahar Hussein Assistant Professor Electrical and Computer Engineering Department Effat University Jeddah 21478 KSA Assistant Professor National Science Mathematics and Technology Unit Effat University Jeddah 21478 KSA

Research in the fields of machine learning and intelligent systems addresses essential problem of developing computer algorithms that can deal with huge amounts of data and then utilize this data in an intellectual way to solve a variety of real-world problems. In many applications, to interpret data with a large number of variables in a meaningful way, it is essential to reduce the number of variables and interpret linear combinations of the data. Principal Component Analysis (PCA) is an unsupervised learning technique that uses sophisticated mathematical principles to reduce the dimensionality of large datasets. The goal of this paper is to provide a complete understanding of the sophisticated PCA in the fields of machine learning and data dimensional reduction. It explains its mathematical aspect and describes its relationship with Singular Value Decomposition (SVD) when PCA is calculated using the covariance matrix. In addition, with the use of MATLAB, the paper shows the usefulness of PCA in representing and visualizing Iris dataset using a smaller number of variables.

关键词： PCA Data dimension reduction Iris dataset SVD

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Real time Machine Learning Approach for a Smart Door Unlocking Using Face recognition System

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IOP Conference Series: Materials Science and engineering 2021年第1期1130卷

作者： P Rayavel A Sivakumar P V Gopirajan S Surenderanath P Rathnavel Assistant Professor Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai-600044 Tamilnadu India Assistant Professor Department of Information Technology Sri Sairam Engineering College Chennai-600044 Tamilnadu India Assistant Profesor Department of Computer Science and Engineering Saveetha Engineering College Chennai-602105 Tamilnadu India Assistant Professor Department of Electrical and Electronics Engioneering Sri Sai Ram Institute of Technology Chennai-600044 Tamilnadu India

This proposed framework goes about as a home security framework for both Person location and gives security to entryway access control by utilizing facial acknowledgment for the home climate. The more peculiar's face is distinguished inside a home climate accomplished by catching a picture from a web camera and handling will be done on caught pictures. The web camera catches the arrangement of pictures when the individual comes before the entryway. The caught picture is analyzed utilizing our face acknowledgment calculation model. In the event that the individual is the approved one, at that point the entryway is opened. Then again, if the individual isn't the approved one. The picture of the individual is shipped off the power in the event that he perceives the individual and wants to open the entryway. He can distantly get to the entryway and permit the individual in.

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Renewable Energy Sources Efficient Detection in Triangulation for Wireless Sensor Networks

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IOP Conference Series: Materials Science and engineering 2021年第1期1055卷

作者： B Barani Sundaram Nunu Srinivas N Kannaiya Raja Manish Kumar Mishra Dhanabal Thirumoorthy NR Reddy Professor & Associate Dean-ICT Department of Computer Science BuleHora University Bulehora Ethiopia Assistant Professor School of Electrical and Computer Engineering Addis Ababa Institute of Technology Addis Ababa University Addis Ababa Ethiopia Professor Department of Computer Science School of Informatics and Electrical Engineering Ambo University IOT Campus Ambo Ethiopia Department of Computer Science University of Gondar Gondar Ethiopia Lecturer BuleHora University Bulehora Ethiopia Assistant Professor Department of Computer Science Mekelle University Mekelle Ethiopia

It is important for wireless sensor networks (WSNs) to determine whether hubs can be restricted, called localizability recognition. This progression is important for confining hubs, achieving minimal organizations of effort and distinguishing critical in field-based applications. Because of their high calculation and communication costs, centralized graph algorithms are meaningless to an asset-restricted WSN, whereas distributed methodologies can skip a large number of hypothetically locatable hubs in a resource-limited WSN. In this document we are proposing a efficient and successful distributed methodology to solve that address of issue. In addition, we demonstrate our calculation accuracy and analyze the reasons why our calculation will discover increasingly localizable hubs while requiring less established area hubs. The paper introduces a new RSSI-based localization approach triangle as well as centroid placement, utilizing triangle as well as centroid approach to minimize RSSI measurement error. Modelling simulations show that this algorithm can significantly increase the precision of the location compared to the trilateration.

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