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Mechanical properties of natural fiber reinforced composite structure

Materials Today: Proceedings 2023年

作者： Pothula Yamini Spandana Rokkala Shakriya Rishika P. Meghana Rani R. Arul Kumar Department of Aeronautical Engineering MLR Institute of Technology Hyderabad India Department of Mechanical Engineering Sri Krishna Polytechnic College (Govt. Aided Institute) Kovaipudur Coimbatore-42 Tamil Nadu India

The increase in drastic impact on the environment led to the search for natural composites to replace the conventional ones. Also, the properties of natural fibers such as thermal insulation , low density, mechanical and acoustic properties made researchers curious to use them in various industrial applications to get overall better efficiency in respective fields. The characteristics of natural fiber composites are durable, low cost, low weight, high specific strength , non-abrasive, equitably good mechanical properties , eco-friendly and biodegradable. In this investigation, the mechanical properties of the composite of kenaf, hemp fiber and epoxy polymer were measured to know about the specific strength of the resulting natural composite. This was achieved by fabricating the composite and then testing it under various conditions using a Universal testing machine and a 3-point Bending Test. The fractured surfaces were then observed by SEM.

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Quality Grading of Beef using Deep Convolutional Neural Network

Quality Grading of Beef using Deep Convolutional Neural Netw...

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HCT Information Technology Trends (ITT)

作者： Aarav Sharma Tanupriya Choudhury Tanmay Sarkar Madhusmita Mohanty Nikunj Bansal Sachi Nandan Mohanty Informatics Cluster School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand India CSE Dept. University of Petroleum and Energy Studies (UPES) CSE Dept. Graphic Era Hill University Dehradun Uttarakhand India Department of Food Processing Technology West Bengal State Council of Technical Education Govt. of West Bengal Malda Polytechnic Malda India VIT-AP School of Business (VSB) VIT-AP University Amaravati Andhra Pradesh India School of Computer Science & Engineering VIT-AP University Amaravati Andhra Pradesh India

Beef is one of the most widely consumed meat, being an organic substance it is prone to degradation over time. In our paper we have proposed a Convolutional Neural Network model to grade a given sample of Beef and predict its freshness as good or bad. We have proposed a CNN model architecture along with a data (image) pre-processing pipeline that can be used to classify a given Beef sample. We have done comparative analysis between different pre-processing pipelines as well as other CNN based classification models and after training have achieved 99.63% test accuracy.

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A method for predicting software reliability using object oriented design metrics

A method for predicting software reliability using object or...

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2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019

作者： Haritha Madhav, C. Vipin Kumar, K.S. Govt. Engg. College Thrissur Department of Computer Science and Engineering Thrissur India

ISBN: (数字)9781538681138

ISBN: (纸本)9781538681138

Object-oriented software metrics are important in software engineering field for measurement of the software quality, size estimation, complexity and effort. Object oriented software development is the most popular and best practice in software development. Object oriented software metrics can be used for measuring extend of object orientedness of a program. These quantities are important information indicators, that can be helpful for a software developer in take decisions for designing good software. It is proven that the software metrics provide quantitative information about the structure of the code, which can be used to determine the reliability of software. The design of the software is highly recommended to be object oriented in the software development environment. Software metrics specific to programs that have object oriented design can be used to measure the quality of the software like inheritance, assessing the effects of coupling, the complexity of the code, size aspects, etc, of the software developed in such an environment. Software measurement is a way for assessing the quality of software. Many attributes can define quality of a software, internal (size, complexity) as well as external (reliability)[14]. Often internal attributes are contributing factors to external attributes. Reliability as a term means the degree to which a component, product or system executes specified functions for a specific period of time under specified conditions. Software reliability is the software being trustworthy of the specifications. The aim of the work is to model the relationship between reliability of object oriented software to the object oriented design metrics and predict reliability of software. © 2019 IEEE.

关键词： Software reliability

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Experimental study on flexural strength of stitched foam composites

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Materials Today: Proceedings 2023年

作者： Muskaan Prasad G. Sangeetha V. Rahul A. Anil Kumar R. Arulkumar Department of Aeronautical Engineering MLR Institute of Technology Hyderabad Telangana India Department of Mechanical Engineering Sri Krishna Polytechnic College (Govt. Aided Institute) Kovaipudur Coimbatore 42 Tamil Nadu India

Sandwich composites and laminate composites are considered as major classes of lightweight structures in a wide range of engineering applications . Lightweight potential of fabric has demonstrated the specific mechanical properties of sandwich composites with or without core material. Many studies have shown that tension, compression, flexure , stress is improved or degraded up to 20% by a stitched process. The present paper focuses on the study of structural improvements of stitched foam composite and also to compare it with unstitched composite of the same glass fiber material. The use of vacuum bagging process to make the stitched foam composite structure and a simple hand lay-up technique for unstitched composite. These composites are made by using E Glass fiber 180GSM, aradur hy 951 hardener , Araldite ly 556 epoxy resin and glass fiber yarn. For stitched foam composite, laminates are stitched in transverse direction i.e., through thickness direction with high strength yarn. 3-point bending test is done to find flexural strength of both composites and compare their results stitched sample showed higher value of flexural strength and flexural rigidity after three-point bending test the strength of stitched composite is 40% more than unstitched composite material.

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Analysis of surface quality for electrical discharge drilled inconely 718 super alloy by means of Taguchi approach

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AIP Conference Proceedings 2023年第1期2786卷

作者： Lavish Rawal Rakesh Kumar Satish Namdev Ashish Goyal Department of Automobile Engineering Manipal University Jaipur Jaipur India-303007 Department of Mechanical Engineering Govt. Polytechnic Adampur Tarabganj Gonda India-27140 Department of Mechanical Engineering Manipal University Jaipur Jaipur India-303007

Surface roughness is an inherit of any manufacturing process. It is a measure of the irregularities present on the structural surface after machining. Existing study is aimed to highlight the influence of input process parameters over surface roughness. Inconely 718, a nickel and chromium based super alloy offering great strength, resistance to corrosion and is very tricky to machine is elected as workpiece material. Copper is selected as the material for rotating tool electrode and additional grooves are provided in spiral manner so as to minimize machining period with improved processing. Peak current, pulse-on time, hole depth and tool diameter are taken in consideration as the input machining factors for Electrical discharge drilling. Taguchi L16 mixed-level concept is practiced for the experimental design. Analysis of variance is carried out in order to find out significant parameters that affect surface roughness. On the basis of ANOVA for the current experimental design, peak current, pulse on time also hole depth are concluded as the significant factors.

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Analysis and Real-Time Implementation of IoT in Autism

Research Square

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Research Square 2021年

作者： Afzal, Muhammad Javaid Ashraf, Muhammad Waseem Tayyaba, Shahzadi Javaid, Farah Department of Physics Govt. Islamia College Civil Lines Lahore Pakistan G.C. University Lahore54000 Pakistan Department of Computer Engineering The University of Lahore Lahore54000 Pakistan Lahore Pakistan

The present study comprises the importance and use of IoT, smart home, peer-to-peer, and some other different technologies for the betterment of mankind. Artificial intelligence and machine learning are becoming more and more popular in IoT-based smart homes and other techs. The IoT framework has been used to provide a predictable, concrete, and self-paced learning environment and encourages excellent visual information processing. These techs are also useful for special children with autism. They need smart homes, peer-to-peer networking, robot therapy, and wearable techs. In this paper, the authors presented a unique FLC simulation for the behavior of a child with moderate autism. This simulation shows that how IoT, smart home, peer-to-peer technology, ABA therapy, robot therapy, and wearable techs can bring comforts in the life of a child with autism. All of them have long lasted impacts in producing social skills in an ASD person. The 3D graphical figures presented the graphical analysis of a child’s social behavior. The simulation presented 50% betterment change in the social behavior of children and this can be increased up to 87% by using intensive therapies. It is also verified by Mamdani’s method. A real-time implementation of a boy with autism has shown significant improvements in his social skills. © 2021, CC BY.

关键词： Automation

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Plant leaf damage detection using convolution neural network models

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AIP Conference Proceedings 2023年第1期2822卷

作者： Varsha P. Gaikwad Vijaya Musande Department of Information Technology . Govt. College of Engineering Aurangabad(MH) India. Department of Computer Science and Engineering Jawaharlal Nehru college of Engg. Aurangabad(MH) India.

Since decade agriculture is the main source of food source. It plays a very vital role in food production. More than sixty percent of the population of earth depends on agriculture sources for their main supply. Various factors such as climate, rain, soil, and insects affects on growth of the plant. Every year more than 15-20 percent crop losses occurs due to plant disease. Potato and tomato mostly used in every kitchen as well as in various industrial productions. These crops are mostly affected with late and early blight disease. The late and inaccurate identification of disease decreases the productivity of the crops. The key purpose of our research is to discover plant disease at an early stage by using the Convolution Neural Network (CNN). For this research we considered Plantvillage dataset of 3580 tomato plant leaf images and 2150 potato plant leaf images. We used CNN customized models. For our research AlexNet model achieves 84.7% accuracy and VGG16 model achieves 96.5% accuracy for tomato and potato plant leaves respectively.

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Applications of Deep Learning (DL) Techniques in Detecting Breast Cancer and Malignant Cells

Applications of Deep Learning (DL) Techniques in Detecting B...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： I. S. Chakrapani Neha Tyagi Swati Tyagi Pankaj Kunekar D. Lakshmi Padmaja Kumud Pant Department of Zoology PRR&VS Govt. College Vidavalur Nellore dist Andhra Pradesh Department of Computer Science and Engineering Amity University Greater Noida Uttar Pradesh India University of Delaware Newark DE USA Vishwakarma Institute of Technology Pune Department of IT Anurag University Telangana Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India

According to the most recent statistics, the most common type of cancer globally is the breast carcinoma and kills close to 900,000 people annually. Early and accurate diagnosis of the illness can increase the likelihood of successful treatment and lower the mortality rate. In fact, an early diagnosis can help stop it from spreading and prevent the premature victims from getting it. Researchers who study cancer have a number of difficulties when attempting to differentiate between benign and malignant tumors as well as attempting to make judgments about benign and metastatic breast carcinoma. Examine the effectiveness of automated deep learning algorithms at identifying malignant cells in women’s breasts and cancer stage. This paper suggests applying deep learning algorithms to whole-slide pathology images in order to possibly increase diagnostic efficacy and accuracy. The convolutional neural networks (CNN), sparse auto encoders (SAE), and stacked sparse auto encoders are illustrations of techniques of deep learning were used in this research work. There are numerous public mammographic databases available. The methods discussed in this paper are put to the test using the mini-MIAS mammographic database. The stacked sparse auto encoder performs better, this method has to be tested in a clinical setting before being used. It has higher accuracy and precision as compared to CNN and SAE. Better diagnostic performance was achieved by several deep learning methods. Deep learning algorithms are used to better reliably identify tiny tumors while detecting breast cancer via a mammogram.

关键词： Deep learning Pathology Databases Malignant tumors Muscles Breast cancer Mammography

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A Comparative Approach to Mitigate Economic Denial of Sustainability (EDoS) in a Cloud Environment 4

A Comparative Approach to Mitigate Economic Denial of Sustai...

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4th International Conference on Information Systems and computer Networks, ISCON 2019

作者： Nautiyal, Swati Wadhwa, Shruti NITTTR Department of Computer Science and Engineering Chandigarh India Post Graduate Govt. College Department of Computer Science Chandigarh India

ISBN: (数字)9781728136516

ISBN: (纸本)9781728136516

In cloud computing, a new threat is introduced namely Economic Denial of Sustainability (EDoS) which is similar to Distributed Denial of Services (DDoS) attack that targets the vulnerabilities of the cloud server user along with its price model. EDoS attackers continually request bulk resources, such as virtual security devices and databases, virtual machines, virtual networking devices, that targets the auto scaling feature of cloud. As a result, there is a huge increase in consumer bill due to the illegitimate requests which results in bankruptcy. In this paper, various EDoS mitigation techniques are discussed with their different approaches. This paper proposed a new approach that uses Artificial Neural Network along with Genetic Algorithm. The classification of is done using Artificial Neural Network that that classify the cloud server consumer and may lessen the EDoS attacks in the cloud environment whereas Genetic Algorithm (GA) is deployed for optimization of the properties of each server using pertinent fitness function. The proposed technique's comparison with existing technique is taken under the considerations and expectations. © 2019 IEEE.

关键词： Neural networks

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A Novel Defocus-blur Region Detection Approach Based on DCT Feature and PCNN Structure

arXiv

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arXiv 2023年

作者： Basar, Sadia Ali, Mushtaq Waheed, Abdul Ahmad, Muneer Miraz, Mahdi H. Department of CS & IT Hazara University Mansehra21120 Pakistan Higher Education Department Govt. Girls Degree College Palosai KPK Peshawar25121 Pakistan Department of Computer Science Women University Swabi Swabi23430 Pakistan School of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Human and Digital Interface Woosong University Daejeon Korea Republic of School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia School of Computing Faculty of ArtsScience and Technology Wrexham Glyndwr University Wrexham United Kingdom Faculty of Computing Engineering and Science University of South Wales Pontypridd United Kingdom

The motion or out-of-focus effect in digital images is the main reason for the blurred regions in defocused-blurred images. It may adversely affect various image features such as texture, pixel, and region. Therefore, it is important to detect in-focused objects in defocused-blurred images after the segmentation of blurred and non-blurred regions. The state-of-the-art techniques are prone to noisy pixels, and their local descriptors for developing segmentation metrics are also complex. To address these issues, this research, therefore, proposed a novel and hybrid-focused detection approach based on Discrete Cosine Transform (DCT) coefficients and PC Neural Net (PCNN) structure. The proposed approach partially resolves the limitations of the existing contrast schemes to detect in-focused smooth objects from the out-of-focused smooth regions in the defocus dataset. The visual and quantitative evaluation illustrates that the proposed approach outperformed in terms of accuracy and efficiency to referenced algorithms. The highest Fα-score of the proposed approach on Zhao's dataset is 0.7940 whereas on Shi's dataset is 0.9178. © 2023, CC BY.

关键词： Neural networks

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