International Journal of Engineering and Manufacturing (IJEM)

ISSN: 2305-3631 (Print)

ISSN: 2306-5982 (Online)

DOI: https://doi.org/10.5815/ijem

Website: https://www.mecs-press.org/ijem

Published By: MECS Press

Frequency: 6 issues per year

Number(s) Available: 72

(IJEM) in Google Scholar Citations / h5-index

IJEM is committed to bridge the theory and practice of engineering and manufacturing. From innovative ideas to specific algorithms and full system implementations, IJEM publishes original, peer-reviewed, and high quality articles in the areas of engineering and manufacturing. IJEM is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of engineering and manufacturing applications.

 

IJEM has been abstracted or indexed by several world class databases: Google Scholar, Microsoft Academic Search, Baidu Wenku, Open Access Articles, Scirus, CNKI, CrossRef, JournalTOCs, etc..

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IJEM Vol. 14, No. 2, Apr. 2024

REGULAR PAPERS

Non-Digital Method of Process Safety Management (PSM) Compliance, OSHA PSM and EPA RMP Rulemaking Initiatives, and Methodology to Estimate Related Economic Impact on PSM Facilities in the United States of America

By Dheerajkumar R Narang

DOI: https://doi.org/10.5815/ijem.2024.02.01, Pub. Date: 8 Apr. 2024

Over the years, process safety management (PSM) program development and implementation has benefited the operating facilities in terms of successful avoidance of process safety incidents, and related business losses arising due to equipment damage, production interruption and environmental damage. PSM covered facilities have also reported improved personal injury record through successful implementation of an applicable, regional PSM standard. However, there is still a need to address both the efficiency and effectiveness aspects related to non-digital (manual) PSM program development, implementation, audit, and compliance. There has been continuous reporting of significant process safety incidents even after the promulgation of process safety management regulations both in the United States and globally. The uncertain macroeconomic and political conditions have also forced the governments to allocate less than required fiscal budget to their regional regulatory bodies to address any ongoing operational efficiency concerns in successful enforcement of their PSM regulation. This research paper will investigate and discuss the key issues faced by both the government regulatory bodies and process plant facilities, with the current non-digital (manual) method of PSM implementation, audit, and compliance. Moreover, recent modernization initiatives of safety regulations undertaken by federal regulatory bodies such as OSHA and EPA in the United States are described and discussed in the context of achieving the PSM compliance effectively and efficiently. The paper will also discuss detailed comparison between OSHA’s 1992 regulatory impact analysis study and 2023 information collection requirement burden hour and cost estimate and will outline a methodology to estimate the total economic burden of PSM compliance on the existing and future PSM covered facilities in the United States. The methodology to adjust (correct) OSHA’s PSM compliance cost estimates are based on accounting key regulatory, industrial, organizational, and economic factors prevalent in the global process plant industry.

[...] Read more.
A Real-time Light-weight Computer Vision Application for Driver’s Drowsiness Detection

By Saikat Baul Md. Ratan Rana Farzana Bente Alam

DOI: https://doi.org/10.5815/ijem.2024.02.02, Pub. Date: 8 Apr. 2024

The issue of drowsiness while operating a motor vehicle is an increasingly common occurrence that has been found to contribute significantly to a substantial number of fatal accidents annually. The urgency of the current situation necessitates implementing a solution to mitigate accidents and fatalities. The present study aims to investigate a less intricate and less expensive but remarkably efficient approach for detecting drowsiness in drivers, in contrast to the existing complex systems developed for this purpose. This paper focuses on developing a simple drowsy driver detection system utilizing the Python programming language and integrating the OpenCV and Dlib models. The shape detector provided by Dlib is employed to accurately determine the spatial coordinates of the facial landmarks within the given video input. This enables the detection of drowsiness by monitoring various factors such as the aspect ratios of the eyes, mouth, and the angle of head tilt. The performance evaluation of the system under consideration is conducted through the utilization of standardized public datasets and real-time video footage. When tested with dataset image inputs, the system showed exceptional recognition accuracy. The performance comparison is done to show the efficacy of the proposed approach. Traveling can be made safer and more effective by combining the proposed system with additional safety features and automation technology in cars.

[...] Read more.
Vehicle Object Tracking Based on Fusing of Deep learning and Re-Identification

By Huynh Nhat Duy Vo Hoai Viet

DOI: https://doi.org/10.5815/ijem.2024.02.03, Pub. Date: 8 Apr. 2024

Object tracking is a popular problem for automatic surveillance systems as well as for the research community. The requirement of an object tracking problem is to predict the output including the object position at the current frame based on the input the position of the object at the previous frame. To present the comparison and experiment of some object tracking methods based on deep learning and suggestions for improvement between them in this paper, we had taken some important steps to conduct this research. First, we find out the studies related to deep learning-based object tracking models. Secondly, we examined image and video data sets for verification purposes. Thirdly, to evaluate the results obtained from existing models, we experimented with a little work related to object tracking based on deep learning networks. Fourth, based on the implemented object tracking models, we had proposed a combination of these methods. And finally, we summarize and give the evaluations for each object tracking model from the results obtained. The results show that object tracking based on Siammask model has the highest results TO score of 0.961356383 on VOT dataset and 0.969301864 on UAV123 dataset, but the possibility of errors is also high. Although the result of the combined method has few scores those are lower than the object tracking based on Siammask model, the combined method is more stable than the object tracking based on Siammask model when TME score of 16.29691993 on VOT dataset and 10.16578548 on UAV123 dataset. The Vehicle ReIdentification method results have scores that are not too overwhelming. However, the TME score is the highest with the TME score of 11.55716097 on the VOT dataset and 4.576163526 on the UAV123 dataset.

[...] Read more.
Alleviating Unwanted Recommendations Issues in Collaborative Filtering Based Recommender Systems

By Abba Almu Abubakar Roko

DOI: https://doi.org/10.5815/ijem.2024.02.04, Pub. Date: 8 Apr. 2024

The overabundance of information on the internet and ecommerce has resulted to the development of recommender system to discover interesting items or contents that are recommendable to the user. The recommended items might be of no interest or unwanted to the users and can make users to lose interest in the recommendations. In this work, a Collaborative Filtering (CF) based method which exploits the initial top-N recommendation lists of an item-based CF algorithm based on unwanted recommendations penalisation is presented.  The method utilises a relevance feedback mechanism to solicit for users preferences on the recommendations while popularise similarity function minimises the chances of recommending unwanted items. The work explains the proposed algorithm in detail and demonstrates the improvements required on existing CF to provide some adjustments required to improve subsequent recommendations to users. 

[...] Read more.
Design Modification and Fabrication of An Active Solar Dryer

By Danesh Kumar Meghwar Atam Kumar

DOI: https://doi.org/10.5815/ijem.2024.02.05, Pub. Date: 8 Apr. 2024

This study presents the design modification and performance evaluation of an active solar dryer. The Modified Active Solar Dryer (MASD) is equipped with a non-concentrating flat plate thermal collector and drying chamber to dry various agricultural products. The drying chamber contains trays inside it. These are used to completely circulate airflow inside it. The drying Chamber has a cross-section Area of 0.30m × 0.45m and a height of 0.45m. The Flat plate collector has 0.91 m length, 0.45 m width, 0.12 m depth, and 0.049 m^3 volume. This study performed experimental work and evaluated performance by drying the chili in a Simplified Active Solar Dryer and Modified Active Solar dryer from 8 am to5 pm with average solar irradiance of 883.25W⁄m^2 . The temperature has been recorded at six different points. The difference in temperatures between the dryer input and the surrounding air has been observed continuously. Temperatures at the Thermal Collector and Drying Chamber are the main prime mover of solar drying, so our main concern was on these two temperatures. The temperature achieved by SASD in the thermal collector is in the range of 55 ~ 60˚C meanwhile in the MASD temperature range is 65 ~ 70˚C. In SASD chili was dried in five days meanwhile in Modified Active Solar Dryer it took four days. The moisture content of the chili was reduced to 10% and it took 45hour for SASD and 34 hours for MASD. It is concluded that Modified Active Solar Dryer took 34% less time than the Simplified Active Solar Dryer. 

[...] Read more.
Machine Learning Approaches for Cancer Detection

By Ayush Sharma Sudhanshu Kulshrestha Sibi B Daniel

DOI: https://doi.org/10.5815/ijem.2018.02.05, Pub. Date: 8 Mar. 2018

Accurate prediction of cancer can play a crucial role in its treatment. The procedure of cancer detection is incumbent upon the doctor, which at times can be subjected to human error and therefore leading to erroneous decisions. Using machine learning techniques for the same can prove to be beneficial. Many classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are proven to produce good classification accuracies. The following study models data sets for breast, liver, ovarian and prostate cancer using the aforementioned algorithms and compares them. The study covers data from condition of organs, which is called standard data and from gene expression data as well. This research has shown that SVM classifier can obtain better performance for classification in comparison to the ANN classifier.

[...] Read more.
Big Data in Cyber-Physical Systems, Digital Manufacturing and Industry 4.0

By Lidong Wang Guanghui Wang

DOI: https://doi.org/10.5815/ijem.2016.04.01, Pub. Date: 8 Jul. 2016

A cyber physical system (CPS) is a complex system that integrates computation, communication, and physical processes. Digital manufacturing is a method of using computers and related technologies to control an entire production process. Industry 4.0 can make manufacturing more efficient, flexible, and sustainable through communication and intelligence; therefore, it can increase the competitiveness. Key technologies such as the Internet of Things, cloud computing, machine-to-machine (M2M) communications, 3D printing, and Big Data have great impacts on Industry 4.0. Big Data analytics is very important for cyber-physical systems (CPSs), digital manufacturing, and Industry 4.0. This paper introduces technology progresses in CPS, digital manufacturing, and Industry 4.0. Some challenges and future research topics in these areas are also presented.

[...] Read more.
Reliability Analysis Techniques in Distribution System: A Comprehensive Review

By Prakash Kafle Manila Bhandari Lalit B. Rana

DOI: https://doi.org/10.5815/ijem.2022.02.02, Pub. Date: 8 Apr. 2022

Quality of electricity with continuity is the reliability of the power system which is inversely proportional with the duration of power supply interruption. It depends on some expected or unexpected faults/failures on the systems, speed of protecting systems, preventive maintenance, and motivation of technical staffs. The detailed study of the distribution system is more crucial as its reliability is the concern of utility’s fame, service, customers’ satisfactions and reflects to the overall revenue. The relevant articles from the various sources has been collected and analyzed different reliability indices with their significance. Also, to realize the methodology related with reliability analysis, a comparative study among its different components has been carried out and the best techniques for maintaining system reliability are suggested.

[...] Read more.
Gas Leakage Detector and Monitoring System

By Nureni Asafe Yekini Adigun J. Oyeranmi Oloyede A. Olamide Akinade O. Abigael

DOI: https://doi.org/10.5815/ijem.2022.05.05, Pub. Date: 8 Oct. 2022

Leakage of gas is a major issue in the industrial sector, residential buildings, and gas-powered vehicles, one of the preventive methods to stop accidents associated with gas leakage is to install gas leakage detection devices. The focus of this work is to propose a device that can detect gas leakage and alert the owners to avert problems due to gas leakages. The system is based on a microcontroller that employs a gas sensor as well as a GSM module, an LCD display, and a buzzer. The system was designed for gas leakage monitoring and alerts with SMS via an Arduino microcontroller with a buzzer and an MQ2 gas sensor. The circuit contains a Microcontroller MQ2 gas sensor, buzzer, LCD display, and GSM module, when the sensor detects gas leakage it transmit the information to the Microcontroller while the microcontroller makes a decision and then forwarded a warning message to the user as SMS to a mobile phone for decision to be taken accordingly. The output of this research will be significant in averting problems associated with gas leakages now and in future. 

[...] Read more.
Towards the Development a Cost-effective Earthquake Monitoring System and Vibration Detector with SMS Notification Using IOT

By Shaina Delia G. Tomaneng Jubert Angelo P. Docdoc Susanne A. Hierl Patrick D. Cerna

DOI: https://doi.org/10.5815/ijem.2022.06.03, Pub. Date: 8 Dec. 2022

As one of the countries situated in the Pacific Ring of Fire, the Philippines suffers from an inexhaustible number of natural disasters every year. One of the most destructible ones is the occurrence of earthquakes. Because of the high damage that earthquakes incur, along with their inevitability and unpredictability, developing effective methods of earthquake damage mitigation as well as disaster preparedness is imperative to lessen the negative impacts it is capable of producing in communities. One efficient way of doing this is by implementing an earthquake early warning (EEW) system that is capable of sending message alerts to receivers to warn them in the event of a hazardous earthquake. With this objective, this study centers on creating an earthquake detector with SMS messaging to function as an EEW system with an added advantage of being low-cost to make it more accessible to the public. Using electronic components based on an Arduino Mega 2560 and a Global System for Mobile Communications (GSM) module, the earthquake detector and its alert message system were created. A series of tests in different locations across Butuan City was then performed to assess the device’s accuracy in measuring different Intensity levels when subjected to surface vibrations. Comparative analysis showed that its recorded values. Corresponded with the values obtained from accelerometer-based mobile applications. In conclusion, the study was deemed functional in its ability to detect low and high surface vibrations, which proves that it is successful in detecting earthquake tremors and vibrations in the event of an earthquake.

[...] Read more.
Development of a Low-Cost Air Quality Data Acquisition IoT-based System using Arduino Leonardo

By Louis Anton A. Cruz Maria Teresa T. Grino Thea Marie V. Tungol Joel T. Bautista

DOI: https://doi.org/10.5815/ijem.2019.03.01, Pub. Date: 8 May 2019

Air pollution is responsible for an estimated 5.5 million deaths in 2013 which costed the global economy approximately US$225 billion in lost labor income. To address the problems caused by air pollution, this study aims to develop a low-cost and portable air quality monitoring system that detects the levels of CO, PM2.5, PM10, temperature, and humidity. Using Internet of Things (IoT), the data that the system gathers can be accessed through the internet. Moreover, the system assesses the obtained data through a comparative analysis with the AQI. The Iterative Design Loop method was used in the development of the air quality monitoring system. Furthermore, the sensors were programmed using the Arduino Integrated Development Environment (IDE). Using the Welch’s t-test, it was found that the obtained data of the system is not significantly different to that of the standard air quality monitoring systems. To achieve more accurate data from the developed system, the raw data of the developed and standard system were calibrated through an equation from the trendline. Through the use of Acer CloudProfessor, the study successfully developed an air quality monitoring system that can be accessed through the internet.

[...] Read more.
Fully Automated Hydroponics System for Smart Farming

By Hariram M Shetty Kshama Pai K Navaneeth Mallya Pratheeksha

DOI: https://doi.org/10.5815/ijem.2021.04.04, Pub. Date: 8 Aug. 2021

This project is focused on developing a Fully Automatic Hydroponics system which helps in monitoring and controlling temperature, Humidity, pH and EC in Hydroponics. Hydroponics is a method of growing crops without soil. Plants are grown in rows or on trellises, just like in a traditional garden, but they have their roots in water rather than in dirt. Although, there are different ways in which hydroponics can be implemented, there is no individual system which can measure and control pH and EC level of nutrient solution along with its surrounding temperature and humidity automatically. We use PIC16F877A microcontroller and four pumps, three of which are used to pump water, nutrient solution, pH solution and the fourth pump is used to control the humidity. A fan is used to control the temperature which increases its speed as the temperature increases. The pumps are turned on depending on the EC and pH values obtained from the electrodes. A passive LCD display is used to display variations in the values. Different Analysis like water usage, plant growth in comparison with regular farming method and hydroponics is successfully completed which results in hydroponics system is significant method in comparison with soiled cultivation method in terms of yield and water usage. This project is expected to produce high yield crops by taking minimal space, makes work easier for farmers in growing of plants, and also consumes less amount of water when compared to traditional method resulting in conservation of water.

[...] Read more.
A Review on Stabilization of Soft Soils with Geopolymerization of Industrial Wastes

By Tadesse A. Wassie Gokhan Demir

DOI: https://doi.org/10.5815/ijem.2023.02.01, Pub. Date: 8 Apr. 2023

Geopolymers are inorganic aluminosilicate polymers that solidify into ceramic-like substances at tempera-tures close to ambient. The elements in silicate oxide (SiO2) and aluminum oxide (Al2O3) are essential for the hardening of geopolymers because they combine with other elements to create N-A-S-H formation, which gives the material its distinctive strength. Geopolymers based on industrial wastes are increasingly being used to stabilize soft soils. Fly ash, GGBS, metakaolin, glass powders, and others are a few of the industrial wastes that aid in synthesizing geopolymers. Several experimental studies were carried out to determine the mechanical strength, durability, and microstructure im-provement of soft soils stabilized with geopolymers. Some of the experiments include X-ray diffraction (XRD), scan-ning electron microscopy (SEM), unconfined compression testing (UCS), and durability testing. The main objective of this review was to assess the different types of binders, binder ratios, alkali activator types, alkali activator concentra-tions, and other parameters used in synthesizing geopolymers. The binder's proportion varies between 5% and 30% of the soil's dry weight. Researchers commonly use sodium silicate (Na2SiO3) and sodium hydroxide (NaOH) solution for the alkali activator. Since the unconfined compression test is one of the quickest and least expensive ways to determine shear strength, most researchers were used to measure stabilized soils' mechanical strength. This paper highlights the most frequently used industrial wastes used to synthesize geopolymers. The review enables researchers to acquire es-sential and complementary inputs for future research.

[...] Read more.
Deep Neural Network for Human Face Recognition

By Priya Gupta Nidhi Saxena Meetika Sharma Jagriti Tripathi

DOI: https://doi.org/10.5815/ijem.2018.01.06, Pub. Date: 8 Jan. 2018

Face recognition (FR), the process of identifying people through facial images, has numerous practical applications in the area of biometrics, information security, access control, law enforcement, smart cards and surveillance system. Convolutional Neural Networks (CovNets), a type of deep networks has been proved to be successful for FR. For real-time systems, some preprocessing steps like sampling needs to be done before using to CovNets. But then also complete images (all the pixel values) are passed as input to CovNets and all the steps (feature selection, feature extraction, training) are performed by the network. This is the reason that implementing CovNets are sometimes complex and time consuming. CovNets are at the nascent stage and the accuracies obtained are very high, so they have a long way to go. The paper proposes a new way of using a deep neural network (another type of deep network) for face recognition. In this approach, instead of providing raw pixel values as input, only the extracted facial features are provided. This lowers the complexity of while providing the accuracy of 97.05% on Yale faces dataset.

[...] Read more.
Automated Wall Painting Robot for Mixing Colors based on Mobile Application

By Ayman Abdullah Ahmed Al Mawali Shaik Mazhar Hussain

DOI: https://doi.org/10.5815/ijem.2023.01.04, Pub. Date: 8 Feb. 2023

The final stage, which is the building paint or the adopted design, is where most real estate developers and constructors struggle. Where extensive painting is required, which takes a lot of time, effort, and accuracy from the firm doing the work. Additionally, it might be challenging to decide on the precise color grades for the design and calculate the right amount of paint to use for the job. Where these activities are extremely expensive, and the complex implementation is accompanied by worries and skepticism. These are the motivations behind the development of painting machines that blend colors. Artificial intelligence is used in the machine's design to make it efficient and quick at what it does. High accuracy is needed when selecting the proper colors, and this machine is distinguished by its ability to select the proper color tone. The color sensor (TCS34725 RGB) determines the relevance and accuracy of the desired color by comparison with the system database with the assistance of the light sensor (STM32), which measures the degree of illumination of the chosen place. By combining basic colors, this technique saves the customer the hassle of looking at specialized stores for the level of color they require. By giving the system the codes assigned to each color, it may also blend colors. The system also has the feature of controlling the machine remotely via smart phone application by enabling bluetooth and wifi features.

[...] Read more.
Machine Learning Approaches for Cancer Detection

By Ayush Sharma Sudhanshu Kulshrestha Sibi B Daniel

DOI: https://doi.org/10.5815/ijem.2018.02.05, Pub. Date: 8 Mar. 2018

Accurate prediction of cancer can play a crucial role in its treatment. The procedure of cancer detection is incumbent upon the doctor, which at times can be subjected to human error and therefore leading to erroneous decisions. Using machine learning techniques for the same can prove to be beneficial. Many classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are proven to produce good classification accuracies. The following study models data sets for breast, liver, ovarian and prostate cancer using the aforementioned algorithms and compares them. The study covers data from condition of organs, which is called standard data and from gene expression data as well. This research has shown that SVM classifier can obtain better performance for classification in comparison to the ANN classifier.

[...] Read more.
Reliability Analysis Techniques in Distribution System: A Comprehensive Review

By Prakash Kafle Manila Bhandari Lalit B. Rana

DOI: https://doi.org/10.5815/ijem.2022.02.02, Pub. Date: 8 Apr. 2022

Quality of electricity with continuity is the reliability of the power system which is inversely proportional with the duration of power supply interruption. It depends on some expected or unexpected faults/failures on the systems, speed of protecting systems, preventive maintenance, and motivation of technical staffs. The detailed study of the distribution system is more crucial as its reliability is the concern of utility’s fame, service, customers’ satisfactions and reflects to the overall revenue. The relevant articles from the various sources has been collected and analyzed different reliability indices with their significance. Also, to realize the methodology related with reliability analysis, a comparative study among its different components has been carried out and the best techniques for maintaining system reliability are suggested.

[...] Read more.
Neural Networks-based Process Model and its Integration with Conventional Drum Level PID Control in a Steam Boiler Plant

By Douglas T. Mugweni Hadi Harb

DOI: https://doi.org/10.5815/ijem.2021.05.01, Pub. Date: 8 Oct. 2021

Controlling drum level is a major and crucial control objective in thermal power plant steam boilers. The drum level as a controlled variable is highly characterized by complex non-linear process dynamics as well as measurement noise and long-time delays. Developing a data-driven process model is particularly advantageous as it could be built from ongoing operational data. Such a model could be used to assist existing controllers by providing predictions regarding the drum level. The aim of this paper is to develop such a model and to propose a control architecture that can be easily integrated into existing control hardware. For that purpose, different neural networks are used, Multilayer Perceptron (MLP), Nonlinear Autoregressive Exogenous (NARX), and Long Short Term (LSTM) neural networks. LSTM and MLP were able to capture the dynamics of the process, but LSTM showed superior performance. The results demonstrate that the use of traditional machine learning criteria to evaluate a process model is not necessarily adequate. Using the model in an open-loop and a closed-loop simulation is more suitable to test its ability to capture the dynamics of the process. A novel architecture that integrates the process model within an existing closed-loop controller is proposed. The architecture uses adaptive weights to ensure that a good model is given more influence than a bad model on the controller’s output.

[...] Read more.
Fire and Motion Early Warning Device: Its Design and Development

By Ronnie Camilo F. Robles Ruth G. Luciano Rolaida L. Sonza Arnold P. Dela Cruz Mariel Cabrillas

DOI: https://doi.org/10.5815/ijem.2021.06.01, Pub. Date: 8 Dec. 2021

Cases of theft and robbery of computers, CCTV equipment, and LCD projector have become more frequent in schools. In addition, fire hazards are great threat to educational institutions where expensive learning materials are kept. Such incidents could be lessened and avoided if schools are equipped with appropriate security systems capable of monitoring and informing people about the coming possible danger. Thus, the development of Fire and Motion Early Warning Device (FMEWD) is timely and relevant. FMEWD consists of a website and interconnected devices and sensors intended to provide an efficient and effective warning system for preventing incidents relating to fire, smoke, and intrusion within an office. Upon detection, the system automatically sends an email and SMS to registered users. This study used the Agile Development Model which allows features to be delivered quickly and more frequently with higher levels of predictability. Evidently, the integration of different technologies conceptualized by the researcher addresses the pressing security concerns faced by educational institutions like NEUST.

[...] Read more.
Automated Wall Painting Robot for Mixing Colors based on Mobile Application

By Ayman Abdullah Ahmed Al Mawali Shaik Mazhar Hussain

DOI: https://doi.org/10.5815/ijem.2023.01.04, Pub. Date: 8 Feb. 2023

The final stage, which is the building paint or the adopted design, is where most real estate developers and constructors struggle. Where extensive painting is required, which takes a lot of time, effort, and accuracy from the firm doing the work. Additionally, it might be challenging to decide on the precise color grades for the design and calculate the right amount of paint to use for the job. Where these activities are extremely expensive, and the complex implementation is accompanied by worries and skepticism. These are the motivations behind the development of painting machines that blend colors. Artificial intelligence is used in the machine's design to make it efficient and quick at what it does. High accuracy is needed when selecting the proper colors, and this machine is distinguished by its ability to select the proper color tone. The color sensor (TCS34725 RGB) determines the relevance and accuracy of the desired color by comparison with the system database with the assistance of the light sensor (STM32), which measures the degree of illumination of the chosen place. By combining basic colors, this technique saves the customer the hassle of looking at specialized stores for the level of color they require. By giving the system the codes assigned to each color, it may also blend colors. The system also has the feature of controlling the machine remotely via smart phone application by enabling bluetooth and wifi features.

[...] Read more.
A Review on Stabilization of Soft Soils with Geopolymerization of Industrial Wastes

By Tadesse A. Wassie Gokhan Demir

DOI: https://doi.org/10.5815/ijem.2023.02.01, Pub. Date: 8 Apr. 2023

Geopolymers are inorganic aluminosilicate polymers that solidify into ceramic-like substances at tempera-tures close to ambient. The elements in silicate oxide (SiO2) and aluminum oxide (Al2O3) are essential for the hardening of geopolymers because they combine with other elements to create N-A-S-H formation, which gives the material its distinctive strength. Geopolymers based on industrial wastes are increasingly being used to stabilize soft soils. Fly ash, GGBS, metakaolin, glass powders, and others are a few of the industrial wastes that aid in synthesizing geopolymers. Several experimental studies were carried out to determine the mechanical strength, durability, and microstructure im-provement of soft soils stabilized with geopolymers. Some of the experiments include X-ray diffraction (XRD), scan-ning electron microscopy (SEM), unconfined compression testing (UCS), and durability testing. The main objective of this review was to assess the different types of binders, binder ratios, alkali activator types, alkali activator concentra-tions, and other parameters used in synthesizing geopolymers. The binder's proportion varies between 5% and 30% of the soil's dry weight. Researchers commonly use sodium silicate (Na2SiO3) and sodium hydroxide (NaOH) solution for the alkali activator. Since the unconfined compression test is one of the quickest and least expensive ways to determine shear strength, most researchers were used to measure stabilized soils' mechanical strength. This paper highlights the most frequently used industrial wastes used to synthesize geopolymers. The review enables researchers to acquire es-sential and complementary inputs for future research.

[...] Read more.
Fully Automated Hydroponics System for Smart Farming

By Hariram M Shetty Kshama Pai K Navaneeth Mallya Pratheeksha

DOI: https://doi.org/10.5815/ijem.2021.04.04, Pub. Date: 8 Aug. 2021

This project is focused on developing a Fully Automatic Hydroponics system which helps in monitoring and controlling temperature, Humidity, pH and EC in Hydroponics. Hydroponics is a method of growing crops without soil. Plants are grown in rows or on trellises, just like in a traditional garden, but they have their roots in water rather than in dirt. Although, there are different ways in which hydroponics can be implemented, there is no individual system which can measure and control pH and EC level of nutrient solution along with its surrounding temperature and humidity automatically. We use PIC16F877A microcontroller and four pumps, three of which are used to pump water, nutrient solution, pH solution and the fourth pump is used to control the humidity. A fan is used to control the temperature which increases its speed as the temperature increases. The pumps are turned on depending on the EC and pH values obtained from the electrodes. A passive LCD display is used to display variations in the values. Different Analysis like water usage, plant growth in comparison with regular farming method and hydroponics is successfully completed which results in hydroponics system is significant method in comparison with soiled cultivation method in terms of yield and water usage. This project is expected to produce high yield crops by taking minimal space, makes work easier for farmers in growing of plants, and also consumes less amount of water when compared to traditional method resulting in conservation of water.

[...] Read more.
Development of a Mobile Liquid Spraying Machine for Small and Medium Scale Crop Production

By Olayinka Mohammed Olabanji

DOI: https://doi.org/10.5815/ijem.2022.04.02, Pub. Date: 8 Aug. 2022

This article presents the design, simulation, fabrication and performance evaluation of a liquid spraying machine for application of pesticides in a small and medium scale crop plantation. In this article, components of the conceptualized spraying machine were modelled and assembled in SolidWorks CAD environment. The modelled components were designed in order to obtain design parameters for simulation. An extensive simulation on the stress and strain analysis was carried out on the designed components. The significance of the simulation is to predict the structural integrity and performance of the component parts of the machine before fabrication. The components were fabricated from locally sourced material in order to ensure a lower cost of production. The fabricated spraying machine was tested and the performance indicated that a field efficiency of 79% is obtainable in an average time of 1374 s to spray a maize crop field area of 1813 m2 having an average crop height of 0.52m. Further observations from the performance analysis also show that the field efficiency of the spraying machine drops to a value of 75% when used in a crop field area of 2206.3 m2. This is an indication that the spraying machine’s efficiency will reduce as the field area increases. In essence, the significance of the approach presented in this article is to ensure that the simulation predicts the performance of the design and the fabrication of the spraying machine using locally sourced material will ensure lower cost of fabrication.

[...] Read more.
Towards the Development a Cost-effective Earthquake Monitoring System and Vibration Detector with SMS Notification Using IOT

By Shaina Delia G. Tomaneng Jubert Angelo P. Docdoc Susanne A. Hierl Patrick D. Cerna

DOI: https://doi.org/10.5815/ijem.2022.06.03, Pub. Date: 8 Dec. 2022

As one of the countries situated in the Pacific Ring of Fire, the Philippines suffers from an inexhaustible number of natural disasters every year. One of the most destructible ones is the occurrence of earthquakes. Because of the high damage that earthquakes incur, along with their inevitability and unpredictability, developing effective methods of earthquake damage mitigation as well as disaster preparedness is imperative to lessen the negative impacts it is capable of producing in communities. One efficient way of doing this is by implementing an earthquake early warning (EEW) system that is capable of sending message alerts to receivers to warn them in the event of a hazardous earthquake. With this objective, this study centers on creating an earthquake detector with SMS messaging to function as an EEW system with an added advantage of being low-cost to make it more accessible to the public. Using electronic components based on an Arduino Mega 2560 and a Global System for Mobile Communications (GSM) module, the earthquake detector and its alert message system were created. A series of tests in different locations across Butuan City was then performed to assess the device’s accuracy in measuring different Intensity levels when subjected to surface vibrations. Comparative analysis showed that its recorded values. Corresponded with the values obtained from accelerometer-based mobile applications. In conclusion, the study was deemed functional in its ability to detect low and high surface vibrations, which proves that it is successful in detecting earthquake tremors and vibrations in the event of an earthquake.

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Early Detection of Dementia using Deep Learning and Image Processing

By Basavaraj Mali Patil Megha Rani Raigonda Sudhir Anakal Ambresh Bhadrashetty

DOI: https://doi.org/10.5815/ijem.2023.01.02, Pub. Date: 8 Feb. 2023

Dementia is the world's most deadly disease. A degenerative disorder that affects the thinking, memory, and communication abilities of the human brain. According to World Health Organization, more than 40 million people worldwide suffer from this illness. One of the most common methods for analyzing the human brain, including detecting dementia, is using MRI (Magnetic resonance imaging) data, which provides insight into the inner working of the human body. Using MRI images a deep Convolution neural network was designed to detect dementia, we are utilizing image processing to help doctors detect diseases and make decisions on observation, in an earlier stage of the disease. In this paper, we are going to get to the bottom of the DenseNet-169 model, to detect Dementia. There are approximately 6000 brain MRI images in the database for which the DenseNet-169 model has been used for classification purposes. It is a Convolution Neural Network (CNN) model that classifies Non-Dementia, Mild Dementia, Severe Dementia, and Moderate Dementia. The denseNet-169 model helps us determine Dementia disease. And also present the 97% accuracy for clarification of disease is present in the patient body. we are conducted this survey for providing effective disease prediction model for physicians to conclude that the disease stage is accurate and provide proper treatment for that.  

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