International Journal of Engineering and Manufacturing @ijem
Journal articles - International Journal of Engineering and Manufacturing
All articles: 649
An Efficient Genetic Algorithm Orienting to the Protein Fold Prediction
Scientific article
Proteins are amino acid chains that acquire their biological and biochemical properties by folding into unique 3-dimensional structures. The biological function of a protein is dependent on the protein folding into the correct, or "native", state. At present, there are so many ideas to predict the structure of the protein folding. This paper first present the concept of protein folding and how is significant to study protein fold prediction. In this paper we join the simulated annealing factor into Parallel Genetic Algorithm and use this hybrid Parallel GA to predict the structure of protein fold. The revised algorithm is more efficient than traditional Genetic Algorithm and simulated annealing algorithm.
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An Empirical Study on Consumers’ Continuance Intention Model of Online Group-buying
Scientific article
This paper examines the factors influencing consumers’ intention to continue using a new pattern of online group-buying, which benefits consumers from high discounts and is prevalent in many countries of the world since 2009. A theoretical model is proposed based on Expectation-Confirmation Model of IS Continuance (ECM-ISC) from the previous IS continuance literature, integrating with e-recovery service quality, perceived risk and two external variables. Data collected from a questionnaire survey of OGB consumers provides empirical support for the proposed model. The results indicate that consumers’ satisfaction with prior use and perceived usefulness significantly influence consumers’ continuance intention. Consumers’ satisfaction is determined primarily by consumers' confirmation of expectation from prior use and secondarily by perceived e-recovery service quality. Further, confirmation also has a significant influence on post-acceptance perceived usefulness. Confirmation, in turn, is significant influenced by both product quality and information quality. Some suggestions for practitioners are also offered.
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An Improved DWT-SVD Based Robust Digital Image Watermarking for Color Image
Scientific article
The Digital Watermarking Technique has gained its importance due to its ability to provide the secure mechanism for copyright protection and authenticity of the digital data in this high growing internet and computer technology where the tampering and distribution of digital data illegally from unauthorized users is inevitable. For these two important properties of Digital Watermarking, i.e. Robustness and Imperceptibility of watermarked image must take into consideration. In this paper, invisible robust digital watermarking is proposed using Discrete Wavelet Transform and Singular Value Decomposition in YCbCr Color space. The performance of the proposed algorithm is compared with some previous works and results found are more robust against various geometric attacks.
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An Improved Grey Wolf Optimization Algorithm for Liquid flow Control System
Scientific article
Liquid flow in a process industry is one of the significant factors which should be controlled to get the better quality and decrease the expense of generation. Customary methodology includes manual tuning of the input process parameter to obtain the required flow rate is tedious and exorbitant. Notwithstanding, estimation of a precise computational model for fluid stream control procedure can fill in as elective methodology. It is only a non-straight enhancement issue. As a contextual investigation, the WFT - 20-I measure control arrangement for flow rate measurement and Control issue is thought of. In this work we proposes a hybrid improved particle swarm optimization (PSO-GWO) used to start the people's position, which can build the decent variety of the wolf pack, balance the global and neighborhood search capacity of the calculation and improve the intermingling pace of the calculation contrast with the Gray wolf enhancement (GWO) and Particle swarm advancement (PSO). Non linear models are improved utilizing those recently proposed streamlining strategies. Additionally all the utilized optimization techniques can anticipate the fluid stream rate with good exactness. The outcomes were investigated by utilizing the root mean square error (RMSE), exactness, and the different measures to evaluate the level of identification performance of the liquid flow contextual analysis model. The trustworthiness of the present models was compared with the past model for similar subsystems utilizing competitive intelligent methodologies. The measurable examination of the acquired outcomes produced the proposed HPSOGWO has most elevated generally speaking proficiency (i.e.99.96%) and it beat the others strategies for the majority of the instances of demonstrating for fluid stream control process. The outcomes of the present model show that the proposed approach gives prevalent demonstrating execution and outflanks its rivals.
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An Improved HARQ Mapping Mechanism in LTE-Advanced System
Scientific article
Based on the analysis of the current hybrid automatic repeat request (HARQ) mapping mechanism and the corresponding problems in LTE-Advanced system with carrier aggregation, this paper designs an improved HARQ mapping mechanism, in which an idea of semi-static mapping is introduced. The simulation results validate its effectiveness in improving system performance through the trade-off between diversity gain and overhead.
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Scientific article
Virtualization technology play a vital role in cloud computing. In virtualization environment multiple applications can run at a same time. VM migration is one of the important features of the virtualization, which allows application to be transparently migrated along with their execution environments across physical machines. VM migration consists of four steps. These steps are source PM selection, VM selection from the selected PM in first step, target PM selection for placing the selected VM and selecting method for transfer VM data. In our proposed approach we are focusing on the last step i.e., choosing method for transferring VM data. In this paper proposed approach is divided into two phases. First phase filter all pages which is modified in the last iteration and unmodified in the current iteration. In the second phase, page is divided into two types i.e., high dirty page and normal based on the modification in the last few iterations. For all filtered pages in the first phase now we check the number of times when the page is modified from the history record. If the page is modified more times the page will not be send in the current iteration otherwise the page will be send to the destination VM. To evaluate the performance of the proposed approach it is implemented in CloudSim simulator and compare with the existing time series based pre-copy approach in term of total migration time and down time. Experiment result shows that proposed approach gives better result as compare to the base approach.
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An Innovative Leukemia Detection System using Blood Samples via a Microscopic Accessory
Scientific article
Leukemic patients are in a rapid increase. Hence, the use of microscopic images of blood samples through visual inspection to identify blood disorders has increased, opening the door for computerized techniques for detecting leukemia. This project applies computer vision techniques to increase the accuracy and speed of detection from periph-eral blood. It also enhances visualization by providing an appropriate supplement to traditional microscopy. A micro-computer (Raspberry Pi) was well programmed in Python for analyzing images with the help of a Raspberry Pi camera and a touch screen as an alternative to the eyepiece. To achieve diversity and seek for more accuracy, image datasets for this project were obtained from various resources. These datasets were then analyzed through image processing techniques to detect leukemia cells. This detection process involves resizing cells to a standard size, noise removal by linear scaling filter, global-local contrast enhancement, segmentation of white blood cells (WBCs) using marker-controlled watershed algorithm, overlapping detection and separation using watershed and k-means clustering algorithms, and extraction with selection of the most relevant features from cells. These features were then imported into the Support Vector Machine (SVM) model which resulted in an accuracy of 93.2773%. A standalone desktop application with a suitable graphical user interface (GUI) was implemented. It was then uploaded into the Raspberry Pi, some code lines were rewritten for dealing with the camera, the hardware was designed and implemented, and then formal experiments were conducted resulting in the detection of leukemia in 5 samples out of 6. This implies that precise detection can be implemented with different data taken in various imaging conditions.
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Scientific article
Optical Character Recognition Systems (OCR) is a tool that helps computers read text from pictures of papers. It makes it easier for machines to understand what the words say without needing a person to read it out loud. It allows for easy digitizing of historical documents, archival material, and medical records thereby saving on their retrieval times. However, the accuracy of OCR systems heavily relies on the quality of the input images. To negate the contribution of the quality of input images to the accuracy of OCR systems, in this paper, we propose an integrated image pre-processing pipeline integrated with the OCR systems that enhances the quality of input images for efficient image to text conversion. This method results in an easily understandable text output with a lower Character Error Rate (CER) in comparison to the current methods. In addition, we explore a technique for converting text from a document or image into machine-readable form and then converting it to audio output using gTTS, a Python library that interfaces with Google Translate's text-to-speech API. We assess the effectiveness of this approach and illustrate that it substantially enhances OCR precision when compared to other existing methods. This paper presents a clear overview of the growth phases and significant obstacles, accompanied by compelling comparisons of results achieved through various methods.
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Scientific article
Rice isn't just essential to global food security; disease outbreaks and poor soil health management stymie its growth. Often, traditional rice farmers lack the knowledge or resources to diagnose plant diseases and monitor soil conditions in real time. Existing solutions typically focus on either soil health monitoring or disease detection, but not both, leaving farmers unable to respond to the identified threat. This project will mitigate this by combining real-time monitoring, disease classification, and a recommendation system into a single solution. Poor disease detection and inadequate health examinations of soils commonly result in decreased rice productivity. The proposed research is also focused on the development of an intelligent system equipped with IoT sensors to monitor soil parameters such as moisture, nitrogen concentration (NPK), and temperature in real time, and a machine-learning-based system capable of classifying 15 different rice diseases. The system also includes a recommendation engine that provides actionable recommendations for treating an illness, making it a complete soil and crop health management tool. The system is based on a transfer learning model (MobileNetV2) that classifies rice illnesses using image classification. The model was trained on 22,688 images of rice diseases, achieving a detection accuracy of 96.42%. The system was also highly accurate for monitoring soil health, with minimum standard deviations of 0.20% and 0.22 for soil moisture and nitrogen levels, respectively. The results obtained reflect the effectiveness of the developed system in enhancing the farming process by enabling farmers to identify diseases at early stages and improve soil conditions. Lastly, the methodology enhances rice production, reduces crop losses, and helps achieve global food security.
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Scientific article
Birth marks the evolution from a fluid environment to one in which the baby breathes. Respiration difficulties after birth and in the first hours of life are common. Some babies will experience more severe respiration problems, and some of these will require medical care that only expert physicians can offer. In the first six hours of life, asphyxia is recognized. Neonatal asphyxia is a syndrome categorized by the deprivation of oxygen supply at birth and causes many morbidities and mortalities among newborns. Improved results depend on early detection and action. Using the African Vultures Algorithm that performs hyperparameter optimization expands accuracy and effectiveness in supporting the model. This integration improves the detection and sympathetic of neonatal asphyxia by using African Vultures Algorithm's capability to find fine results in high-dimensional places as well as the capability of LGNN to method structured clinical data. Herein, our model excels over present deep learning models and computer vision techniques for accurate detection of neonatal asphyxia. LGNN model achieved an accuracy of 98% in Dataset 1 and 96.5% in Dataset 2. F1-Score of the proposed model was achieved as 97.6% in Dataset 1 and 96.7% in Dataset 2. In Dataset 1 and Dataset 2, LGNN model as long as 97.9% and 97.1% precision, individually. In Dataset 1 and Dataset 2, the proposed (LGNN) provided 98.8% and 97.7% recall, one-to-one. Efficiently exploit the graph structure for feature aggregation and inference by employing a linear graph neural network architecture. Improved newborn outcomes are made possible by the suggested model, which is a potent diagnostic tool that also helps clinicians comprehend underlying pathophysiological causes.
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Scientific article
A clear, statistically sound, yet easily understandable breast cancer diagnosis is a difficult issue in all healthcare systems, because early stages of breast cancer are critical in therapy success and long-term survivability. This machine-learning-based breast cancer classifier, in a statistically justified, rigorously experimentally validated way, classifies a set of 569 breast cancer cases with 9 cytological features for breast cancer diagnosis. The classifier uses a rigorous set of data cleanup measures, including missing-value substitution, correlation-based feature reduction, and projection into principal component space, to achieve high data quality, reduce redundancy, and enhance feature usefulness. Five supervised classifiers, in a widely accepted train-test model using an 80:20 random sample split and 5-fold cross-validation, are fitted and evaluated using Accuracy, Precision, Recall, F1-score, and Area under the ROC curve. In these tests, the Random Forest classifier got the best result, with 95.84% Accuracy, 95.31% Precision, 95.12% Recall, 95.21% F1-score and 0.982 area under the ROC curve; in a statistically sound consistency test using cross-validation, its mean accuracy reached 95.96% with a small standard deviation of 0.43. To provide a clear, interpretable indication of which features truly matter, we performed a feature-importance analysis on the best classifier, the Random Forest model. Results show that the expression levels of Bland Chromatin, Single Epithelial Cell Size, Normal Nucleoli, Uniformity of Cell Shape, Uniformity of Cell Size and Bare Nuclei are closely related to breast cancer diagnosis; this is almost the same as the clinical diagnosis findings, and very naturally suggests that abnormalities of cellular morphology and nuclei are major symptoms of breast cancer. In comparison, prior research may neglect validation and efficiency comparisons or focus only on the classifier's accuracy. Our method combines multiple levels of assessment (statistical data-by-data validation, feature importance, cross-validation, and comparison of different classifiers using ensemble learning) into a single evaluation system. This combined approach not only enhances predictive capability but also makes the entire setup more explicitly interpretable from a clinical perspective, thereby making it more suitable for health care decision support. Given the strong classification performance, interpretability, and validation suggested above, the model would help physicians detect breast cancer very early, reducing the risk of misdiagnosis.
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Scientific article
Fake news detection focuses on identifying and preventing the spread of misleading or false information. It is crucial for maintaining the integrity of public discourse and protecting individuals from the harmful effects of misinformation. By ensuring the correctness and reliability of the information, the fake news detection hinders the loss of trust in the media, institutions, and public communication channels. The fake news detection system suggested is in the process of data acquisition where news stories are either manually or automatically retrieved from the net via web crawlers. The collected data later filters the information so it will use only credible sources. Phase two consists of the pre-processing phase using BERT, wherein the data will be tokenized and mapped into contextual embeddings that reflect the semantic meaning of words. Phase three is about engineering features using methods like TF-IDF and Word2vec to fine-tune the embeddings and label the important textual features. The final Phase of Classification occurs using the engineered features such that BERT-generated outputs are fine-tuned and passed through softmax functions to ascertain whether the news is fake or real. This holistic and all-encompassing approach integrates advanced natural language processing with feature engineering for an effective system concerning detection of fake news accurately. The model achieved remarkable results over various phases. Training accuracy went from 75% up to those above 95% whereas test accuracy tips above 90%, soaring from below 70%. The model's performance was validated with a balanced confusion matrix and a high ROC AUC of 0.94. Throughout different phases, accuracy, precision, recall, and F1-score increased, reaching 97.0%, 96.7%, 96.8%, and 96.9%, respectively, in the final classification phase, demonstrating robust and reliable detection capabilities.
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Scientific article
Nonpolluting, renewable energy can be harnessed from the sun using solar thermal concentrator and photovoltaic systems. A novel asymmetric oblique compound conical solar concentrator was designed and constructed with automatic solar tracking system. The experimental study of the developed system was carried out to inspect the efficiency of the designed solar concentrator and automatic azimuth and altitude tracking. In this solar tracking, the RTC (Real Time clock) based active solar tracking method is used to track the sun in order to attain the maximum temperature. A simple microcontroller based control system is used to control this open loop solar tracking system, further the concentrated thermal power was measured using both the K-type temperature sensor and thermometer gun. The temperature obtained at the receiver of solar concentrator is sent to the measurement unit using GSM wireless module available at remote location. The monitoring unit receives the temperature values and recoded them regularly. Besides, the maximum average solar thermal power of the system was observed to be during 10.00 AM to 3.00 PM. This temperature is useful for various heating applications.
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Scientific article
CMOS is a technology that has revolutionized the field of electronics. Over the time the processing technologies and design methodologies of CMOS devices have proved to be in full swing with the Moore’s law and the miniaturization paradigm. However, after surviving for more than five decades, CMOS is now facing challenges to live through the submicron ranges. The scaling in CMOS has reached a higher limit, showing adverse effects not only from physical and technological point of view but also from material and economical perspective. This drift inspires the researchers to look for new promising alternatives to CMOS which vow better performance, density and power consumption. One of the promising alternatives to digital designing in CMOS is the Quantum-dot Cellular Automata (QCA). QCA is a technology that involves no current transfer but works on electronic interaction between the cells. The QCA cell basically consists of quantum dots separated by certain distance and the entire transmission of information occurs via the interaction between the electrons localized in these quantum dots. In this paper the limitations to CMOS in submicron range and concepts for designing in QCA have been discussed. Further the building blocks are explained theoretically as well as using QCA Designer implementations with focus on cell interaction and clocking mechanisms.
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Scientific article
Sugarcane bagasse ash (SBA) is a solid waste obtained from the boilers of sugar factories after the combustion of sugarcane bagasse. Sugarcane bagasse ash mainly consists of amorphous silica and can be used as a supplementary cementitious material in the production of concrete. In the present study, the physical properties of sugarcane bagasse ash, namely, particle size distribution, surface area, specific gravity, morphology and chemical properties like elemental composition of SBA and ordinary Portland cement (OPC) have been investigated. The microstructural observations were made using Scanning Electron Microscope (SEM). Elemental compositions were analyzed and images coupled to microanalysis by Energy dispersive spectroscopy (EDS). The microstructure observations and elemental characterization of cement concrete matrix was also carried out after 28 days of curing. Blended cement concrete specimens were prepared using SBA up to 30% in regular intervals of 10% by weight of pure Portland cement, coarse and fine aggregate, tap water and superplastisizer. SEM results indicates that substitution of SBA particles by Portland cement produces dense matrix as compared to control mix and EDS analysis shows fluctuations in calcium and silica concentrations as the cement replacement level increases in the blended cement concrete.
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Analysis and Numerical Simulation of Deterministic Mathematical Model of Pediculosis Capitis
Scientific article
In this work, we formulated a deterministic mathematical continuous time model for the detection and elimination of a non-life threatening disease (head lice) by considering a fixed (constant) population size during the epidemic period. The formulated mathematical model was normalized for easy analysis, the model’s properties were obtained, as well as the disease free equilibrium point, the local stability and the basic reproduction number. We adopted MATLAB programing language to carry out the numerical simulation of the nonlinear ordinary differential equation, as well as simulation of different model state variables and effects of different model parameters on the state variables over time. Our result shows that early detection and treatment will lead to termination of the disease.
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Analysis and Treatment of Water Contaminated by Petroleum Products
Scientific article
The unparalleled rate in population explosion and advancements in technology has created alarming impacts on the society. This has urged the need for vigilant and efficient data mining of these phenomena. Petroleum being an indispensible part of life has escorted to several detrimental results. Air and water are the major elements which fall prey to this. The present study involves rigorous scrutinizing with statistical analysis of test data carried out on water samples collected from petrol pumps and automobile service stations for the determination of nature of configuration. The samples were collected at regular time intervals and at the fixed place irrespective of the weather and climatic conditions that prevailed. The haphazardness or fuzziness of the amount of contamination present in water recipients due to petroleum products is being evaluated using regression analysis and the runs test. It is found that organic and inorganic chemicals are more in the collected sample that leads to chemical oxygen demand (COD) and the same was reduced using activated rice husk as an adsorbent. A case study involving its harmful effects on concrete is accessed via Scanning Electron Microscope (SEM) tests.
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Analysis of CT DICOM image segmentation for abnormality detection
Scientific article
The cancer is a menacing disease. More care is required while diagnosing cancer disease. Mostly CT modality is used for Cancer therapy. Image processing techniques [1] can help doctors to diagnose easily and more accurately. Image pre-processing [2], segmentation methods [3] are used in extraction of cancerous nodules from CT images. Many researches have been done on segmentation of CT images with different algorithms, but they failed to reach 100% accuracy. This research work, proposes a model for analysis of CT image segmentation with filtered and without filtered images. And brings out the importance of pre-processing of CT images.
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Analysis of Cost-Effectiveness of Grid-Based and Off-Grid Electrification Designs in Nigeria
Scientific article
The significance of electricity access to man's socioeconomic development cannot be overstated. putting competing demands on limited resources. Electrification as one of the demands requires efficient use of these scarce resources using appropriate electrification plans that would improve the attractiveness of policymakers to electrification which in turn brings the development to rural and extending communities. There is a need for a rural-electrification planning structure that considers social equity and balanced regional development through the most cost-effective investment options delivering a reasonable level of service. The objectives of the study were to compare two electrification plans including extending the existing grid distribution system and implementing renewable energy-based rural electrification (RE) for two dissimilar communities. Grid extension, which has a lower initial installation cost and is a suitable option where there are commercial and industrial consumers, is the most economically feasible electrification plan for a community that is close to the existing power grid or one that is growing, according to the study's findings. However, results of the study also show that off-grid electrification model is more cost-effective than a grid extension for electrifying far-from-the-grid un-electrified areas. This model has a higher functional value, economic value, and suitability for remote communities because these areas have fewer infrastructures and off-grid systems are typically designed for basic lighting and residential applications.
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Analysis of Exposure Uniformity on Cells in Vitro with 900MHz TEM Chamber
Scientific article
The TEM chamber is a proposed exposure device for experiments detecting cell responses to electromagnetic irradiation at the mobile frequency of 900 MHz. In this paper, we extend the traditional propagating-wave exposure to the standing-wave exposure. Four standing waves with different properties are produced respectively by two types of passive loads and two types of active loads. Systematically, the maximum E field and maximum H field of the standing waves are used to expose Petri dishes of three sizes. The standard deviation (SD) of the specific absorption rate (SAR) is calculated for the cell layer and the cell suspension. We compare the SD data to determine the best arrangement with the highest exposure uniformity while considering the exposure intensity. The investigation uses the numerical simulation of the finite-difference time-domain (FDTD) method with the result accuracy supported by comparison to theoretical data on the field distribution in the TEM chamber.
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