International Journal of Engineering and Management Research
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Optimization of Shipping Cost of Cement for Selected Construction Projects of Julius Berger
Get PDFThe Transportation modelling technique was adopted in solving the transportation problem of shipping cement from three supply locations (cement depots) to three demand locations (construction sites) for Julius Berger construction company in Port Harcourt. The research was carried out for standard 900 bag truck load of cement from the selected cement depots. Transportation costs of the cement were analysed and the initial feasible solutions obtained, using the North-West Corner, Least Cost, and Vogel’s approximation methods. The Least Cost method resulted in the most feasible cost. Finally, the optimum shipping cost was attained, using the Stepping stone method, which resulted to an amount of $2,259 or (₦1,716,840 as at April 2023). Alternatively, Microsoft Excel solver was used on a computer in order to draw a comparison of the results. This gave exactly the same results
Agriculture Automation System using Machine Learning and Internet of Things
Get PDFAgriculture balances both food requirement for mankind and supplies indispensable raw materials for many industries, and it is the most significant and fundamental occupation in India. The advancement in inventive farming techniques is gradually enhancing the crop yield making it more profitable and reduce irrigation wastages. This project aims at making use of evolving technology i.e., IOT and smart agriculture using automation. Monitoring environmental conditions is the major factor to improve yield of the efficient crops. The feature of this project includes development of a system which can monitor temperature, humidity and moisture content of the crops in agricultural field through sensors using Arduino board. Decision tree algorithm, an efficient machine learning algorithm is applied on the data sensed from the field in to predict results efficiently which helps in decision making regarding water supply in advance
Evaluation of the Socio-Economic Survey and Statistical Metrics for Lambhvel Village, Gujarat State
Get PDFThis study is aimed to research the life of people of village Lambhvel, Gujarat, awareness about various government schemes, about their family, and what they are expecting from the government for the betterment of their life. This survey is part of NSS Village Camp 2023 which was held from 22nd May to 27th May at Lambhvel, Anand, Gujarat. This survey is done on 23rd May. Which total of the 40-person personal interview has taken at Lambhvel. Of 40 persons 30% are women and 70% are men. Of which 40% are self-employed and 15% are farmers only. 87.5% are having ration cards and out of it, 70% of people are taking ration from it which means the BPL rate is very high in the village. 92.5% are having Aadhar card. An average of 5.075 persons are living in one house and each house has an average of 0.75 Girls and 1.075 boys in each house. And 1.825 Children in each house, which means there are 2 adults and 2 children in one house but we can also see that some house has 5 or 8 children in one house. The average income of one house by our survey is Rs 85300. This village has very less awareness about any government scheme because 17.5% of people are taking benefit of any Government scheme and most of the people out of 17.5% are taking benefit of Pradhan Mantri Ujjwala Yojana (PMUY) and 35% of people having Maa Card/ Aayushman Card. And most people want cleanliness in the city and a better education system in their village for their children
Augmented Fake News Detection Model Using Machine Learning
Get PDFIn today\u27s time, fake news has become like a virus for any social media platform, which destroys the uniqueness of that platform itself. Because a fake news is sent to hurt the sentiments of any person, society or religion. That\u27s why today we need a computer artificial intelligent based model that can detect any fake news before it is posted. All social media platforms have worked in this direction, but somewhere it seems that their model is insufficient to catch such fake news. Because some social media companies have tried to decide whether the news is fake or not on the basis of some predefined datasets. And some companies have searched only on the keywords of the news that the news is fake. This proves that we need a model that is based on the old dataset, and the current news dataset and keywords. Along with this, it is also important to pay attention to the timing, place and type of news, while these things are not taken care of in the existing models. So I would like to include all these parameters in my model to help detect fake news. If we recognize the Fake News at the right time, then we can take the right steps at the right time. Computer based models are not always accurate, so the model should also have the facility to compare with real news. If news is compared with current news then 76% of fake news can be detected at the same time. Therefore, the model should also have the facility of comparative review
Fake Profile Detection on Social-Media
Get PDFIn this generation, social media platforms such as Twitter, Instagram, Linkedln, and others play an important role in our everyday lives. The whole world is actively involved. However, it must also address the problem of false profiles. The majority of fake pattern are generated by human or robots or cyborgs built to spread for misinformation, data piracy and identity theft. Therefore, In this article, we will discuss a model name as Detection of fake profile on social media model, which will be differentiate between fake and real profile on twitter based on visible features like, friend counts, follower counts, status counts, and more by using various machine learning classification methods. The dataset will be used twitter profile and we will Taking the Machine learning classification model like, Neural Network (NN), Random Forest, XG-Boost, and LSTM for determining the authenticity of a social media profile and for used implementation language is Python3 along with all the required libraries like, pandas, NumPy, and Sklearn etc
Software Defect Prediction Based on Support Vector Classifier and Rule Mining
Get PDFSoftware defect prediction plays a crucial role in ensuring the quality and reliability of software systems. Rule mining-based approaches have gained popularity in this domain as they provide insights into the relationships between software metrics and the occurrence of defects. This abstract presents an overview of software program defect prediction based totally on rule mining.
The process begins with the collection of historical data from previous software projects, encompassing defect records and associated software metrics. Relevant features are extracted from the data, including static code analysis metrics, change metrics, process metrics, and dynamic metrics. The collected data is then prepared by addressing data quality issues, handling missing values, and splitting it into training and testing sets.
Using a rule mining algorithm, such as association rule mining or decision tree induction, patterns and rules are discovered that correlate the software metrics with defect occurrences. The goal is to identify rules with high support and confidence, indicating strong associations between specific metrics and defect-prone areas of the software.
The discovered rules are evaluated using appropriate metrics, such as precision, recall, F1 score, or AUC-ROC, to assess their effectiveness in predicting defects. Once validated, the rules are applied to new software projects, where the software metrics are fed into the rule model to classify components as defect-prone or defect-free.
Continuous validation and improvement of the defect prediction model are necessary to ensure its accuracy and performance. This involves incorporating new data, refining the rules or metrics, and adapting the model to changing software development practices.
Software defect prediction based totally on rule mining offers a valuable approach for identifying potential defects early in the software development lifecycle. By leveraging historical data and discovering meaningful relationships between software metrics and defects, organizations can proactively allocate resources and implement preventive measures to improve software quality and reliability
Design and Simulation of Switched Capacitor Based Multilevel Inverter with Reduced Components
Get PDFFunction of inverter is to convert DC signal to AC signal. Boost voltage and to improve the power quality and capacity with sinusoidal type waveform is obtained by use of multi-level inverter. The Multilevel inverter increases number of devices and other components, switched-capacitor (SC) units have ability to boost the input voltage considerably switched capacitor-based inverter with reduced switch is proposed in this paper without inductor and transformer operation with voltage boosting feature and inherent capacitor self-voltage balancing performing with no interference. The structure proposed in this paper is capable of producing 17 level output waveform with double voltage gain using a dual voltage source with reduced switches and other equipment’s. The inverter switching pulse is modulated using PD-PWM technique, which enables a high quality output waveform
The Influence of Social Media Engagement on Various Outcomes - Brand Equity, Value Co-Creation and E-Word of Mouth
Get PDFSocial media users\u27 rising acceptance of them demonstrates that customers are developing strong emotional relationships with them. The purpose of the study to The Influence of Social Media Engagement on Various Outcomes - Brand Equity, Value Co-Creation and E-word of Mouth
The Impact of the Information Security Policies on Organizational Performance
Get PDFThis study aims to explore the importance of enforcing a solid information security policy on the different institutions, giving a conceptual overview on the relationship between information security practices and organizational performance, presenting global indicators in peer-reviewed journals and records of information security firms and platforms, comparing best practices to productivity and improved performance, Analysing various risks and proposing the best solutions and appropriate protection techniques by following the best various strategies and techniques, propose the mitigation strategic prevention and hybrid techniques to protect the information due to their less expensive and simplicity
Utilization and Development of Libas (Spondias Pinnata) Spray-Dried Powder
Get PDFIn this study, the effect of maltodextrin concentration on the physico-chemical, sensory and storage characteristics of Libas (Spondias pinnata) powder produced by spray drying was investigated. Initially, fresh Libas leaf extracts were drawn out in distilled water heated at 100°C for 30 minutes. 10, 20 and 30% maltodextrin was added to the solution prior to sieving. These solutions were then atomized in a pilot scale centrifugal spray dryer at an inlet temperature of 180°C, material cooling temperature of 70°C, fan operation frequency of 60 Hz, atomizer operation frequency of 300 Hz and mean outlet temperature of 125°C. Spray dried Libas leaf powder was analyzed for product yield, pH, moisture content, ash content and solubility time. The level of acceptability of the spray dried Libas leaf powder was also assessed in terms of appearance, taste, aroma, mouthfeel and overall acceptability using a 9-point hedonic scale conducted on thirty (30) sensory panelists. Findings of the study revealed that maltodextrin concentration significantly affected various physico-chemical, sensory and storage characteristics of spray dried Libas leaf powder. Product yield, solubility and residual moisture content were found to be significantly improved by an increase in maltodextrin concentration. However, it was also found that there is a decrease in the ash content and level of acceptability of the spray dried samples with an increase in maltodextrin concentration. Storage of the spray dried Libas leaf powder for six (6) months significantly affected pH and moisture content. No significant difference in sensory characteristics was detected among treatments indicating that powder quality is still acceptable during the six (6) months storage period