Engineering Commons^™

Survey: A Study On Image Encryption Using Dna In Bioinformatics, Rana M. Zaki, Zaed S. Mahdi, Matheel E. Abdulmunim

Journal of Soft Computing and Computer Applications

One area of study between computer science and biology is bioinformatics, which deals with methods for collecting, processing, storing, and evaluating biological data. Sequences of RiboNucleic Acid (RNA), DeoxyriboNucleic Acid (DNA), and proteins make up biological data, which has a wide range of uses in domains such as feature extraction, data segmentation, data security, and more. In cryptography, DNA sequences are used as data carriers, enhancing the unique properties of biomolecules. This approach involves using DNA sequences to enhance the security of confidential data that must be transmitted over networks or stored securely. Several DNA-based security techniques have been developed, …

New Feature Selection Using Principal Component Analysis, Zaid Mundher Radeef, Soukaena Hassan Hashem, Ekhlas Khalaf Gbashi

Journal of Soft Computing and Computer Applications

Dimensionality reduction techniques streamline machine learning by reducing data complexity, improving model accuracy, and cutting computational costs. They remove noise and irrelevant features, making models faster and more efficient. These techniques also enhance data visualization and interpretation by condensing data into manageable, insightful dimensions. Ultimately, dimensionality reduction leads to simpler, more interpretable models without sacrificing critical information, making it a cornerstone of efficient data analysis and machine learning applications. Theoretically, feature extraction tends to create new features that encapsulate more information by combining multiple existing features, resulting in more concentrated and informative features. In contrast, feature selection involves choosing a …

Development Of A Hybrid Methodology Of Deep Learning And Machine Learning For Lung Nodule Detection In Medical Computed Tomography Images, Zaed S. Mahdi, Rana M. Zaki, Alaa Kadhim Farhan, Negar Majma

Journal of Soft Computing and Computer Applications

Deep learning and machine learning play an important role in the medical field, helping doctors make accurate, fast and effective diagnosis. Despite the progress achieved in the use of modern technologies in detecting cancerous nodes, current studies still suffer from some challenges and limitations that must be addressed to obtain high efficiency in identifying cancerous nodes. These challenges include using image pre-processing, combining deep learning and machine learning techniques, and constantly adapting to clinical changes, in order to address this. A hybrid methodology has been proposed for detecting cancerous nodules in the lung in medical Computed Tomography (CT) images. It …

Enhancing Image Classification Using A Convolutional Neural Network Model, Zena M. Saadi, Ahmed T. Sadiq, Omar Z. Akif, Marwa M. Eid

Journal of Soft Computing and Computer Applications

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as …

Improved Rapidly-Exploring Random Tree Using Firefly Algorithm For Robot Path Planning, Dena Kadhim Muhsen, Firas Abdulrazzaq Raheem, Yuhanis Yusof, Ahmed T. Sadiq, Faiz Al Alawy

Journal of Soft Computing and Computer Applications

In robotics, efficient path planning makes robots work independently and move through changing environments over time. This study combines the Rapidly-exploring Random Tree (RRT) architecture with the Firefly Algorithm (FA) to make robot’s path-planning better. The proposed ERRT-FA, which stands for "Enhanced RRT with Firefly Algorithm", generates better routes using Firefly social habits. Plan routes using Firefly social habits can effectively aid in exploring configuration space. The role of the FA is to enhance the RRT algorithm by providing an optimized exploration of the search space, ultimately leading to optimizing the path found by the RRT algorithm and better paths …

Foreword From Editor - 16th Edition: Toward An Inclusive Community Engagement, Yandi Andri Yatmo

ASEAN Journal of Community Engagement

This edition of AJCE defines and elaborates on the idea of inclusive community engagement as a means to involve the community in a meaningful process. ‘Inclusive’ refers to the principles of encompassing everyone, all individuals and groups alike, regardless of their identity, background, characteristics, needs, and perspectives, thereby ensuring that all voices are represented (Hodkinson, 2011). This practice extends beyond individuals with disabilities and embodies broader ideas of equality. Inclusive engagement plays a crucial part in fostering a constructive dialog that incorporates diverse perspectives within a community. Such engagements prioritize community participation in the decision-making process that affects their well-being …

Exploring Student Satisfaction In Learning With Podcast Applications: A Qualitative Study Based On Open-Ended Questions, Indah Permatasari, Peny Meliaty Hutabarat, Erni Adelina

Jurnal Vokasi Indonesia

This study aims to explore student satisfaction with the use of podcasts as a learning medium in the Non-News Radio Production course. A qualitative approach was used, with three open-ended questions posed to students: (1) What different experiences did you have when listening to the course material via podcast?(2) Did listening to the course material through podcasts help you focus on understanding the material? And why? And (3) provide your opinion on the Adapto podcast material shared during the Non-News Radio Production course in the 4th semester. The data obtained was thematically analyzed to identify the main emerging themes. The …

First-Principles Study Of Ferroelectric Properties And Co2 Reduction Reaction Capabilities In Two-Dimensional Monolayers And Heterostructures, Mo Li

Dissertations

Two-dimensional (2D) materials hold significant potential for CO₂ reduction reactions (CO₂RR) due to their high surface-to-volume ratio. However, achieving high selectivity for desired products and overcoming limitations posed by scaling relationships remain challenging. Recent studies suggest that ferroelectric (FE) materials with switchable out-of-plane polarization (OOP) can effectively tune the adsorption behavior, thermodynamics, and kinetics of CO₂RR, offering promising solutions to these challenges. Using density functional theory (DFT) and the Berry phase approach, this work expands the family of 2D ferroelectrics by theoretically identifying Y₂CO₂, Y₂CS₂, and Sc …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

2024 Scholarly Productivity Report, Missouri University Of Science And Technology

Civil, Architectural and Environmental Engineering Scholarly Productivity Reports

No abstract provided.

Gas-Generating Reactive Materials: Design, Evaluation And Effect Of Morphology On Their Ignition And Combustion, Purvam Mehulkumar Gandhi

Dissertations

This work investigates optimizing gas-generating reactive materials, focusing on particle morphology's effect on ignition and combustion behavior. Metal-based gas-generating energetic materials (EMs) are promising alternatives to replace traditional CHNO compounds. Design of advanced metal-based EMs requires consistent ways of evaluating how their characteristics affect their ignition and combustion. Many relevant evaluation approaches exist; however, the results may be difficult to compare directly across different studies. Commonly, experimentalists ignite metal-based EMs in enclosed chambers and report pressures, P. However, direct comparison of these pressures is hindered by variations in the chamber volume, V, and the EM mass, m. To standardize the …

Decomposition Of Diisopropyl Methylphosphonate (Dimp) Exposed To Elevated Temperatures And Combustion Products Of Reactive Materials, Elif Irem Senyurt

Dissertations

The safe and efficient destruction of chemical weapon agents (CWAs) stockpiles remains a critical global challenge. Understanding the thermophysical properties, decomposition mechanisms, and interactions of CWAs with their environment is crucial for developing effective strategies to mitigate their threats. Diisopropyl methylphosphonate (DIMP) and Dimethyl methylphosphonate are commonly used nerve agent surrogates. This dissertation focuses on DIMP and DMMP, investigates their properties and behavior under conditions relevant to prompt defeat scenarios.

The thermophysical properties of DIMP and DMMP were experimentally characterized, including vapor-liquid surface tension (3-60 °C) and viscosities of neat and aqueous solutions. Results revealed significant non-ideal behavior in aqueous …

Optimizing Bioethanol Production From Solanum Torvum (Devil's Thorn): An Evaluation Of Conversion Efficiency And Invasive Plant Management Potential, Jaya Ashwin D S, Navaneeth K, Abhay Mahesh Baadkar, Supriya S Sundar, Bhoomika B J

Manipal Journal of Science and Technology

Solanum torvum, commonly known as the Devil’s thorn, is an invasive species present in India that causes negative ecological consequences. However, given its abundance and high starch content in the plant, it could be utilized as a potential feedstock for sustainable biofuel production. We aim to explore the feasibility of bioethanol production from S. torvum and its potential as a means of managing this invasive species. The study will take on a comprehensive approach, including techniques for optimizing starch extraction and improving its accessibility. Fermentation with suitable strains of microorganisms, and analysis of bioethanol yield. In addition, the study will …

Genetic Algorithm-Based Design Solution Of An Area Lighting Scheme - A Case Study, Prabhat Mishra, Arnab Ganguly, Amartya Roy, Mihir Kumar Manna, Abhik Hazra

Manipal Journal of Science and Technology

In this paper, a Genetic Algorithm (GA)-based approach is taken for the lighting design of a specified area. The design of an area lighting scheme primarily depends upon the application of that area and accordingly target values of lighting design parameters are to be decided from relevant BIS (Bureau of Indian Standard) lighting codes. There are several design variables, viz., light distribution, aiming of the luminaire, pole spacing, luminaire mounting height, grid dimension over the field, etc. The task of a lighting designer is to achieve the target design parameters through a suitable combination of set design variables and design …

Field Oriented Control (Foc) Of Permanent Magnet Synchronous Motor (Pmsm) Applied In Electric Vehicle (Ev), Unnikrishnan P C, Delgin Saji, Ashwini M, Georgee Cleetus, Joshua Andrews Chandy

Manipal Journal of Science and Technology

Synchronous motors are the most commonly used steady-state three-phase AC motors in electrical systems. The synchronous speed of these motors remains constant, being equal to the supply frequency and its rotational period corresponding to the integral number of AC cycles. So, these motors are mainly used to improve the power factor in power systems.

The paper focuses on field-oriented control of a permanent magnet synchronous motor to effectively control its speed and torque. AC motors only have stator currents, so separate control mechanisms such as vector controls are required to control the motor’s operation. Field-oriented control is the most commonly …

Contingency Analysis On Transmission Line Of Ieee 9 Bus System, Padmashree K S

Manipal Journal of Science and Technology

Ensuring power system security poses a significant challenge for engineers in the field. Conducting security assessments is crucial as it provides insight into the system's condition in the event of a contingency. The widely employed contingency analysis technique serves to anticipate the impact of outages, such as equipment failures or transmission line disruptions, enabling pre-emptive measures to maintain system reliability. However, analyzing each contingency offline is arduous due to the extensive number of system components, with only select contingencies posing severe threats to the system. Computing performance indices for every scenario is a step in the contingency selection process, which …

An Energy-Efficient Clustering Technique Of Heterogeneous And Homogeneous Wireless Sensor Networks, Ahmad Alkhayyat, Rohit Sharma

NJF Intelligent Engineering Journal

The technology of wireless sensor networks (WSN) has recently gained widespread recognition as an emerging one. A WSN consists of a set of sensors powered by batteries. Inaccessible locations usually make it difficult to replace or recharge sensors' batteries. These networks are plagued by energy consumption, which is the main problem. The clustering algorithms are remarkably effective in dealing with such problems in this regard. As a result, this technique appears to help reduce node energy consumption, which ultimately increases the network's lifespan. Heterogeneous and homogeneous clustering algorithms exist. In homogeneous clustering algorithms, all nodes have the same technical characteristics, …

Optimized Thyroid Disease Classification Using Nature-Inspired Algorithms: Gwo & Woa, Sayan Mondal

NJF Intelligent Engineering Journal

Recently, there has been an upsurge in the number of cases of thyroid disease. Thyroid function is essential for metabolism, making the early diagnosis of thyroid dysfunction an urgent matter. The issue of class imbalance has not been thoroughly examined, even though there are multiple publications on the topic of thyroid disease detection. Furthermore, the binary-class problem has been the primary emphasis of previous research. This study intends to address these concerns by using the suggested strategy, which takes into account ten distinct thyroid illnesses. In order to choose the best features from the Thyroid dataset, this research proposes two …

A Predictive Iot And Cloud Framework For Smart Healthcare Monitoring Using Integrated Deep Learning Model, Preeti Rani, Umesh Chandra Garjola, Haider Abbas

NJF Intelligent Engineering Journal

The researchers developed a deep learning-based smart healthcare monitoring system based on IoT and cloud technology. The proposed system integrates IoT sensors for real-time collection of physiological data, such as ECG, blood pressure, and heart rate, with cloud computing for secure storage and advanced analytics. Utilizing the Bi-LSTM model with fuzzy inference systems (FIS), the framework enhances the accuracy and efficiency of heart disease prediction. According to the evaluation, the model performs better in terms of accuracy, precision, recall, and F1 score than traditional LSTM and FLSTM models. By enabling early detection and personalized interventions, the system aims to reduce …

Real-Time Congestion Control And Load Optimization In Cloud-Manets Using Predictive Algorithms, Preeti Rani, Mohammed Hussien Falaah

NJF Intelligent Engineering Journal

Cloud-MANET environments require a system to balance load and control congestion. As a result of integrating real-time network metrics with predictive traffic algorithms, the proposed model optimizes the management of dynamic topologies, network bandwidth constraints, and fluctuating traffic loads. In addition to energy-aware multi-path routing, the framework incorporates adaptive congestion control mechanisms to ensure data transmission is efficient and stable. This algorithm provides higher packet delivery ratios, reduces end-to-end delays, and increases throughput over existing algorithms, according to the evaluation results. Hybrid Cloud-MANET systems can benefit from this approach by optimizing resource utilization and network performance.

New Forced Convection Flow Of Nanofluid Within A Partially Filled Porous~Straight Channel, Ammar I. Alsabery, Ali Sahib Abosinee

NJF Intelligent Engineering Journal

This study specifically examines how the movement and dispersion of nanoparticles affect heat transfer in a linear channel that contains a partially porous medium. The existing body of literature is lacking a comprehensive understanding of the convective heat transfer of nanofluids in porous channels. This presents an open research topic that demands further investigation. The porous channel is modelled using Finite Element Method (FEM) for steady flow. The assumption of thermal equilibrium model is made between the solid phases and nanofluid. The non-uniform distribution of nanoparticles within the channel is postulated. Consequently, the equation for the distribution of volume fraction …

A Predictive Framework Combining Iot And Machine Learning Regression Models For Smart Precision Farming, Kusum Yadav, Nesreen Abdou El-Hadiede

NJF Intelligent Engineering Journal

In this paper, the Internet of Things (IoT) and machine learning algorithms that incorporate regressor are integrated to improve precision agriculture. Data from Internet of Things sensors like temperature sensors, humidity sensors, and soil sensors can be collected and analysed using machine learning algorithms like Support Vector Machines (SVMs) and Multilayer Perceptrons (MLPs). By using the proposed system, crop yield will be optimised, resource usage will be minimised, and the environmental impact of agriculture will be reduced. A comparison of predictive accuracy and error metrics, such as RMSE, demonstrated the effectiveness of automated monitoring, predicting crop health issues, and implementing …

Comparative Analysis Of Nature-Inspired Optimization Algorithms: Applications, Challenges And Future Directions, Prerna Mann

NJF Intelligent Engineering Journal

With the proliferation of data generation, the process of achieving optimal solutions is getting more complex. It is becoming increasingly clear that intelligent metaheuristics algorithms are the way to go for solving these complicated optimisation problems, particularly when faced with several restrictions. The development of effective methods for dealing with these optimisation challenges has prompted the creation of numerous new algorithms. These algorithms are either improving their ability to handle problems in many domains or are investigating new contexts in which they could be useful. The field is advancing at a quick pace, leaving many in the dark about its …

Development Of A Spray Pipe Evaporator For Application On Unproductive Salt Farm Land In Indonesia, Srie Muljani, Ketut Sumada, Alfian Rizki Pradana, Caecilia Pujiastuti

ASEAN Journal of Community Engagement

This article discusses the development of a prototype spray pipe evaporator and its efficiency in producing salt in Indonesia. Due to the length of the salt harvesting season in Indonesia, many salt farmers have closed their business doors, leaving many salt ponds abandoned. The spray pipe evaporator prototype was designed to produce a brine solution with a salinity of 23–24 Be from seawater, which has a salinity of 2.5–3.5 Be, in less than 3 days. This is faster than the conventional process of a brine solution salinity of 24 Be. The prototype spray pipe evaporator was assessed in a 20 …

Road Safety Assessment For Accident And Non-Accident Cases Supporting Sustainable Development Goals 2030, Brian Nararya Nugraha, Sekar Warangi Nurcahyati, Hizkia Adhikaratma, Martha Leni Siregar

Journal of Environmental Science and Sustainable Development

Halving the number of global deaths and injuries from road traffic accidents is one target of the Sustainable Development Goals 2030, which is still a challenging problem in Indonesia. However, the majority of research on this topic uses accident-based methods, which limits the safety analysis. Two road safety assessment methods are used to close the gap, with the addition of non-accident-based methods such as Road Safety Audits (RSA). The Margonda Raya Road, which is categorised as a traffic accident blackspot, was selected for the case study. For this, a field survey of the road segment is carried out. To minimise …

Hybrid Solar-Rainwater Harvesting System With Mini Turbine Integration For Enhanced Energy Generation, Shamanth Showri N R, Sathvik V. Koushik Mr., Shreya C R, Samarth S

Manipal Journal of Science and Technology

Uniting the sun's rays with the fluidity of water, the combination of solar and waterpower creates a potent force for renewable energy, lighting the way to a greener tomorrow. The proposed hybrid system combines modified solar panels with integrated rainwater collection channels, a central collection point, and mini turbines for electricity generation. Through experimental testing and simulations, the feasibility and effectiveness of the hybrid system are evaluated, demonstrating its potential to maximize energy output in regions with abundant sunlight and rainfall. The results indicate that the hybrid solar rainwater harvesting system offers a promising solution for sustainable energy generation, with …

Portable Personal Air Monitor, Samreen Zabiulla, T Jaya Soumya, Vaishnavi N, Pronama Biswas, Belaguppa Manjunath Ashwin Desai

Manipal Journal of Science and Technology

Cities around the globe are facing ever-increasing challenges to air quality, posing serious risks to public health due to high levels of particulate matter and various pollutants. Addressing these challenges requires a multifaceted strategy, including strict regulations, sustainable urban planning, and adopting cleaner technologies. To tackle these issues, we developed a portable personal air monitor designed to effectively assess environmental conditions. This innovative device incorporates a range of sensors and a filtration system to measure concentrations of different pollutants. What sets it apart is its unique design inspired by the human lung, utilizing a diaphragm vacuum pump to mimic the …

High-Frequency Gold Price Forecasting: Optimizing Multi-Layer Perceptron With Genetic Algorithm, Vrtagic Sabahudin, Fatih Dogan

Materials Science and Engineering Faculty Research & Creative Works

Accurately forecasting gold price actions is critical in financial markets due to gold's role as a safe-haven asset. This paper addresses the challenge of forecasting gold prices by applying a Genetic Algorithm (GA) with a Multi-Layer Perceptron (MLP) model. The research exploits historical financial data from various instruments, including gold futures, Bitcoin, and key currency pairs, to improve prediction accuracy. By optimizing the MLP's hyper parameters through GA, the model efficiently captures complex relationships in high-frequency data, achieving a remarkable R² score of 0.9993. This level of precision demonstrates the model's potential for providing actionable insights for traders and investors. …

Forecasting Air Pollution Driven By Vehicle Growth, Public Transport, Industry, And Household Waste, Chandra Harjono, Ludy Gianto, Rachmattullah Sidik, Dyah Lestari Widaningrum

Journal of Environmental Science and Sustainable Development

Jakarta, Indonesia's bustling capital, is grappling with escalating air pollution levels attributed to a confluence of socio-economic and infrastructural factors. This study employs Vensim modelling to project PM2.5 pollution trends through 2040, analysing the dynamic interplay among major contributors: increased vehicular emissions, industrial activities, public transportation deficiencies, and waste management inefficiencies. Materials and Methods: The method that will be used in this air pollution analysis is to integrate empirical data spanning three years to construct a predictive model underpinned by a robust causal loop diagram that elucidates the relationships between system variables and air quality. The results of this paper …

Assessing The Environmental Impact Of Municipal Solid Waste, Ojo Oluwayinka Florence, Olaniyan Olatunji Sunday, Akolade Adebola Saheed, Alabi Oluwaseyi Omotayo, Olaomotito Precious Adesope, Adebayo Kehinde John

Journal of Environmental Science and Sustainable Development

Municipal solid waste (MSW) management presents significant environmental challenges, especially with increasing urbanization and population growth. This study assesses the environmental impacts of MSW, focusing on waste composition, management practices, and their effects on air, water, and soil quality. The primary objective is to evaluate how MSW contributes to environmental degradation and to explore strategies for mitigating these impacts. A hypothesis was developed that optimizing waste composition through proper sorting and treatment can reduce the environmental footprint of MSW management by at least 10% in terms of leachate production and greenhouse gas emissions. Samples were collected using a randomized sampling …