Smart Techniques Research Articles

Utilizing Data Mining Techniques to Predict Student Academic Performance

During this epidemic, a problem in fundamental education affecting all globe is occurring, and we note that education and learning were online and conducted in students. Academic performance of students must be forecast, so that the instructor may better identify the missing pupils and offer teachers a proactive opportunity to develop additional resources for the student to maximize their chances of graduation. Students' academic achievement in higher learning (EH) has been extensively studied in addressing academic inadequacies, rising drop-out rates, graduation delays, and other difficult questions. Simply said, the performance of students refers to the amount to which short and long-term educational objectives are met. Academics nonetheless judge student achievement from different viewpoints, from grades, average grade points (GPAs) to prospective jobs. The literature encompasses numerous computing attempts to improve student performance in schools and colleges, primarily through data mining and analysis learning. However, the efficiency of current smart techniques and models is still unanimous.

Efficacy of Capacitive Deionization Flow Cell Fabricated with Carbon Nanomaterial Modified Electrodes

The supply of freshwater is becoming a serious global challenge. Capacitive deionization (CDI) has been recognized as apromising and smart desalination technique to solve it. Carbonaceous materials, due to their non-toxic characteristics, areused for filtration, adsorption, and water deionization. In this study, a newly designed, homemade low CDI system wasfabricated, and carbon as electrode material was prepared from waste leather through a one-step activated pyrolysis process. Carbonaceous materials prepared at different temperatures were characterized by Fourier-transform infrared (FTIR), scanning electron microscopy (SEM), and X-ray diffraction (XRD) techniques. The charge storage capacity of the carbon materials casted on a glassy carbon electrode was evaluated using cyclic voltammetric and galvanostatic chargingdischarging techniques. The fabricated CDI system could be successfully used for the study of the removal capacity of NaCl from an aqueous solution. The removal capacity of carbon prepared at 600 °C from leather is 11.58 mgg1, which is about three times higher than commercial activated carbon at flow rate and applied potential of 23 mL min–1 and 1.0 V, respectively. Dhaka Univ. J. Sci. 72(2): 52-60, 2024 (July)

Automatic Aircraft Landing System: A Review

The aircraft landing phase, marking the finishing of a flight plane, is a critical yet dangerous aerial maneuver. Even though it might seem simple, predicting the complexities of performance during landing presents a big challenge due to the dynamic characteristics of the phase, interaction with piloting methods, as well as inherent uncertainties in aerodynamics. Landing is executed in close proximity to the ground at reduced airspeed, the landing phase entails an escalated safety risk. Notably, incidents and accidents, particularly overruns where aircraft fail to slow sufficiently on the runway before landing, underscore the imperative for advanced landing technologies. This paper presents a comprehensive review of research spanning from 2000 to the present exploring smart techniques like fuzzy logic and machine learning, an array of control strategies ranging from classic PID control to sophisticated hybrid control approaches, and optimization procedures utilizing diverse optimization algorithms. The primary objective is to provide a comprehensive evaluation of the existing research landscape, offering insights that can propel the evolution of strategies for safer and more efficient landings and making sure the plane touches down safely.

Faculty perspectives on accreditation, academic freedom and quality learning in private higher education institutes: empirical evidence

Purpose This study aims to investigate the effects of standardization, accreditation process on academic freedom and quality learning in higher education institutes (HEIs). In addition, this study explores the mediating effects of academic freedom between standardization, accreditation and learning. Design/methodology/approach To attain the objective, the study uses the theory of self-determination as its theoretical underpinning. The smart PLS-SEM technique is applied for analyzing data. Findings The results indicate that the accreditation process has a significant negative influence on faculty academic freedom and quality of learning in the sampled HEIs. There is also a significant mediating effect of academic freedom. Research limitations/implications There are a few limitations in this study. First, the study considers the faculty members only as respondents. Second, this study only considers the faculty members of private universities as respondents. In the future, public HEIs could also be included in similar studies. Finally, this research has been done in the context of a developing country. Practical implications The findings of the study have pervasive implications for the authorities in HEIs. The authorities of HEIs might capitalize on this evidence in formulating the appropriate policy for their HEIs. Social implications As the accreditation process weakens academic freedom and quality learning, accreditation should not be viewed as an institutional development and quality assurance tool. Rather, accreditation ought to allow for amplifying faculty voices, empowering faculty and protecting their rights. Originality/value Quantitative analysis on the subject addressed in the current study is scarce. Therefore, this research can be considered valuable for stakeholders of HEIs.

Application to novel smart techniques for decarbonization of commercial building heating and cooling through optimal energy management

The present article proposes a novel smart building energy system utilizing deep geothermal resources through naturally-driven borehole thermal energy storage interacting with the district heating network. It includes an intelligent control strategy for lowering operational costs, making better use of renewables, and avoiding CO2 emissions by eliminating heat pumps and cooling machines to address the heating and cooling demands of a commercial building in Uppsala, a city near Stockholm, Sweden. After comprehensively conducting techno-environmental and economic assessments, the system is fine-tuned using artificial neural networks (ANN) for optimization. The study aims to determine which ANN design and training procedure is the most efficient in terms of accuracy and computing speed. It also assesses well-known optimization algorithms using the TOPSIS decision-making technique to find the best trade-off among various indicators. According to the parametric results, deeper boreholes can collect more geothermal energy and reduce CO2 emissions. However, deep drilling becomes more expensive overall, suggesting the need for multi-objective optimization to balance costs and techno-environmental benefits. The results indicate that Levenberg-Marquardt algorithms offer the optimum trade-off between computation time and error minimization. From a TOPSIS perspective, while the dragonfly algorithm is not ideal for optimizing the suggested system, the non-dominated sorting genetic algorithm is the most efficient since it yields more ideal points rated below 100. The optimization yields a higher energy production of 120 kWh/m2, as well as a decreased levelized cost of energy of 57 $/MWh, a shorter payback period of two years, and a reduced CO2 index of 1.90 kg/MWh. The analysis reveals that despite the high investment costs of 382.50 USD/m2, the system is financially beneficial in the long run due to a short payback period of around eight years, which aligns with the goals of future smart energy systems: reduce pollution and increase cost-effectiveness.

Endogenously Activated and Self‐Reinforced DNA Lipid Nanodevice for Spatial‐Specific and High‐Contrast Imaging of MicroRNA in Cells and Animals

AbstractTumor‐specific microRNA (miRNA) imaging strategies are critical for investigating mechanisms associated with cancer progression; however, nonspecific signal leakage and false‐positive signaling limit their selectivity and efficiency. In this paper, an endogenously activated and self‐reinforced DNA lipid nanodevice (LND) for spatial‐specific and high‐contrast imaging of miRNAs in living cells and animals is presented. The LND is functionalized on cholesterol‐based lipid micelles (CLMs) containing smart response and self‐fueling DNA components that display redox‐activatable and autocatalytic miRNA probing activities, respectively. The LNDs are initially silenced and selectively activated using glutathione, an endogenous microenvironmental factor overexpressed in tumor cells, to prevent nonspecific signal leakage. Subsequently, an autocatalytic DNA circuit is used for tumor‐specific and high‐contrast miRNA imaging. It is demonstrated that robust CLM nanoparticles can be easily assembled from a cholesterol‐conjugated G‐rich DNA sequence by stirring in a buffered solution, enabling the LND to enter cells naturally. In addition, in vitro and in vivo data illustrate that the enhanced biostability of DNA components on the surface of LNDs can be realized for minimizing false‐positive signals, making the system ideal for miRNA imaging in living mice. This smart LND technique has great potential for precise biomedical imaging and clinical diagnosis.

Leveraging smart image processing techniques for early detection of foot ulcers using a deep learning network.

To detect foot ulcers in diabetic patients by analysing thermal images of the foot using a deep learning model and estimate the effectiveness of the proposed model by comparing it with some existing studies. Open-source thermal images were used for the study. The dataset consists of two types of images of the feet of diabetic patients: normal and abnormal foot images. The dataset contains 1055 total images; among these, 543 are normal foot images, and the others are images of abnormal feet of the patient. The study's dataset was converted into a new and pre-processed dataset by applying canny edge detection and watershed segmentation. This pre-processed dataset was then balanced and enlarged using data augmentation, and after that, for prediction, a deep learning model was applied for the diagnosis of an ulcer in the foot. After applying canny edge detection and segmentation, the pre-processed dataset can enhance the model's performance for correct predictions and reduce the computational cost. Our proposed model, utilizing ResNet50 and EfficientNetB0, was tested on both the original dataset and the pre-processed dataset after applying edge detection and segmentation. The results were highly promising, with ResNet50 achieving 89% and 89.1% accuracy for the two datasets, respectively, and EfficientNetB0 surpassing this with 96.1% and 99.4% accuracy for the two datasets, respectively. Our study offers a practical solution for foot ulcer detection, particularly in situations where expert analysis is not readily available. The efficacy of our models was tested using real images, and they outperformed other available models, demonstrating their potential for real-world application.

A Self-Healing Based Smart Technique for Electric-supply Restoration Following a Permanent Fault in Smart Distribution Networks

A Self-Healing Based Smart Technique for Electric-supply Restoration Following a Permanent Fault in Smart Distribution Networks

Transforming E-Commerce Logistics: Sustainable Practices through Autonomous Maritime and Last-Mile Transportation Solutions

The logistics landscape in e-commerce is undergoing a profound transformation toward sustainability and autonomy. This paper explores the implementation of autonomous maritime and last-mile transportation solutions to optimize the entire logistics chain from factory to customer. Building on the lessons learned from the maritime industry’s digital transformation, the study identifies key features and proposes a forward-looking autonomous maritime and last-mile transportation system. Emphasizing the role of geospatial technologies, the proposed system employs GIS-based electronic route optimization for efficient goods delivery, integrating onboard and ashore GIS-based sensors for enhanced location precision. A case study was built to analyze the implementation of autonomous means of transport along the route of a product from factory to customer. The integration of autonomous systems shows substantial improvements in logistics performance. Synchromodal logistics and smart steaming techniques can be utilized to optimize transportation routes, resulting in reduced fuel consumption and emissions. The findings reveal that autonomous maritime and last-mile transport systems can significantly enhance the efficiency, flexibility and sustainability of e-commerce logistics. The study emphasizes the need for advanced technological integration and provides a comprehensive framework for future research and practical applications in the logistics industry.

Design Optimization of Flexural and Eigenvalue Characteristics of Perforated and Skewed Bio-Composite Functionally Graded Panels

The development of novel materials is of prime importance in the biomedical areas, especially for bone replacements and dental implants. This study uses finite element modeling of skewed functionally graded bio-composite structures with perforations to examine flexural and eigenfrequency responses. Here, the bio-composite structure comprises Titanium (Ti) and Hydroxyapatite (HAp). Voigt’s micromechanical material model via power-law distribution and Representative volume element (RVE) method is utilized to compute the in-homogenous material properties in the thickness direction. The strain field is based on the equivalent single-layer first-order shear deformation theory with five degrees of freedom, and the governing equations are obtained using principle of minimum potential energy. A computer algorithm is developed in a 2D finite element platform using eight-noded quadrilateral elements. The mesh stability of the skewed and perforated structure is confirmed through the smart mesh technique, and the present model’s accuracy is demonstrated by comparing it with the available reported results. Numerous examples reveal the significance of material gradation, skewness, aspect ratios, perforations, and support conditions on bio-composite functionally graded structures’ flexural and eigenfrequency responses. Furthermore, parametric optimization is conducted to minimize the flexural response and maximize the fundamental frequency by employing the response surface methodology (RSM), followed by determining optimal process parameters and valuable findings.

Exploring the nexus of smart technologies and sustainable ecotourism: A systematic review

Sustainable smart ecotourism, utilizing smart technologies like smartphones, artificial intelligence (AI), and the Internet of Things (IoT), aims to minimize harm to natural and cultural ecosystems, promoting education and environmental conservation. This review aims to examine the concept of sustainable smart ecotourism, analyzing existing literature to gain insights into the significance, components, challenges, and contributions to sustainable development on a global scale. A systematic review was conducted to evaluate sustainable smart ecotourism using PRISMA guidelines. The review focused on scholarly, peer-reviewed studies from developing countries, using databases like ScienceDirect, Jstor, Taylor & Francis, and IEEE. The study used Joanna Briggs Institute and Cochrane Risk of Bias tools to assess study quality. Thematic analysis techniques were used to extract and synthesize data, identifying patterns and trends relevant to smart ecotourism sustainability. Dual analyst verification ensured data integrity and reliability. After conducting a thorough quality evaluation using the Joanna Briggs Institute Checklist and Cochrane Risk of Bias Tool, we identified 29 studies of exceptional quality from an original pool of 9583 records. The use of thematic analysis sheds light on the diverse and important role of the IoT in promoting sustainable ecotourism. This study uncovered both the obstacles and possibilities associated with this technology. The findings provide important insights into the worldwide implementation of smart ecotourism techniques and highlight the significant impact of technology in promoting sustainable tourism models. Smart ecotourism involves multiple stakeholders to enhance environmental impact. Key characteristics include dynamic interactions, co-creation of value, sustainable development, resource sharing, and innovation services. Technology like IoT is crucial for sustainable tourism management. Collaboration with governments, local stakeholders, and organizations is recommended for sustainable policies. As a result of this study, sustainable ecotourism policies result from a collaborative effort between local communities, government agencies, and practitioners in the industry. Smart technologies, including AR/VR and AI, have the potential to enhance operational efficiency while reducing environmental concerns. Ecotourism, partnerships, and education are key to successful implementation and capacity building.

Tele-Trafficking of Virtual Data Storage Obtained from Smart Grid by Replicated Gluster in Syntose Environment

One of the most important developments in the energy industry is the evolution of smart grids, which record minute details of voltage levels, energy usage, and other critical electrical variables through General Packet Radio Service (GPRS)-enabled meters. This phenomenon creates an extensive dataset for the optimization of the grid system. However, the minute-by-minute energy details recorded by GPRS meters are challenging to store and manage in physical storage resources (old techniques lead to a memory shortage). This study investigates using the distributed file system, replicated Gluster, as a reliable storage option for handling and protecting the enormous volumes of data produced by smart grid components. This study performs two essential tasks. (1) The storage of virtual data received from GPRS meters and load flow analysis of SynerGee Electric 4.0 software from the smart grid (we have extracted electrical data from 16 outgoing feeders, distributed lines, in this manuscript). (2) Tele-trafficking is performed to check the performance of replicated Gluster (RG) for virtual data (electrical data received from the smart grid) storage in terms of User Datagram Protocol (UDP), Transmission Control Protocol (TCP), data flow, and jitter delays. This storage technique provides more opportuni11ty to analyze and perform smart techniques efficiently for future requirement, analysis, and load estimation in smart grids compared to traditional storage methods.

Impact of smart sustainable value stream mapping – Fuzzy PSI decision-making framework

Value Stream Mapping (VSM) is a Lean method for waste reduction, crucial across industries. In the era of the fourth industrial revolution, Small and Medium-sized Enterprises (SMEs) are adopting smart, sustainable practices. The focus has shifted from traditional VSM to Smart Sustainable VSM (SS-VSM). This study introduces an SS-VSM framework, integrating lean, sustainable, and smart techniques. A fuzzy-based Preference Selection Index (FPSI) method selects the optimal SS-VSM strategy, considering environmental, economic, and social factors. Sensitivity analysis tests the approach's robustness. The results offer a conceptual framework for implementing and managing VSM, aiding SMEs in enhancing performance through SS-VSM practices.

Development and Validation of UV Spectrophotometric Method for Simultaneous Estimation of some SGLT-2 and DPP-4 inhibitor in Bulk and Pharmaceutical Dosage Form

The result of pharmaceutical industry research for the new class and the new combination of drugs for the treatments of diabetes is the newly approved combination of Remogliflozin etabonate (RGE) and Teneligliptin Hydrobromide hydrate (TG). For the quality control of this formulation, the smart, reproducible and non-sophisticated spectroscopic techniques were developed by modification of UV spectra. The proposed methods exhibited Beer’s law in the range of 10 to 60µg/ml and 5 to 30µg/ml for RGE and TG correspondingly. The mean percentage recovery was found to be in the range of 98.60 to 101.55 for RGE and 99.87 to 100.21 for TG. Further, both analytes were quantified from the formulation using proposed spectroscopic methods with high accuracy. The suggested technique is simple, accurate and reproducible, hence could be used for regular quality control of formulation consisting of RGE and TG. The developed method can be used for analysis of stability samples and routing quality control evaluation in tablet formulation.

Devising of Lazūq (A Conventional Unani Dosage Form) with reference to Transdermal Patch; Then and Now: A Critical Review

Objective: The endeavor is made to criticize the classical indications, preparations and application of Lazūq even after passing many centuries since its inception. Background: According to classical Unani literature, Lazūq is primarily formulated for headache, migraine, toothache, cervical pain, dropping of uvula and different types of pain like sciatica, backache, myalgia and some orthopedic conditions. Its approach is still conventional that encompasses thick sticky stuff of drug/s which is pasted over a cloth or thick paper, consequently, maximum ratio of medicinal properties remains insignificant owed to stratum corneum (layer of skin that functions as a barrier). Devising and method of drug delivery in classical form of Lazūq makes it inconvenient to the patient, thus, it drops patient’s compliance and anticipated efficacy. In this day and age, Unani practitioners do not prescribe it very often because of its devising and limited indications documented in classical literature. Hence, it’s now becoming out of practice. Whereas, modern medicine is practicing its novel approach epitomized as Transdermal patch for several bodily conditions like Acute post-operative pain, Alzheimer disease, Angina pectoris, Anti-emetic patch, Atrial fibrillation, Attention deficit hyperactivity disorder, Dementia, Depression, Dysmenorrhea, Hypertension, Local dermal analgesia, Menopausal symptoms, Motion sickness, Osteoporosis, Overactive bladder, Parkinson’s disease, Post herpetic neuralgia pain, Smoking cessation, Tic disorder, Tourette syndrome and to prevent pregnancy. In transdermal patch, drug is used in micro/nano form with chemically produced permeation enhancers (CPEs) and other smart physio-chemical techniques that makes it very easy to apply, imparts maximum active pharmaceutical ingredients with controlled release of drug. Methodology: Ancient classical Unani formulations of Lazūq was appraised and then its course with respect to today’s newer formulations as transdermal patch was reviewed to look for its progress and application in novel dosage form with the advantage of innovations in pharmaceutical and allied sciences. Conclusion: Classical Unani formulations of Lazūq must be reviewed, transformed, evaluated, coped and applied as per the standard operating procedures (SOPs) of transdermal drug delivery system. Future Prospects: New formulations of Lazūq should be designed and appraised from the medicinal treasure of Unani system of medicine for different therapeutic conditions.

Two green UV-spectrophotometric techniques applying successive and progressive resolution with new concepts for simultaneous determination of completely overlapping ternary mixture

A spectrophotometric platform based on two smart techniques namely Double Divisor ratio difference spectra spectrophotometry (DDRD) and Derivative ratio amplitude difference method (DR-AD) was applied and validated. These techniques were used for determining and resolving the overlapped spectra of the 3 drugs paracetamol (PAR), pseudoephedrine (PSE) and pholcodine (PHO) in their ternary mixture. DDRD is a successive technique while DR-AD is a progressive one. In DDRD, the mixtures’ spectrum underwent division by a double divisor, and then the ratio difference method was applied. For DR-AD, consequent steps were performed to resolve each drug stepwise. DR-AD technique included division using a single divisor. The linearity ranges encompassed 5-25, 100-800 and 5-50 µg/mL for PAR, PSE and PHO, respectively. These methods were conducted according to ICH guidelines to ascertain accuracy, precision, and specificity. The analysis involved varied concentrations of drug mixtures and a pharmaceutical product. Statistical evaluation revealed no notable distinction between the suggested methodologies and those previously documented. Additionally, adherence to green analytical chemistry principles was demonstrated, establishing them as environmentally friendly analytical approaches.

Smart binary phase‐coding for pulse‐compression radar: An electronic protection technique against repeater jamming

AbstractNowadays, modern radar systems increase their target detection capabilities by processing pulses coherently. On the other hand, digital radio frequency memory‐based modern jammers have the ability to work coherently and can deceive radars even with a very low effective radiated power. These jammers, which have the capability of storing the radar's pulse, can use the previous pulses that they have stored in their memory during the electronic deception, without waiting for the last pulse of the radar, in other words, before the new pulse is received. If the radar does not change its parameters from pulse to pulse, such smart jamming techniques applied in this way can be very effective. In this article, the authors propose to use a smart binary phase‐coding method for pulse compression radar as an electronic protection technique against repeater jamming. This approach further improves the target detection capability of modern radar systems, which use coherent integration in the receiver. The proposed method can provide high protection against digital radio frequency memory‐based repetitive range deception techniques without compromising the radar's target detection capability. In the simulations, the traditional approach in which the same code is used without changing from pulse‐to‐pulse, and the approach using code sets obtained by the smart binary phase‐coding method in intra‐pulse modulation are compared. The results show that the proposed method can significantly improve the isolation against deception jamming and the target detection capability simultaneously.

Handrithum Gaming Hub

The "Handrithum Gaming Hub" Project Is an Exciting Blend of Computer Vision and Gaming Tech. Our Main Goal Is to Make Gaming More Fun by Creating a Pong Game That You Control Using Your Hands, Thanks To OpenCV-Based Hand Tracking. Imagine playing Pong without the need for a keyboard or joystick simply move your hands in the air to control the game. Through the use of smart image processing techniques, we capture and interpret hand movements in real-time, allowing for effortless control of the Pong paddles. This seamless interaction between gestures and game commands transforms the gaming experience, making it as natural as possible. Delving into the realm of computer vision, we harness the power of OpenCV to meticulously track every movement of the player's hands. The result is a swift and fluid translation of hand actions into precise game commands, elevating the gaming experience to a new level. Players will feel a physical connection to the game as they effortlessly guide the paddles on the screen with their hands.

Smart River Information Services in managing Nile River navigation system

Abstract In terms of the significance of incorporating smart techniques into large river navigation systems to increase inland navigation competitiveness, this article introduces the development of smart River Information Services (RIS) in Egypt based on smart information and communication technologies as real-time tracking systems, Open Geographic Information Systems, and Nile River Information Systems, as well as proposes measures to tackle the challenges that may be faced, compared with experiences and knowledge of RIS in large river smart navigation systems. This comparison creates new frameworks for the future development of using smart technology in inland waterways in Egypt. It could be concluded that advanced navigation technologies and infrastructures are still being developed in Egypt and are not yet available. Almost all of Egypt's Vessel Traffic Services station equipment and applications are imported from Europe. Undoubtedly, there will be at least a 5- to 10-year time lag for Egypt to develop independent RIS to its full extent. It is necessary to keep good cooperation with the European and Chinese inland sector and refer to their experience and knowledge.

Artificial Intelligence for the Control of Speed of the Bearing Motor with Winding Split Using DSP

This article describes the study and digital implementation of a system onboard a TMS 3208F28335 ® DSP for vector control of the bearing motor speed with four poles split winding with 250 W of power. Smart techniques: ANFIS and Neural Networks were investigated and computationally implemented to evaluate the bearing motor performance under the following conditions: operating as an estimator of uncertain parameters and as a speed controller. Therefore, the MATLAB program and its toolbox were used for the simulations and the parameter adjustments involving the structure ANFIS (Adaptive-Network-Based Fuzzy Inference System) and simulations with the Neural Network. The simulated results showed a good performance for the two techniques applied differently: the estimator and a speed controller using both a model of the induction motor operating as a bearing motor. The experimental part for velocity vector control uses three control loops: current, radial position, and speed, where the configurations of the peripherals, that is, the interfaces or drivers for driving the bearing motor.