International Journal of Innovations in Science & Technology
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A Modified K-Nearest Neighbors Algorithm for the Detection of Heart Disease
Full text linkThe leading cause of mortality worldwide is heart disease, sometimes referred to as cardiovascular disease. It is a dangerous illness that impacts the heart and blood arteries. A significant amount of research and analysis has been done recently with the goal of improving the accuracy and dependability of heart disease data. In this discipline, machine learning is crucial since it provides medical diagnostic tools that may be used to forecast illness and enhance healthcare. In this study, heart disease detection is proposed by combining KNN with Jaccard and Cosine similarities. Further, the results of Jaccard and cosine integrated KNN are compared with the results of state-of-the-art models like KNN and decision trees. Python and its libraries are used for simulation purposes. After the simulation, it was found that Jaccard-based KNN (JKNN) had the best accuracy (97%) according to the study\u27s analysis of the Cleveland heart disease dataset. With 91% accuracy, the Cosine-based KNN (CKNN) likewise demonstrated strong performance. In a similar vein, the decision tree is inadequate for classifying heart disease because of its poor accuracy rate as 85%. Likely, KNN shows average results in the form of accuracy, as 86%. According to the results, the JKNN technique is the best model for this task, closely followed by CKNN. The use of machine learning in the diagnosis and prognosis of heart disease is affected by these discoveries
Preparation of CuSe Thin Films by Chemical Vapor Deposition via Water Splitting for Hydrogen Generation
Full text linkIn recent years, significant research has been done on semiconductor heterostructures to produce hydrogen by water splitting. The absorption of visible light and photoelectrochemical properties of CuO thin film is enhanced by the selenization. The selenization of CuO thin film is done by chemical vapor deposition (CVD) at various temperatures. The structural properties of the prepared samples were carried by XRD and the morphological properties of the prepared film were done by scanning electron microscopy. Optical properties reveal that the bandgap was decreased by increasing the selenization temperature. The solar light to hydrogen conversion efficiency of the CuSe-500oC, CuSe-600oC, and CuSe-650oC films were estimated by using three-electrode cells. It was noticed that CuSe-650oC showed much better STH% compared to pristine CuO thin film
Modeling and Implementation of a Density-Based Traffic Management System via Programmable Logic Controller
Full text linkTraditional traffic signal systems face challenges in efficiently managing traffic when a large number of vehicles move to different lanes. To address this issue, a programmable logic controller (PLC) has been applied Density-Based Smart Traffic Control system using PLC (Programmable logic controller). This work develops a smart traffic control system to keep an eye on the vehicle density at a 4-way junction. Using specific functions, calculations, and logical operations, the system program calculates the traffic density in each lane and transmits data to make automatic decisions regarding traffic signal priorities. The proposed system ensures that the traffic control system adjusts to real-time traffic conditions on the road. By utilizing a PLC (programmable logic controller), all sensors continuously check the position of each lane and perform logical operations. These operations control lanes that require immediate attention and service. Next, the system program is executed to generate output signals to control the traffic lights on the poles, facilitating the switching of red, yellow, or green lights. The duration of the green light, which indicates the ON time for each lane of the intersection, is dynamically adjusted based on the priorities calculated by the system. In summary, the execution of the density-based smart traffic control system with a programmable logic controller enables a more responsive and adaptive approach to traffic management, proficiently allocating priority based on real-time traffic situations at the intersection. This study addresses the challenges of traffic flow with improved safety and reduced congestion at busy junctions
IoT-Based Non-Invasive Monitoring of Blood Sugar Levels with Early Warning Mechanism
Full text linkThis research presents the design and implementation of a non-invasive sugar level monitoring system with an early warning mechanism using embedded systems and Internet of Things (IoT) technologies. The system integrates an Arduino Uno microcontroller with sensors such as the MLX90614 infrared temperature sensor, MAX30102 blood oxygen and heart rate sensor to monitor vital health parameters. The system correlates temperature, blood oxygen levels, heart rate, and glucose levels to provide early warnings for high or low sugar conditions. Experimental results demonstrate the system\u27s accuracy, reliability, and effectiveness in providing real-time health data. Also, this research highlights the potential of non-invasive health monitoring systems in diabetes management and paves the way for future advancements in IoT-based healthcare solutions
DECS: A Deep Learning Approach for EEG Channel Selection in Emotion Classification
Full text linkThe non-stationary nature of Electroencephalogram (EEG) signals often leads to high computational complexity in emotion recognition systems. To address this, we propose a novel framework that integrates optimal channel selection with efficient feature extraction. Our method begins by converting preprocessed EEG signals into two-dimensional spectrograms using a Continuous Wavelet Transform (CWT). These spectrograms are then processed by a GoogLeNet model for deep feature extraction. A key contribution is the Differential Entropy-based Channel Selection (DECS) technique, which identifies and retains the most informative channels. To manage dimensionality, the extracted features are encoded using the Bag-of-Deep-Features (BoDF) method, which employs k-means clustering to create a visual vocabulary and represents features as histograms. Finally, these histogram features are classified using a Support Vector Machine (SVM). Evaluated on the SJTU SEED and DEAP datasets, the proposed model achieves state-of-the-art classification accuracies of 95.1% and 81.1%, respectively, demonstrating its effectiveness and efficiency
Requirements Prioritization- Modeling Through Dependency and Usability with Fusion of Artificial Intelligence Technique
Full text linkRequirements Prioritization is a crucial part of Requirements Engineering which helps to prioritize the customer’s requirements according to his needs and priorities. This prioritization describes which requirements should be addressed first and which can be addressed later in the software development process. Researchers have suggested many methods and techniques of requirements prioritization. However, there is no comprehensive technique that can be used for all sizes of software projects. This research paper includes an overview of the concept of requirements prioritization, the most common techniques used to prioritize the requirements, and their comparison. Based on based on this comparison, a new requirements prioritization technique is presented in this paper which can be used for every size of a software project. This technique aims to provide the solution to many issues of previous techniques especially dependencies of requirements, user involvement as well as designers involvement. The results demonstrated that the RP model outperforms traditional techniques, particularly in agile development environments, by providing a more efficient and flexible prioritization process. The involvement of designers in requirements prioritization and handling of requirements dependencies reduces the efforts required in the design process
A Smart Prediction Platform for Agricultural Crops Using Machine Learning
Full text linkIt is very critical to have the economic development of emerging countries, like Pakistan. Pakistan, while being one of the world’s main suppliers of a wide range of commodities, continues to employ traditional techniques. Pakistani farmers have challenges not just in coping with changing climatic circumstances, but also in meeting increased demands for higher food output of excellent quality. Farmers must be mindful of shifting meteorological circumstances to produce quality crops. Operations are greatly affected by a variety of factors, including the availability of water, the type of soil, the climate, and fertilizer. Farmers in conventional farming must decide on all of these aspects. What to grow, how to use the irrigation schedule, and the kinds of fertilizer are all covered in this event. Decisions made by farmers are primarily dependent on their experience, which can lead to the waste of expensive resources like water, fertilizers, time, effort, etc. Additionally, cultivating crops that are not the best fit for a given soil type and climate by using standard farming methods might arise problems, which can reduce production and profit. The application of machine learning in crop prediction is very widespread. The most popular method is irrigation. The major goal of this paper is to efficiently develop an E-business online platform to enhance farmer’s productivity and circulation cycle. In this paper, we develop a platform for smart crop predictions. The platform will help farmers by assisting them in obtaining suggestions based on several metrics like humidity, temperature, pH, moisture, and rainfall. Additionally, the user of our platform will be able to get precise advice about what crop to plant depending on variables like humidity, pH, and other characteristics. The user will also be able to get connected with the buyers of their crops and meet their requirements in an efficient manner
Securing Cloud Data: An Approach for Cloud Computing Data Categorization Based on Machine Learning
Full text linkIntroduction/Importance of Study: A novel innovative technique known methodical approach is referring as cloud computing (CC), which allows users to store data on remote servers that are accessible through the internet. This method makes it simple to move and retrieve vital and personal data storage. As a result, the demand for it is rising daily. This can be used to store a variety of data, including multimedia content, paperwork-based files, and financial transactions. Furthermore, by lowering operating and maintenance expenses, CC lessens the reliance of the services on local storage.
Novelty statement: Current systems apply only one key size with which all data is encrypted without concerning the level of privacy of the data. This results in higher processing costs and longer processing times. Furthermore, none of these methods improves secrecy and only achieves a low accuracy rate in data classification.
Material and Method: This study presents a cloud computing strategy for data sensitivity that is based on automated data classification. The model suggested in this study utilizes Random Forest (RF), Naïve Bayes (NB), k-nearest neighbor (KNN), and support vector machine (SVM) classifiers to achieve automated feature extraction. This methodology is designed to operate effectively across three sensitivity levels: basic, confidential, and highly confidential.
Results and Discussion: The experiments were performed on the Reuters-21578 dataset, which consists of 21,578 documents. The simulation results demonstrated that the three proposed models achieved accuracy rates of 97%, 96%, and 95%, respectively. These findings indicate that SVM, RF, and KNN outperform NB in classification performance.
Concluding Remarks: Additionally, the suggested study offers helpful recommendations for researchers and cloud service providers (like Dropbox and Google Drive)
A FEM Analysis of BLDC Ceiling Fan with Different Slot-Pole Combinations
Full text linkBLDC motors have recently made significant advancements in the automation industry. Due to their high efficiency and power density, they are widely used in everyday applications such as fans, electric bikes, rail transit, and automobiles. The slot-pole structure is a key factor influencing motor design. This research explores various slot-pole combinations to enhance performance.
For ceiling fan applications, a balanced and highly efficient stator with concentrated winding has been designed based on different slot-pole configurations. Two commonly used combinations—18-slot/16-pole and 12-slot/14-pole—were analyzed. However, these configurations result in high cogging torque and a low winding factor, reducing the efficiency of BLDC ceiling fans.
To overcome these issues, a 24-slot/22-pole combination is proposed. This design improves torque production, power efficiency, and magnetic flux density while reducing cogging torque and increasing cogging frequency. The effectiveness of this structure is evaluated using the finite element method (FEM) in Ansys Electronics Desktop softwar
Steering Control of Ackermann Architecture Weed Managing Mobile Robot
Full text linkA robot that finds and eliminates weeds from crops is called a weed control robot. Weeds deplete primary crops moisture supplies and hinder their development. They may be harmful to both human and animal health and result in losses in crop yield. Herbicides and other chemicals have been used for many years to eradicate weeds from crops; nevertheless, these chemicals harm plants and contaminate the environment. In this work, a novel semantic weeds detection approach based on PC/BC-DIM network has developed which shows outbreaking performance and classification results compared to the state-of-art approaches. We developed an autonomous weed control robot which consists of Ackermann Architecture and delta robot. Delta robot have a camera on its base that is used to detect the real time weeds in the environment. First of all, image is acquired by camera and with the help of image processing techniques we are able of detecting the weed from other crops and eliminate them by the electrical discharging method in which electrodes are connected at its end effector that will burn the weed detected. We also developed a system for path planning and obstacle avoidance for navigation of mobile robot in which we used the technique of stereo vision that will capture the stereo images of environment and find their disparity. With the help of depth information, robot will be able to detect the object in its way and avoids the obstacle and find the shortest path to navigate in field using A* algorithm. The results obtained from this work are simulation based which are detection of weed in field images using image processing and path planning of robot using stereo images of field. The system has a fairly good overall accuracy of 81.25%. The efficiency of the system is moderate, but the relatively high False Positive Rate and RMS Error suggest that the system need improvement to reduce significant errors and false positives. Our future work involves the removal of weed and implementation of simulated results to hardware