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SVM Based AutoEncoder for Detecting Dementia in Young Adults
Dementia's impact on cognitive function necessitates timely diagnosis for effective intervention. Understanding the need for timely detection, the proposed work integrates SVM's decision boundary determination and autoencoder's noise reduction capabilities. The proposed work advances in dementia detection in young adult. Results indicate promising performance, with the model achieving high accuracy around 85.33%. The ROC curve illustrates a balanced trade-off between sensitivity and specificity, while the precision-recall curve highlights effective classification. Importantly, the model surpasses existing literature, underscoring its practical utility. While acknowledging limitations, such as parameter fine-tuning, this study lays the groundwork for refining and expanding this innovative methodology. In summary, this research contributes to the urgent field of early dementia detection, potentially transforming patient care and intervention strategies. 2023 IEEE. -
Forecasting the Academic Horizon: Machine Learning Models Unraveling the Complex Web of Student Well-being Determinants
In the contemporary academic landscape, the well-being of students is pivotal not only for their individual success but also for the broader educational ecosystem. This study meticulously delves into a rich dataset encompassing diverse student attributes, academic performance metrics, and economic indicators to discern patterns and predictors affecting student well-being. Leveraging a multi-faceted research methodology, we employed various machine learning models, ranging from logistic regression to advanced ensemble methods, aiming to forecast and comprehend the intricate determinants of student outcomes. The research design, underpinned by rigorous exploratory data analysis, revealed intriguing correlations between economic conditions, academic achievements, and students' well-being. The Gradient Boosting model, in particular, showed a significant improvement post hyperparameter tuning, with an accuracy reaching up to 77.63%. On the other hand, models like the Random Forest achieved a base accuracy of 77.29%. These insights highlight the potential of data-driven methodologies in understanding and predicting student well-being. As we stride into an era where data-driven decisions in education are paramount, our findings offer a robust foundation for future endeavors in this realm. Future directions of this study encompass refining prediction models with more granular data, exploring the psychological facets of student well-being, and devising actionable interventions based on the identified predictors. 2023 IEEE. -
Design and analysis of single stage Step-up converter for Photovoltaic applications
Main novelty of the proposed work is dual leg single stage DC-AC converter for DC\AC grid and solar based applications. Operating principles, components design and modulation techniques are presented. Initially proposed concept is simulated in MATLAB Simulink platform and after validated in a real time prototype model is the future work. Proposed idea has some advantages like few passive components, less leakage current due to few switching frequency components, wide range voltage with absence of DC link capacitor. High efficiency due to single stage operation so this circuit is highly suitable for high\low voltage photo-voltaic energy conversion. Electromagnetic interference also less with continuous current. 2023 IEEE. -
Efficient Power Conversion in Single-Phase Grid-Connected PV Systems through a Nine-Level Inverter
In this paper, a novel nine-level inverter-based method for achieving efficient power conversion in single-phase grid-connected photovoltaic (PV) systems is proposed. The traditional two-level inverter has poor power quality and a high harmonic content. By using fewer power switches and adding more voltage levels, the proposed nine-level inverter gets around these restrictions, improving power conversion efficiency and lowering total harmonic distortion (THD). The effectiveness of the indicated technique for accomplishing better power quality and greater overall system efficiency is demonstrated by the simulation findings. A promising approach to improving the efficiency of single-phase grid-connected PV systems is the suggested nine-level inverter. 2023 IEEE. -
Data Ingestion - Cloud based Ingestion Analysis using NiFi
Data Ingestion has been an integral part of Data Analysis. Bringing the data from various heterogeneous sources to one common place and ensuring the data is captured in the appropriate format is the key for performing any Big data task. Data ingestion is performed using multiple frameworks across the industry and they all have their own set of benefits and drawbacks. Apache NiFi is one popular ingestion framework which is used widely and does Ingestion effectively. Ingestion is performed on various sources and the data is generally stored in clusters or cloud storage. In this paper, we have done the File Data Ingestion using the NiFi framework on a local machine and then on two cloud-based platforms, namely Google Cloud Platform (GCP) and Amazon Web Services (AWS). The objective is to understand the latency and performance of the NiFi tool on Cloud-based Ingestion and provide a comparative study against the typical Data Ingestion. The entire setup was done on a local machine and two corresponding cloud platforms namely GCP and AWS. The findings from the comparative analysis have been compiled in a tabular format and graphs are created for easy reference. The paper places emphasis on the significance of NiFi's data ingestion performance on Cloud Platform and attempts to present it as a major activity on the data ingestion platform for Cloud Ingestion Solution. 2023 IEEE. -
Improved tweets in English text classification by LSTM neural network
This paper analyzes the performance of an LSTM-type neural network in the sentiment analysis task in tweets in English about the COVID-19 pandemic. Primarily, the organization and cleaning a database of tweets about the COVID-19 pandemic is performed. From the original database, two other databases through different discretizations of the polarities of the tweets using Heaviside-type functions are created. Vectorization of tweets using the Word2Vec word embedding technique is carried out. Computational implementations of LSTM neural networks to the context of our research problem are adapted. Analyzes and discussions on the feasibility of the proposed solution taking into account different types of hyperparametric adjustments in the neural network models is carried out. Publicly available databases organized through the Mendeley Data public data repository are used. 2023 IEEE. -
An Innovative Method for Fuel Consumption and Maintenance Cost of Heavy-Duty Vehicles based on SR-GRU-CNN Algorithm
A heavy-duty vehicle's fuel usage, and thus its carbon dioxide emissions, are significantly impacted by the driver's behavior. The average fuel economy of a car varies by about 28% between drivers. Fuel efficiency can be improved by driver education, monitoring, and feedback. Fuel efficiency-based incentives are one form of feedback that can be provided. The largest challenge for transportation companies implementing such incentive programs is how to accurately evaluate drivers' fuel consumption. The processes of preprocessing, feature extraction, and model training are all utilized in the suggested method. Principal component analysis (PCA) is widely utilized in data science's preprocessing stage. GMM is used for feature extraction. Afterwards, SR-GRU-CNN is used to train the models based on the selected features. When compared to the two most popular alternatives, CNN and SR-GRU, the proposed methodexcels. 2023 IEEE. -
Enhancing Software Cost Estimation using COCOMO Cost Driver Features with Battle Royale Optimization and Quantum Ensemble Meta-Regression Technique
This research suggests a unique method for improving software cost estimates by combining Battle Royale Optimisation (BRO) and Quantum Ensemble Meta-Regression Technique (QEMRT) with COCOMO cost driver characteristics. The strengths of these three strategies are combined in the suggested strategy to increase the accuracy of software cost estimation. The COCOMO model is a popular software cost-estimating methodology that considers several cost factors. BRO is a metaheuristic algorithm that mimics the process of the fittest people being selected naturally and was inspired by the Battle Royale video game. The benefits of quantum computing and ensemble learning are combined in the machine learning approach known as QEMRT. Using a correlation-based feature selection technique, we first identified the most important COCOMO cost drivers in our study. To get the best-fit model, we then used BRO to optimize the weights of these cost drivers. To further increase the estimation's accuracy, QEMRT was utilized to meta-regress the optimized model. The suggested method was tested on two datasets for software cost estimating that are available to the public, and the outcomes were compared with other cutting-edge approaches. The experimental findings demonstrated that our suggested strategy beat the other approaches in terms of accuracy, robustness, and stability. In conclusion, the suggested method offers a viable strategy for improving the accuracy of software cost estimation, which might help software development organizations by improving project planning and resource allocation. 2023 IEEE. -
A Stacked BiLSTM based Approach for Bus Passenger Demand Forecasting using Smart Card Data
Demand forecasting is crucial in the business sector. Despite the inherent uncertainty of the future, it is essential for any firm to be able to accurately predict the market for both short- and long-term planning in order to place itself in a profitable position. The proposed approach focus on the passenger transport sector because it is particularly vulnerable to fluctuations in consumer demand for perishable commodities. At every stage of the planning process from initial network designs to final pricing of inventory for each vehicle in a route-an accurate prediction of demand is essential. Forecasting passenger demand is crucial since passenger transportation is responsible for a substantial chunk of global commerce. The suggested method relies on three distinct techniques: data preparation, feature selection, and model training. Data modification, cleansing, and reduction are the three sub-processes that make up preprocessing. When it comes to feature selection, partition-based clustering algorithms like k-means are the norm. Let's go on to training the models with stacked BiLSTM. The proposed method is demonstrably superior to both LSTM and BiLSTM, the two most common competing approaches. The proposed method had a success rate of 98.45 percent. 2023 IEEE. -
XGBoost Classification of XAI based LIME and SHAP for Detecting Dementia in Young Adults
As technology progresses on a fast pace, it is imperative that shall be used in the field of medicine for the early detection and diagnostics of dementia. Dementia affects humans by deteriorating the cognitive functions, and as such many algorithms have been used in the detection of the same but all these algorithms remain a black box to the medical fraternity which is still dubious about the nature and credibility of the prediction. To ease this issue, the use of explainable artificial intelligence has been proposed and implemented in this paper, which makes it easy to understand why and how the model is giving a particular output. In this paper the XGBoost classification algorithm has been used which give an accuracy of 93.33% and to understand these predictions, two separate algorithms namely Local Interpretable Model-agnostic Explanations (LIME) and Shapely Additive Explanations (SHAP) have been used. These algorithms are compared based on the type of explanation they provide for the same input and thus the weakness of LIME algorithm has been found out at certain intervals based on the clinically important features of the dataset. On the other hand, both the algorithms make it easy for medical practitioners to understand the dominating factors of a predicted output thereby helping to eliminate the black-box nature of dementia detection. 2023 IEEE. -
Smell Technology: Advancements and Prospects in Digital Scent Technology and Fragrance Algorithms
Smell technology, a rapidly expanding sector of the scent business, aims to digitally replicate and transmit aromas. Its applications include virtual reality, e-commerce, and healthcare. Recent advances in the field include the creation of smell algorithms and the use of artificial intelligence to create more realistic fragrances. Fragrance algorithms are mathematical models that predict the scent of a fragrance based on its chemical composition. They might be used to the perfume industry to streamline the production of perfumes and do away with the need for expensive trial-and-error methods. Artificial intelligence is also being used to create digital representations of fragrances that closely resemble the real thing by analysing the chemical composition of actual odours. A possible benefit of this technology in the healthcare sector is that synthetic odours may mimic the scents of diseases and aid physicians in making more precise diagnoses. Additionally, some companies are developing small devices that can be connected to computers or mobile devices to emit odours on demand, providing users of virtual reality, gaming, and online shopping with a more realistic experience. In spite of these advancements, it is still exceedingly challenging to recreate the complexity of natural scents, which can include hundreds of different components. With more research and development, there is still a tonne of promise for fragrance technology in the future. 2023 IEEE. -
A Predictive Modelling of Factors Influencing Job Satisfaction Through a CNN-BiGRU Algorithm
The fields of humanities, psychology, and sociology are where the word 'job satisfaction' originated. According to psychology, it is a condition in which a worker experiences his circumstances emotionally and responds by experiencing either pleasure or suffering. It is regarded as a variable in various sociological categories pertaining to how each employee assesses and thinks about his work. Because a satisfied employee contributes to and builds upon an organization's success, job satisfaction is intimately tied to an employee's performance and the quality of the work they do. As a result, job satisfaction directly correlates to an organization's success. The proposed strategy incorporates data preprocessing, feature selection, and model training. The missing value is a common feature of data preparation. Feature selection is chosen using the ANOVA F-Test Filter, the Chi-Square Filter, and the full Data Set Construction procedure. The model's efficacy can be evaluated with the help of CNN-BiGRU. The proposed technique is compared to two more models: BiGRU and CNN. It has been shown that our proposed technique outperforms two other models. 2023 IEEE. -
Predicting Work Environment and Job Environment Among Employees using Transfer Learning Approach
Today's enterprises face numerous challenges as a result of the world's rapid evolution. Maintaining a content workforce is crucial to a company's success and survival in today's fast-paced business environment. The efficacy, productivity, efficiency, and dedication of the company's staff are directly associated with the company's capacity to meet the needs of its employees in the workplace. The focus of this system is to identify the factors that contribute to a satisfying work environment for the participants. Preprocessing, feature selection, and model training are the first three steps in the suggested methodology. Data mining systems should get in the habit of normalizing data as a preliminary processing step. The multiple elements assessing company culture and worker satisfaction were consolidated using Principal Components Analysis (PCA) in the feature selection phase. Once features have been selected, KNN-SVM is utilized for model training. When compared to the two most popular alternatives, SVM and KNN, the proposed technique performs better. 2023 IEEE. -
A Intelligent Approach for Fault Detection in Solar Photovoltaic Systems based on BERT-BiGRU Network
Large-scale photovoltaic (PV) plant problem identification and diagnosis is expected to grow more difficult in the future as more and more plants of increasing capacity enter into existence. To keep large-scale PV installations safe, reliable, and productive, automatic identification and localization of any mal-operation among thousands of PV modules is necessary. In order to identify problems in PV plants, the suggested method compares the 'residuals' (fault indicator signals) generated by each string to a predetermined threshold. The suggested method relies on three distinct processes: data preparation, feature extraction, and model training. Preprocessing employs the method of Transform Invariant Low-rank Textures (TILT). The most useful and efficient measurements from feature extraction are kept while less important ones are discarded in favor of the Reduced Kernel PCA technique. Let's move on to model training with BERT-BiGRU. The proposed method is clearly superior compared to the two leading options, BERT and GRU. The proposed method had a 97.36% success rate. 2023 IEEE. -
Evaluating the Effectiveness of a Facial Recognition-Based Attendance Management System in a Real-World Setting
Face recognition technology has been extensively used in multiple verticals of security, surveillance, and human-computer interaction. Conventional techniques including manual sign-ins, identity cards, or biometric verification have been used by traditional attendance systems. Face recognition systems have, however, become a popular way to track attendance, thanks to developments in computer vision and machine learning. The construction of an attendance registration application is the main topic of this research study, which also offers a thorough overview of facial recognition attendance systems. This study seeks to provide light on the benefits, drawbacks, and potential applications of these fast-developing technologies. Face recognition technology may be integrated into attendance systems to increase productivity, accuracy, and user comfort. However, issues like privacy worries and technological constraints must be resolved. With predicted future improvements in machine learning algorithms and hardware capabilities, face recognition attendance systems look to have a bright future. This research article adds to a deeper understanding and successful application of facial recognition technology in attendance systems by examining these features. 2023 IEEE. -
Enhancement of Accuracy Level in Parking Space Identification by using Machine Learning Algorithms
Parking space identification is a crucial component in the development of intelligent transportation systems and smart cities. Accurate detection of parking spaces in urban areas can significantly improve traffic management, reduce congestion, and enhance overall parking efficiency. This proposed model is focuses on enhancing the accuracy of parking space identification through the utilization of Support Vector Machine (SVM) algorithms. The proposed methodology involves the following steps. First, a dataset comprising labelled parking space images is collected and pre-processed to ensure optimal quality and consistency. Next, feature extraction techniques are applied to capture certain relevant spatial and textural information from the images in the dataset, enabling the creation of informative feature vectors. These feature vectors are then utilized to train a SVM model, which is well-known for its capability to handle complex classification tasks. To measure the effectiveness of the SVM-based approach, a comprehensive set of experiments is carried out using real-world parking data. The performance metrics is to analysis accuracy level of the parking space identification. Comparative analysis has been done by comparing the proposed SVM approach with other popular machine learning algorithmsto demonstrate the superiority. The results indicate that the SVM-based model achieves a significantly higher accuracy level in parking space identification compared to other existing algorithms. 2023 IEEE. -
Area and Energy Efficient Method Using AI for Noise Cancellation in Ear Phones
Adaptive filters are suitable for most of the Digital Signal Processing (DSP) applications such as channel equalization, noise cancellation, echo cancellation, channel estimation and system identification. Nowadays due to the advancement in semiconductor technology, the need for Active Noise Cancellation (ANC) headphones in compact devices is increased. The major idea behind this proposed work is to design an area and energy efficient novel adaptive filter suitable for in-ear headphones by combining Normalized Least Mean Square (NLMS) and Block LMS (BLMS). The proposed filter is designed and simulated using Xilinx ISE 13.2. The simulation results shows that the proposed design mitigates the unwanted noises in various frequency bands. 2023 IEEE. -
An Efficient and Robust Explainable Artificial Intelligence for Securing Smart Healthcare System
The advent of IoT technologies has a tremendous impact on the healthcare sector enabling efficient monitoring of patients and utilizing the data for better analytics. Since every activity related to a patients health is monitored, the focus on smart healthcare applications has significantly transferred from service provision to a security perspective. As most healthcare applications are automated security plays a vital role. The technique of machine learning has been widely used in securing smart healthcare systems. The major challenge is that these applications require high-quality labeled images, which are difficult to acquire from real-time security applications. Further, it highly time-consuming and cost-expensive process. To address these constraints, in this paper, we define an efficient and robust explainable artificial intelligence technique that takes a small quantity of labeled data to train and de-ploy the security countermeasure for targeted healthcare applications. The proposed approach enhances the security measure through the detection of drifting samples with explainability. It is observed that the proposed approach improved accuracy, high fidelity, and explanation measures. Also, this approach is proven to be considerably resistant against numerous security threats. 2023 IEEE. -
Unraveling Campus Placement Success Integrating Exploratory Insights with Predictive Machine Learning Models
The dynamics of campus placements have garnered considerable attention in recent years, with educational institutions, students, and employers all keenly invested in understanding the factors that drive successful recruitment. This surge in interest stems from the potential implications for academic curricula, student preparation, and hiring strategies. In this study, we aimed to unravel the myriad factors that influence a student's placement success, drawing from a comprehensive dataset detailing a range of academic and demographic attributes. Our methodology combined thorough exploratory data analysis with advanced predictive modeling. The exploratory phase unveiled notable patterns, particularly highlighting the roles of gender, academic performance analysis, Degree and MBA specialization in placement outcomes. In the predictive modeling phase, the spotlight was on state-of-the-art machine learning models, with a particular emphasis on their capacity to forecast placement success. Notably, algorithms like Logistic Regression and Support Vector Machines not only confirmed the insights from our exploratory analysis but also showcased remarkable predictive prowess, with accuracy scores nearing perfection. These findings not only demonstrate the capabilities of machine learning in the academic and recruitment spheres but also emphasize the enduring importance of core academic achievements in influencing placement outcomes. As a prospective direction, future research might benefit from examining how placement trends evolve over time and integrating qualitative insights to provide a holistic view of the campus recruitment process. 2023 IEEE. -
A Comparative Analysis of LSB & DCT Based Steganographic Techniques: Confidentiality, Contemporary State, and Future Challenges
In order to maintain anonymity and security, the steganography is the technique of cloaking confidential data within what seems like harmless digital material. Several steganographic methods have been established devised over time, but those centered around the discrete cosine transformation (DCT) and the least significant bit (LSB) have drawn the most consideration. In this study, two common steganographic methods are compared and contrasted with an emphasis on the secrecy they can keep, the usage they are now receiving, and any potential difficulties in the future. As an alternative, the DCT-based method uses the frequency domain properties of cover media to obfuscate hidden information. Since it spreads the concealed information across several frequency coefficients, it provides greater security than LSB-based techniques. The resilience and imperceptibility of the concealed data are improved by a variety of DCT-based algorithms, such as the modified quantization and matrix encoding approaches, which we explore in detail. We also give a general summary of both approaches'current state in terms of their application, constraints, and areas in which they may be used. We evaluate the benefits and drawbacks of each approach, considering elements like payload size, computing difficulty, and detection resistance. 2023 IEEE.