2692-4048ISSN (Online)
2770-0070ISSN (Print)
The ability to retrieve a word from the cusp of memory often encounters the well-documented Tip-of-the-Tongue (TOT) barrier. This cognitive phenomenon can impede communication and learning. Addressing this, our study introduces a novel reverse dictionary framework empowered by cutting-edge neural network architectures to facilitate the retrieval of words from definitions or descriptions. This research draws the path of the development and the efficiency of various natural language deep learning models formulated to grasp the semantics inside the text. This work started with gripping a new dataset with rich content from a linguistic perspective. An accurate pre-processing step, including text normalizations and contextual features extraction, was conducted to transform the unstructured text into structured features fitting the model training. Dense vectors representative of text have been extracted using the BERT embedding model. Three models (LSTM, FNN, and GRU) were tested and compared using scrapped and benchmarked data. The proposed model that was consisted from Bert embedding and LSTM learner was evaluated and showed notable performance under cosine similarity and mean square error metrics. The LSTM model proved useful in real-world applications by exhibiting excellent semantic coherence in its embedding and accuracy in its predictions. This research evolved a discussion about the efficient behavior of the pre-trained BERT model in enhancing vocabulary. In addition, this work sheds light on the crucial role of reverse dictionaries in many NLP applications in the future. Subsequent research endeavors will focus on augmenting the multilingual functionalities of our methodology and investigating its suitability for other cognitive linguistic phenomena.
Read MoreDoi: https://doi.org/10.54216/FPA.170101
Vol. 17 Issue. 1 PP. 01-14, (2025)
The integration of Artificial Intelligence (AI) within the Medical Internet of Things (MIoT) is advancing swiftly, leading to significant developments in the detection of illnesses like epilepsy by analyzing Interictal Epileptiform Discharges (IED) in electroencephalograms (EEG).The availability of EEG data has facilitated the creation of innovative applications, including seizure detection. While neurologists have traditionally relied on EEG data analysis to identify epileptic seizures, the manual evaluation of EEG brain waves is a laborious and complex process that places significant stress on specialists. This paper presents a simple Convolutional Neural Network (CNN) method for the automated detection of IEDs based on EEG waveforms. This approach helps reduce the burden on epilepsy patients by forecasting seizures and enabling timely interventions. It also eases the workload for neurologists and less experienced specialists, thereby accelerating the diagnosis process. The proposed method was implemented by utilizing a series of images that depicted the magnitude of the EEG signal across each sensor. The study divided participants into two groups: (A) healthy individuals and (B) individuals with epilepsy. The results demonstrated an accuracy of up to 96.4% compared to human expert diagnoses, displaying the method's effectiveness and practicality in detecting seizure occurrences in EEG data.
Read MoreDoi: https://doi.org/10.54216/FPA.170102
Vol. 17 Issue. 1 PP. 15-25, (2025)
This research investigates how Egyptian investor sentiment affects cryptocurrency returns, focusing specifically on Bitcoin. We utilized an enhanced investor sentiment index in Egypt, constructed through factor analysis of various literature-based variables. Our study's findings revealed a notable positive correlation between the investor sentiment index, lagged by one order, and Bitcoin returns, as per the estimation and analysis using VAR models. Analysis indicates that a one standard deviation change in the investor sentiment index leads to an alteration in the influence of each standard deviation of the original positive variable, resulting in a switch from positive to negative and vice versa in the medium and long term. Regarding variance decomposition, the short-term variance error of 100% is primarily explained by Bitcoin returns themselves. However, in the medium to long term, besides Bitcoin returns, the investor sentiment index emerges as the most influential variable affecting Bitcoin returns. Causality tests reveal a unidirectional short-term impact from the investor sentiment index to Bitcoin returns via Granger causality tests. Additionally, using the Toda-Yamamoto causality test, long-term bidirectional effects between Bitcoin returns and the investor sentiment index were observed.
Read MoreDoi: https://doi.org/10.54216/FPA.170103
Vol. 17 Issue. 1 PP. 26-52, (2025)
This research delves into the developments, in cloud computing and their significance for e government. It introduces an approach to e government advancement known as "Electronic Governmental Cloud (e-GCloud) " aimed at addressing identified issues and meeting the requirements of cloud computing. The study will conduct a review of existing literature and online sources analyzing studies and articles on the evolving landscape of cloud computing to elucidate its role in e government applications. It aims to outline the deployment strategy for cloud computing in e government settings and propose a novel governmental framework called "e- GCloud” designed as an exclusive private cloud community for national governments use. Additionally, this research, evaluates factors influencing the integration of cloud computing into e government systems by drawing insights from senior government officials and IT personnel within governmental entities. The results suggest that e-GCloud outperforms in applications due, to its enhanced flexibility, resource availability and prompt responsiveness.
Read MoreDoi: https://doi.org/10.54216/FPA.170104
Vol. 17 Issue. 1 PP. 53-66, (2025)
A network Intrusion detection system is a system that can find out different types of attacks. ANIDS is used to find out the noble type of attack by using machine learning and deep learning techniques. These techniques are very useful to find out those attacks whose patterns are not stored in the database. Therefore, these types of systems need more research to improve their accuracy and reduce the false alarm rate. In this paper, we are going to propose an ensemble framework for NIDS using different ML and DL techniques. In this paper, we have used the XGBOOST algorithm for feature extraction and for classification, CNN and RNN deep learning techniques are used. This ensemble model is used for the binary and multiclassification of attacks. Our model was checked on the dataset CICIDS-2018 which gives a better accuracy and low false alarm rate.
Read MoreDoi: https://doi.org/10.54216/FPA.170105
Vol. 17 Issue. 1 PP. 67-77, (2025)
The development and productivity of maize, an important crop worldwide, may be stunted by several nutritional deficiencies. If we want to increase maize output, we need to find these problems quickly. This study suggests a thorough method for identifying nutritional deficits in maize plants by analyzing leaf photos. Our approach combines deep learning algorithms with conventional machine learning methods to analyze and extract information from these pictures. The four types of nutritional deficiencies that were examined are zinc (Zn), potassium (K), nitrogen (N), and phosphorus (P). The standard machine learning method uses Gabor, Discrete Wavelet Transform, Local Binary Pattern, and Gray-Level Co-occurrence Matrix (GLCM). Then, classification is done using algorithms like Support Vector Machine (SVM), Decision Tree, and Gradient Boosting. According to our experimental data, machine-learning algorithms successfully diagnose nutritional deficits in maize plants. The results of this study highlight the promise of machine learning algorithms for improving agricultural yields via better plant nutrition management. Farmers and agricultural specialists may greatly benefit from automated image analysis that can identify nutritional deficits in maize plants quickly and correctly. This technology has the potential to contribute to the sustainability and security of food on a worldwide scale.
Read MoreDoi: https://doi.org/10.54216/FPA.170106
Vol. 17 Issue. 1 PP. 78-94, (2025)
With the prevalence of stress-related disorders on the rise, there is an increasing demand for advanced methodologies that can effectively detect and analyze stress levels. In response to this need, this research explores the integration of Fast Fourier Transform (FFT), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) techniques for unlocking insights into stress dynamics from Electroencephalogram (EEG) signals. Stress, a multifaceted phenomenon with far-reaching implications for mental health, necessitates innovative approaches for its identification and management. The study begins by elucidating the complexity of stress and its impact on individuals' well-being, highlighting the urgency for accurate and efficient stress detection methodologies. Building upon this foundation, the technical intricacies of FFT, SVM, and CNN integration are explored, elucidating their respective roles in the stress detection framework. The FFT method is employed for spectral analysis of EEG signals, providing a foundation for identifying stress-related patterns in the frequency domain. The application of Artificial Neural Networks (ANNs) for feature extraction and classification is explored, leveraging their capacity to discern intricate relationships within EEG data structures. Complementing ANNs, Support Vector Machines (SVMs) are harnessed for stress level classification, capitalizing on their robustness and efficiency in handling high-dimensional data spaces. Furthermore, Convolutional Neural Networks (CNNs) are integrated into the framework to automatically learn hierarchical features from raw EEG signals, enhancing the accuracy and efficacy of stress detection methodologies. Through comprehensive evaluation and comparison with existing algorithms, the integrated approach demonstrates superior performance across key metrics. Stress detection algorithms, such as SVM, exhibit accuracy levels ranging from 70% to 96.5%, with our proposed approach achieving remarkable results. The integrated model achieves an accuracy of 96.5% and an Area under the Curve (AUC) of 0.98, surpassing existing methods in terms of accuracy, sensitivity, specificity, and AUC.
Read MoreDoi: https://doi.org/10.54216/FPA.170107
Vol. 17 Issue. 1 PP. 95-106, (2025)
A wireless body area network (WBAN) is a wireless sensor network (WSN) that is essential to monitor patient health. Sensor nodes (SNs) are commonly positioned either inside or outside the patient's body within this network. These nodes have the ability to send data to the sink node if any functional modifications in the patient are observed. Delivering efficient routing and energy management of network nodes is a complex effort in WBAN. The energy efficiency of SNs is a primary challenge to the effective deployment of WBAN. To handle this problem, a new metaheuristic optimization algorithm called Elk Herd Optimizer (EHO) is proposed in this research. This research aims to focus on energy-efficient routing methods in WBAN sensors that are connected to the human body to enhance health monitoring efficiency. The proposed WBAN model includes the deployment of eight biosensor nodes on the human body. The primary objective is to minimize the energy utilization of WBANs by selecting the most appropriate cluster heads (CHs) based on the EHO. The EHO-based routing protocol showed higher performance in WBANs in terms of energy consumption, End-to-End (E2E) delay, packet delivery rate (PDR), network lifetime (NLT), packet loss rate (PLR), and throughput. The research model was validated by comparing its findings with the existing routing protocols. The research model surpassed all the comparable models in terms of energy consumption, latency, NLT, PDR, PLR, and throughput. The routing protocol based on the EHO algorithm improves energy efficiency by effectively selecting CHs and routing paths. The EHO model efficiently reduces the total time delay, which is essential for monitoring health in real time. It achieves a high PDR while maintaining a low packet loss rate. Furthermore, the EHO-based routing extends the longevity of the network. Additionally, it enhances network performance, hence facilitating uninterrupted and dependable monitoring of health data.
Read MoreDoi: https://doi.org/10.54216/FPA.170108
Vol. 17 Issue. 1 PP. 107-123, (2025)
The incidence of cancer cases has been rising rapidly over the last few decades. Skin cancer is one of the widely found types of cancer, is further classified into two main types, Melanoma and Non-Melanoma. Though Melanoma is less common than other types of skin cancer, it can be lethal if not treated promptly. But it is not the only type of skin lesion that needs attention. It becomes necessary to promptly identify and classify the skin lesions for the recovery of the patient. The machine learning models of Deep Learning prove to be very efficient in this regard. Hence, we developed a deep learning model which is an ensemble of InceptionV3, Xception and ResNet152 models. It can classify the skin lesions into seven main types -Melanoma, Melanocytic Nevi, Benign Keratosis-like lesions, Basal cell carcinoma, actinic keratosis, vascular lesions, Dermatofibroma. The method was applied to dermoscopic images from the HAM10000 dataset. The presence of noise and artifacts in the images makes it difficult to classify. So, as a preprocessing step, we performed hair removal on the dermoscopic images which is a series of methods that starts with blackhat filtering, subsequently creating a mask for inpainting and then applying the inpainting algorithm. Further Contrast enhancement was performed by applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm on the luminance channel of HSV image to improve the contrast of the image and also makes sure that it is not over-amplified. It is then followed by Skin Lesion Segmentation where a grabcut algorithm is applied on the enhanced image which segments the image. Thus, the segmented images are produced which are fed to the Model for training and testing. To cope up with the unbalanced dermoscopy image dataset available, we performed Image augmentation on the images generated in the previous step which alters the existing images to create some more images for the model training process, thus solving the problem of paucity of dataset and substantially increases the performance of the model. The final dataset generated is fed to the three deep learning models InceptionV3, Xception and Resnet152 which achieved an accuracy of 84.6%, 86.5% and 86.7% respectively. These were later given to two different ensemble models - Stacking and Random Forest. The Stacking model achieved an accuracy of 88.6% and Random Forest achieved an accuracy of 92.59%. The proposed system includes a GUI for a good user experience.
Read MoreDoi: https://doi.org/10.54216/FPA.170109
Vol. 17 Issue. 1 PP. 124-134, (2025)
Sign language recognition is important for enhancing message and user-friendliness for the community of deaf and hearing-impaired people. This paper proposes a Malayalam Sign Language (MSL) method using sign language that emerged from the state of Kerala. The main factor contributing to this emergence of such regional sign language is the absence of a standardized and consistent approach to the use of Indian Sign Language (ISL) in various states. This is due to the variations in signs, grammar, and syntax used in different regions. The system uses the You Only Look Once v8 (YOLOv8) algorithm-based object detection method which is based on Convolution Neural Network (CNN), a widely accepted deep learning neural network design employed mainly in computer vision. As the dataset for MSL is not publicly available, we used an MSL video from YouTube provided by the National Institute of Speech and Hearing for training a custom model. We pre-processed the video to extract the frames and annotate them with sign labels. Then, we trained the YOLOv8 algorithm on the annotated frames to detect the hand region and recognize signs in real time. The proposed approach achieved an accuracy of 97.21% calculated from the mean Average Precision value on the MSL dataset. The result achieved outperformed other existing approaches even while using less dataset count compared to others.
Read MoreDoi: https://doi.org/10.54216/FPA.170110
Vol. 17 Issue. 1 PP. 135-145, (2025)
For many years, scientists have studied the way people express their emotions through body language and facial expressions. However, it is extremely difficult to accurately interpret the emotions of a person from just a single image. Interpreting facial emotions in photographs is a complex task. It is challenging to accurately detect facial emotions with the help of neural networks when the face is occluded with fragmentary blocks. With the advent of technology, emotion detection has become more accurate and reliable. It is now possible to use facial expression recognition in images to detect emotions such as happiness, sadness, anger, fear, surprise, and more. This research discusses the effectiveness of using neural networks to identify facial emotions in photographs with occlusions present. The datasets like Fer2013 dataset, CREMA-D and RAVDESS were used to train the model and the datasets were altered by implanting occlusions randomly in the images. The altered datasets were also used to evaluate the model. The challenges and opportunities that arise when neural networks are used in this context are explored. Additionally, insight is also provided into the best approach to accomplish the task.
Read MoreDoi: https://doi.org/10.54216/FPA.170111
Vol. 17 Issue. 1 PP. 146-158, (2025)
This article focuses on improving the accuracy and efficiency of multimodal human motion analysis using advanced techniques. Initially, Generative Adversarial Networks (GANs) were used for skeletal enhancement, and then Contrast-Limited Adaptive Histogram Equalization (CLAHE) was applied on the enhanced images to check the quality Joint-level. Limb-level, Temporal, Statistical Features are effectively recovered from contrast enhancing images. Furthermore, with the selected optimal features acquired from PutterFish Customized Serval Optimizer (PFCSO), the RehabNet++ architecture that makes the human movement assessment has been trained. This PFCSO model has been developed based on the inspiration acquired from the Pufferfish Optimization Algorithm (POA) and the Serval Optimization algorithm (SOA), respectively. The RehabNet++ architecture includes an optimized Multilayer Perceptron (O-MLP), STR-ResNet architecture, Attention-based Convolutional Neural Networks and Transfer Learning. The O-MLP model has been formulated by optimizing the hidden layers of MLP using the PFCSO model. In addition, Grad-CAM visualization is included to provide a graphical description for model selection. A comparative study has been conducted to test the proposed deep learning algorithm against the original methods using the Kimore dataset. This analysis is implemented in PYTHON and is dedicated to multimodal human motion analysis.
Read MoreDoi: https://doi.org/10.54216/FPA.170112
Vol. 17 Issue. 1 PP. 159-182, (2025)
This study presents a novel approach to predictive modeling of muscular performance and fitness progression using artificial intelligence techniques. Leveraging advanced machine learning algorithms, including artificial neural networks (ANN), support vector machines (SVM), and gradient boosting machines (GBM), we develop a comprehensive model capable of accurately forecasting key metrics related to muscular strength, endurance, and overall fitness. Extensive experimentation and evaluation demonstrate the superiority of the proposed method over existing algorithms across a range of performance metrics, including accuracy, precision, recall, F1-score, and error metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Our findings highlight the importance of feature selection techniques and model hyperparameter optimization in driving predictive performance, underscoring the need for careful model development and tuning. The practical implications of our research extend to sports science and athletic training, where the proposed method can inform personalized training strategies tailored to individual athletes' needs and goals. Moving forward, further research is needed to validate the robustness and generalizability of the proposed method across different populations and athletic disciplines, as well as to explore its integration with real-time data sources for more dynamic and responsive training programs.
Read MoreDoi: https://doi.org/10.54216/FPA.170113
Vol. 17 Issue. 1 PP. 183-195, (2025)
The stock price exhibits quick and extremely nonlinear fluctuations in the financial market. A prominent worry among scholars and investors is the correct prediction of short-term stock prices and the corresponding upward and downward trends. Financial organizations have successfully incorporated machine learning and deep learning techniques to anticipate time series data accurately. Nevertheless, the precision of these models' predictions still needs improvement. Most current studies employ single prediction algorithms that cannot overcome intrinsic limitations. This paper proposes a methodology that utilizes the MUTUAL, principal component analysis (PCA), and Long Short-Term Memory (LSTM) model to accurately simulate and predict the variations in stock prices. The technology is utilized for the three global stock market datasets: TSLA, S&P500, and NASDAQ. The highest level of improvement achieved is a correlation of 99%. Furthermore, there is a reduction in error for the metrics MSE, MAPE, and RMSE, with improvements of 0.0001, 0.009, and 0.01 correspondingly.
Read MoreDoi: https://doi.org/10.54216/FPA.170114
Vol. 17 Issue. 1 PP. 196-208, (2025)
The increasing use of credit cards, especially for online payments, has led to a significant increase in fraud involving credit card payment technologies. Financial companies must enhance fraud detection systems to mitigate significant losses. This study introduces a methodology for developing a credit card fraud detection system that uses the Synthetic Minority Oversampling Technique (SMOTE) to address an imbalanced dataset problem and an attention layer to identify important features in the input sequence, two long short-term memory (LSTM) layers modeling long-run dependencies within a sequence of transactions, a dropout layer that neglects values lower than 0.3, and two dense layers, which allows enhancing the accuracy of prediction of fraudulent transactions. When implemented, the proposed system achieves an accuracy of 0.9434% on the IEEE dataset, 0.9850% on the Banksim dataset, and 0.9757% on the European dataset. This methodology shows improvements in fraud detection, emphasizing its ability to enhance financial security systems and reduce misclassification in credit card transactions.
Read MoreDoi: https://doi.org/10.54216/FPA.170115
Vol. 17 Issue. 1 PP. 209-220, (2025)
In Wireless Sensor Networks (WSN), congestion control plays a crucial role as the traffic load surpasses the capacity of each major channel. The WSN constrained resources must be taken in consideration while devising such strategies to get the best throughput. Various factors are contributed in the congestion; the primary factor is the over flowing buffer, packet loss, reduce network throughput and loss of energy. This research, studies path load distribution in novel networks, including anonymous communication. Initially there is a chance that the public Wi-current Fi approach will result in notable imbalances. We next modify an optimal path-selection algorithm and use flow level visualization to show that this results in a substantially improved network load balance. Web-based Congestion Control (WCC) needs to make it possible to give WCC channel flows a distinct quality of service (QoS) in order to overcome this difficulty.
Read MoreDoi: https://doi.org/10.54216/FPA.170116
Vol. 17 Issue. 1 PP. 221-228, (2025)
Face detection is important in computer vision and image processing, particularly in surveillance, security systems, video analytics, and facial recognition applications. However, face detection algorithms face challenges like position variations, lighting fluctuations, size and resolution differences, facial expressions, and background clutter. This research aims to develop a system that achieves high accuracy in detecting and localizing faces using local descriptors and spatial feature extraction techniques, specifically the Histogram of Oriented Gradients method (HOG). Using videos from the YouTube Face database, features were extracted from frames and trained using a convolutional neural network (CNN). The HOG technique achieved a 94% accuracy rate and good localization compared to CNN without feature extraction.
Read MoreDoi: https://doi.org/10.54216/FPA.170117
Vol. 17 Issue. 1 PP. 229-237, (2025)
This study proposes an intelligent system designed to detect and manage epidemic outbreaks within institutional settings by leveraging a fusion of advanced AI technologies. The system operates through five key stages: symptom-based diagnostic testing, AI-powered cough detection, analysis of X-ray and CT scan images using Convolutional Neural Networks (CNN), evaluation of vital signs, and the geolocation of COVID-19 patients using GPS. Cough detection is enhanced by integrating Short-Time Fourier Transform (STFT) and Mel-Frequency Cepstral Coefficients (MFCC). Trained on an extensive dataset comprising over 5,856 CT scans, 7135 X-ray images, and over 30,000 crowdsourced cough recordings, the system demonstrates a high accuracy rate of 95% in identifying potential epidemic cases. This fusion of techniques offers a robust solution for early detection and rapid intervention, significantly mitigating the risk of widespread transmission within high-density environments.
Read MoreDoi: https://doi.org/10.54216/FPA.170118
Vol. 17 Issue. 1 PP. 238-252, (2025)
Machine learning (ML) is the most up-to-date approach for classifying music genres. Due to technological ML advancements, its technologies can help in music genre recognition best. In machine learning, effective fusion of different features could improve recognition performance. Hence, this paper presents a new robust method for Arabic music classification based on the fusion of different sets of features. Frequency-domain, time-domain, and cepstral domain features have been combined and compared with other state-of-the-art approaches. Four machine-learning models that categorize music into its appropriate genre have been created: support vector machines (SVM), K-nearest neighbors (KNN), naïve Bayes (NB), and random forest (RF) classifiers were utilized in a comparative analysis of other ML algorithms, and the accuracy of these models has been assessed and derives the appropriate conclusions. To assess the performance of our method, two various datasets are used: the collected dataset, namely Zekrayati, which was collected by authors in favor of this paper, and the global GTZAN dataset, which was used to compare with previous studies. The experimental findings indicated that the SVM exhibited a higher optimal accuracy of 99.2% and has proven that the fusion proposed features will help to classify music in different fields.
Read MoreDoi: https://doi.org/10.54216/FPA.170119
Vol. 17 Issue. 1 PP. 253-263, (2025)
In e-commerce, web mining for page recommendations is widely used but often fails to meet user needs. To address this, we propose a novel solution combining semantic web mining with BP neural networks. We process user search logs to extract five key features: content priority, time spent, user feedback (both explicit and implicit), recommendation semantics, and input deviation. These features are then fed into a BP neural network to classify and prioritize web pages. The prioritized pages are recommended to users. Using book sales pages for testing, our results demonstrate that this solution can quickly and accurately identify the pages users need. Our approach ensures that recommendations are more relevant and tailored to individual preferences, enhancing the online shopping experience. By leveraging advanced semantic analysis and neural network techniques, we bridge the gap between user expectations and actual recommendations. This innovative method not only improves accuracy but also speeds up the recommendation process, making it a valuable tool for e-commerce platforms aiming to boost user satisfaction and engagement. Additionally, our system’s ability to handle large datasets and provide real-time recommendations makes it a scalable and efficient solution for modern e-commerce challenges.
Read MoreDoi: https://doi.org/10.54216/FPA.170120
Vol. 17 Issue. 1 PP. 264-271, (2025)