International Journal of Innovations in Science & Technology
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Decoding Cognitive States and Emotions Using the Electroencephalogram
Full text linkEmotions are essential in human communication, social interaction, and decision-making. However, accurately classifying emotions is difficult with many applications in various domains such as psychology, psychiatry, neuroscience, and human-computer interaction. Emotion detection is one of the key challenges in current research, especially when emotional words are used. It is already known that positive and negative words have an impact on human behaviour and emotions, but very rare study that focus on emotions based on the words. In this study, we propose a novel approach for emotion classification based on electroencephalogram (EEG) data elicited by text stimuli, which are various English words. Text stimuli can evoke rich and diverse emotions, but they have been less explored than other modalities for emotion elicitation. In this study, EEG data of 25 participants were used, which were collected using a 128-channel EGI system. The collected data was pre-processed, and features were extracted using four methods: Convolutional Neural Network (CNN), Wavelet Transform (WT), Power Spectral Density (PSD), and the raw data itself was used as features. The results showed that CNN features achieved an average accuracy of 80%, followed by WT with 75%, PSD with 72%, and raw data with 65%. Our study shows the feasibility and effectiveness of using CNN, PSD, and WT with SVM for emotion classification based on EEG data and text stimuli. Lastly, a hybrid model was proposed based on the combination of CNN for feature extraction and SVM for classification
Dynamic Behavior of a Magnetized Multi-Component Hybrid Nanofluid on an Oblique Elongating Interface Affected by Extraction and Permeable Media Interactions
Full text linkThe current study explores the mechanism of heat transfer in non-Newtonian Maxwell tri-component nanofluid flow past an inclined stretching sheet embedded in a permeable medium. The electrically conducting nanofluid is considered under the impact of the Lorentz force. The nanoparticles of three types: Silver, Copper, and Ferric oxide, are considered and mixed with the water taken as a base fluid. The proposed phenomenon in the form of differential equations is solved numerically for the numerical outcomes. These results reflect that the Maxwell fluid parameter has an increasing impact on the velocity of the fluid and a decreasing effect on the temperature. The increasing magnetic force effects highlight the increasing trend in temperature of the fluid and the decreasing impact on the velocity of the fluid. The increasing number of nanoparticles has an increasing thermal effect on the fluid. Similarly, the skin friction and rate of heat transfer are dependent functions of pertinent parameters. The differential equations are solved using the exact solver bvp4c
Abstractive Urdu Text Summarization Using Multilingual Transformer Models: A Deep Learning Approach
Full text linkIn contrast to directly copying source text, abstractive text summarization produces short summaries through an understanding of the text. Urdu\u27s low-resource language, which is also characterized by complexities, presents further obstacles. This text investigates the possible extent of deep learning models to automate Urdu text summarization. With respect to the general summary and particular attention to word choice, we try to address the challenges posed by the Urdu language, and we make use of deep learning models for a dataset of Urdu news articles to produce summaries that are accurate and coherent. BERTScore quantitative analysis reveals that the fine-tuned mBART model has an F1 score of 0.497, which is better than mT5 (0.355). As opposed to the most recent Urdu summarization research (2023-2025) in which the majority of reports include ROUGE-based scores, our methodology exhibits a superior semantic consistency and abstractiveness
Improving Software Requirements Elicitation in Agile Environment
Full text linkRequirement elicitation plays an important role during the software development life cycle. The selection of an improper requirement elicitation method will affect the quality of developed software. Agile methodologies are popular in the industry and follow an incremental approach to developing software. Agile methodologies value customer needs, interaction among teams, interaction with customers, and change management. Researchers proposed methods for requirement elicitation in agile software development. This research aims to investigate the issues faced during requirement elicitation in agile software development. We will identify the method that motivates the requirements elicitation in agile software development to meet our objective. After identifying the literature, a systematic literature review will be performed. An introductory overview, publications trends and values, strengths, and limitations will be highlighted. Based on the identified limitations, we will propose a new requirement elicitation method useful in agile software development. To evaluate the results, two teams of equal expertise were given the same project to develop. One of the teams developed using the proposed framework, and the other one did without using the proposed framework. Then, both of them were given the survey they filled out and gave their input on the requirement elicitation parameters, and the results were compared and validated using a t-test and reliability analysis
Management of Speech Impairment Disorders in Aphasia Patients using Digital intervention with Multilingual Regional Dialects
Full text linkSpeech is a zestful, and intricate activity that enables people to express ideas, emotions, and thoughts. We are able to render our views because of this neural activity. It is a significant process for learning and personal development that every individual deserves to develop, including those with special needs and who are on a journey to learn how to communicate. Children frequently suffer from speech disorders. This entails them at the risk of experiencing social, intellectual, and academic challenges that may persist and affect them in their adolescence and adulthood. In this context, we present a speech therapy solution for special children, to assist kids with speech and language impairments in improving their language skills. The proposed app can act as a useful management tool and rehabilitation system for people with aphasia disorder and their caretakers including parents, guardians, and teachers. This innovative app offers a vast number of features and practice sessions to develop language skills and overcome communication impairment problems. It also supports multiple regional languages, including English, Urdu, and Sindhi allowing users to switch between native languages effortlessly through the settings. The developed app is equipped with a dynamic accuracy assessment, and progress-tracking system, notifying the parents or guardians when practice sessions are missed, ensuring that language development remains consistent and effective. The major novelty of this work is that it has considered regional aphasia patients and their language needs. In contrast to the existing developed therapeutic tools which are mainly oriented towards resource-rich languages, the proposed work aims to address regional languages. The proposed speech therapist App for children can be a powerful tool for parents, caregivers, and educators, providing a fun and interactive way for children to improve their speech and language abilities. The developed solution also offers benefits in the context of enhanced patient involvement, motivation throughout their learning journey, greater flexibility, and accessibility in contrast to in-person therapy, immediate feedback, and careful progress monitoring that makes it easier to assess and modify treatment sessions
Exploring Contextual Similarity in Quranic Ayahs: A Case Study of Surah Al-Baqarah and Aal-e-Imran in Urdu Translations
Full text linkThe translation of sacred texts, particularly the Quran, requires a deep understanding of both linguistic and contextual nuances to preserve the original message. This research investigates the contextual similarity among Quranic Ayahs by analyzing the Urdu translations of Surah Al-Baqarah and Aal-e-Imran from Maulana Maududi\u27s Urdu Quranic translation. Given the importance of accurately conveying the essence of the original Arabic text, this study aims to quantify the contextual relationships between Ayahs within each Surah and assess the effectiveness of Maulana Maududi’s translation in maintaining these relationships. The novelty of this study lies in its application of deep learning, particularly Long Short-Term Memory (LSTM) networks, to evaluate the contextual similarity between Ayahs. The LSTM model is used to capture the deep linguistic and contextual relationships within the translation, offering a data-driven approach to Quranic translation evaluation. The dataset comprises the complete translations of Surah Al-Baqarah and Aal-e-Imran in Urdu, and each Ayah is compared with every other Ayah within the same Surah to compute similarity scores. The results show varying degrees of similarity among Ayahs, with some Ayahs exhibiting high contextual alignment while others display subtle divergences. These findings highlight the ability of LSTM models to uncover hidden patterns in translation, while also pointing out the challenges in preserving the full contextual integrity of the original Arabic text in translation. In conclusion, this study provides valuable insights into the complexities of Quranic translation and offers a novel approach to evaluating the quality of such translations. By combining advanced machine learning techniques with the study of sacred texts, it presents a new avenue for improving the accuracy and contextual coherence of Quranic translations, ultimately contributing to the field of computational linguistics and religious studies
Load Balancing in Cloud Computing: A Proposed Novel Approach Based on Walrus Behavior
Full text linkThis research provides a comprehensive evaluation of load-balancing algorithms in cloud computing, classifying them into static, dynamic, and nature-inspired categories. Static algorithms, such as Round Robin and Min-Min, offer simplicity and efficiency in environments with stable workloads but struggle with adaptability to varying demands. Dynamic algorithms like Throttled Load Balancing and Least Connection are more flexible, adjusting to real-time server load changes and improving resource utilization, though they introduce higher overhead and computational costs. Nature-inspired algorithms, including Ant Colony Optimization and Particle Swarm Optimization, draw from biological processes to achieve high scalability, fault tolerance, and adaptability. A novel Walrus Optimization Algorithm (WaOA) is proposed, inspired by the social and migratory behaviors of walruses, to address challenges such as task bottlenecks and resource underutilization. MATLAB simulations reveal that WaOA outperforms traditional and nature-inspired methods in terms of scalability, response time, and resource optimization. The study concludes with suggestions for integrating machine learning, hybrid techniques, and real-world testing to further enhance WaOA’s effectiveness
Cysteine-Coated Cadmium Sulfide Nanoparticles Conjugated with Curcumin for Antimicrobial Activity
Full text linkNanoparticles have several applications in drug delivery. Attaching therapeutics to specially designed carriers enables precise delivery to specific cells. Nanostructures have unique physicochemical and biological features, such as an increased reactive surface area and the ability to pass through tissues and cell walls due to their small size, making them a promising material for biomedical applications. Cysteine-coated cadmium sulfide nanoparticles were prepared using a wet process at high pressure and temperature, followed by curcumin conjugation. The antioxidant, anticarcinogenic, and anti-inflammatory properties of curcumin are well acknowledged. Cadmium sulfide nanoparticles are of extremely good semiconducting material that shows fluorescence at a particular wavelength in spectrophotometric analysis. X-ray diffraction (XRD) of the nanocomposite was conducted to verify the crystalline nature of nanoparticles and to find the average crystallite size of cadmium sulfide nanoparticles. The Fourier-transform infrared spectroscopy (FTIR) confirmed the conjugation of cysteine with CdS and curcumin. Antibacterial activity of the synthesized material against Escherichia coli (E. coli) cells was assessed at different concentrations. The antibacterial activity of conjugated cadmium sulfide nanoparticles against E. coli bacteria was examined using the well diffusion method. The results showed that cadmium sulfide nanoparticles coated with cysteine and conjugated with curcumin had better cytotoxicity against bacterial infections caused by E. coli bacteria
A Revolutionary Approach Using Artificial Intelligence and Quantum Cryptography – A Review
Full text linkData security is one of the most important aspects of the digital world as technology evolves and expands. Existing cryptographic systems are vulnerable due to quantum threats. The integration of Artificial Intelligence with Quantum Cryptography is an emerging field. AI-driven methods in QC to mitigate and be robust against the quantum threat. Quantum computing uses quantum mechanics to process data very quickly and accurately. Quantum Machine Learning can process big data as compare to classical methods with much more efficiency. The synergistic combination improves the threat detection and classification with accuracy. The integration also significantly enhances the speed and scalability of the large-scale deployment. AI enhances the efficiency and security of QC systems, and the challenges and opportunities of using AI-powered integration of quantum computing are reviewed
Thermal Macro-Modeling and Safe Operating Area Analysis of MOSFETs
Full text linkThermal dissipation in electronic circuits is always an important design constraint. Excessive heat can degrade component performance, reduce lifespan, and in severe cases, cause permanent failure. This paper uses the thermal modeling approach at the circuit level and focuses on the Safe Operating Area (SOA) of MOSFETs (Metal-Oxide-Semiconductor Field-Effect Transistors). The SOA defines the operational limits of MOSFETs by considering the power dissipation to prevent thermal runaway and device failure. In the area of power electronics, it is important to ensure the reliability and efficiency of circuits under different thermal conditions. In this paper, the thermal behavior of MOSFETs is modeled considering factors such as ambient temperature, gate capacitance, PCB (printed circuit board) thermal dissipation, and heatsink addition. This research highlights the importance of thermal design principles in predicting the junction and case temperatures of MOSFETs under various operating conditions. This systematic approach to thermal macro-modeling is crucial for optimizing the performance and reliability of electronic circuits, particularly in high-power applications where thermal management is a critical concern