International Journal of Innovations in Science & Technology
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Design Evolution and Feature Enhancement Strategies for Advanced Digital Stethoscopes
Full text linkA stethoscope is a fundamental auscultation and diagnostic device that plays a major role in medication and assists in the identification of sounds inside the body to predict cardiovascular and respiratory diseases. The advent of electronic and artificial intelligence (AI)-enhanced digital stethoscopes is prompted by the limitations of traditional auscultation performance, such as the need for a clinician\u27s experience, failure to detect the required sounds in noisy conditions, and the inability to store patient data. This study focuses on the evolution in the design, relative performance characteristics, and areas of future improvement of stethoscopes, including digital devices that incorporate AI. Several studies show the employment of advanced filters to acquire important auscultating frequency bands, a high-gain amplifier to boost low-frequency internal body sounds, a noise cancellation circuit to block out background noise, Bluetooth for data sharing in real-time signal processing, and syncing with other medical devices. Key features that could be introduced in future versions are adaptive frequency filters, AI-based clustering to classify the sound, remote diagnostic functionality, and an improved data storage system. Protection circuits that take the form of lithium-ion batteries, wireless modules, and processing based on a microcontroller are some of the resource components highlighted in terms of portability and efficiency. This research seeks to develop stethoscopes by incorporating innovations while managing limitations, ultimately enhancing tools for healthcare professionals
Design of a Novel Compact and High-Efficiency T-Slot Microstrip Antenna for 28 GHz
Full text linkIn this paper, a novel microstrip patch antenna incorporating a T-shaped slot in the radiating patch is proposed to achieve high radiation efficiency and excellent impedance matching for 28 GHz millimeter-wave 5G applications. Utilizing the Rogers RT5880 substrate with a dielectric constant of 2.2, loss tangent of 0.0009, and 0.8mm thickness, the proposed antenna achieves a radiation efficiency of 81.18%, total efficiency of 81.17%, and a peak gain of 7.23 dB over a 2 GHz impedance bandwidth (27–29 GHz). A T-shaped slot is incorporated in the radiating patch to enhance impedance matching and bandwidth. Comparative analysis across ten substrates demonstrates the superiority of Rogers RT5880 in balancing performance, cost, and compactness for mm Wave 5G applications. This innovative microstrip patch antenna design marks a significant advancement in the field, delivering enhanced performance tailored for 5G wireless communication systems
Advanced Deep Learning-Based Potato Defect Identification Leveraging YOLOv8 for Smart Agriculture
Full text linkThis paper presents the design of an effective deep learning model to identify and rank potato defects, enabling intelligent farming and post-harvest tasks. The primary goal is to automate the quality measurement of potatoes in several categories: healthy, damaged, defective, fungal-diseased, and sprouted, with the help of an optimized YOLOv8 model. The data set on potato images was annotated and gathered in the real-world agricultural conditions in a wide variety of images. Data augmentation and transfer learning were used to train the model and enhance generalization and detection rates in different conditions. The experiment showed that the detection performance was high and it reached 95.3% training accuracy, 93.8% validation accuracy, and 92.5% test accuracy with an F1-score of 92.9. The results verify that the suggested approach plays a crucial role in detecting defects in potatoes in real time, which can be used to support comprehensive, computerized, and accurate agricultural surveillance
Automated Objects Delivery System for Interior Locale using Line Following Robot with Optimized Security Parameters
Full text linkAutomated object delivery robots are increasingly sought for convenience, reliability, efficiency, supporting organizational productivity, elderly assistance, and reducing human error and labor costs in indoor delivery tasks. While various security measures have been implemented for the delivery robot’s safety, the design strategies used in existing studies do not suffice as they do not use biometric technology for unlocking the robot and real-time image tracking of robot thievery via mobile app. This research-based project aims to design and develop an object delivery system within small to medium-scale buildings using a robotic prototype controlled via an Android app. The robot navigates using a line-following technique with IR sensors, avoids static obstacles with an ultrasonic sensor, verifies the receiver with a fingerprint scanner, detects the destinations using an RFID module, and captures images of illicit attempts using an ESP32 camera module sending them in the app simultaneously. The designed prototype along with the Android app has undergone several feature tests with varying conditions. The results suggest that the system can securely carry a payload weighing 20 kg and is capable of navigating 10 km with a speed of 5 m/s depending upon the battery power. This project plans to tackle significant Sustainable Development Goals (SDGs) specifically, achieve Quality Education through SDG 4, Decent Work and Economic Growth in SDG 8, and Industry, Innovation, and Infrastructure in SDG 9
A Deep Learning Approach to Semantic Clarity in Urdu Translations of the Holy Quran
Full text linkThe Holy Quran holds profound significance from both religious and linguistic perspectives yet its Urdu translations face difficulties in preserving the original meaning because of ambiguous words which create interpretation challenges for speakers and listeners. This research tackles translation ambiguity in the Urdu translations of the Holy Quran authored by Maulana Abul A’ala Maududi and Fateh Muhammad Jalandhry by applying Word Sense Disambiguation methods with deep learning algorithms. A model based on multilingual BERT identifies ambiguous word senses for Surah Al-Baqarah in particular. The dataset features Surah Al-Baqarah\u27s complete Urdu translation together with a Sense Inventory that contains 3 to 8 senses for 50 frequently used Urdu ambiguous words which are collected from GitHub repository. Sequence classification frameworks within BERT receive contextual embeddings during fine-tuning. The evaluation framework includes the determination of F1 scores alongside confusion matrix analysis and classification report assessment. The model achieved an F1-score of 0.82 when identifying the most frequent sense while reaching an average F1-score of 0.62 across eight predefined sense labels. A sense prediction system functions to improve word sense matching thereby leading to more precise translations. The proposed research makes significant contributions to computational linguistics and Quranic studies by delivering an expandable method that solves word sense ambiguity while offering important insights to help translators and scholars improve their understanding of how context affects meaning within translated texts
AlphaTitan AlphaTitan - An Advanced Multi-Tasking Autonomous AI Assistant for Real-Time Environment Monitoring and Safe
No full textIn today’s world, in terms of industrialization, ensuring safety against potential hazards like gas leaks and fire outbreaks is critical. As a safety measure, this study implements "AlphaTitan," an intelligent, real-time hazard detection system developed to provide accurate detection and timely alerts for enhanced safety in industrial, residential, and public environments to prevent disasters. This system uses sensors that can detect and identify the harmful gases and potential fire hazards, which are the main causes of industrial accidents and health risks in today’s world. After detecting any hazard de, this system instantly triggers alerts, locates the hazard’s real-time location, and also alerts the owner or emergency department by sending the real-time notifications via social media communications systems, like WhatsApp. In addition to this, our system also captures images and records videos of fire hazards. By integrating advanced sensor-based detection technology with IOT devices, Our System “AlphaTitan” provides a reliable, scalable, and affordable solution for safety management, decreasing risks
Optimizing Economic Load Dispatch Using a Hybrid PSO-SA Algorithm: A Novel Approach
Full text linkEconomic Load Dispatch (ELD) is a crucial power system optimization task. It aims to minimize the total cost of electricity generation by strategically allocating power output among available generating units to meet the system\u27s demand while respecting operational limits. This paper investigates how soft computing methods can improve the effectiveness of Electronic Logging Device (ELD) solutions. Specifically, Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms are employed to minimize generation costs for a power system comprising three generating units. The optimization process considers loss coefficients, generation limits, and a predefined cost function. Initially, PSO is used to determine near-optimal solutions, which are further refined using SA to avoid local minima. A hybrid PSO-SA method integrates the global exploration of Particle Swarm Optimization (PSO) with the local refinement of Simulated Annealing (SA) to enhance convergence and solution quality. 1 This approach was implemented in MATLAB and validated through a case study. Simulation results demonstrate that the hybrid method consistently yields high-quality solutions with reduced computational effort, proving its robustness and reliability for solving ELD problems. Combining metaheuristic algorithms shows promise for real-world power system optimization
Deep Learning Based-Cotton Disease Recognition System
Full text linkCotton is a vital cash crop in Sindh, Pakistan, playing a crucial role in the agricultural economy. However, diseases such as Cotton Leaf Curl Virus (CLCV), bacterial blight, and Fusariumwilt significantly reduce cotton yield, affecting farmers\u27 livelihoods. Traditional disease identification methods are labor-intensive, error-prone, and inefficient, necessitating automated approaches for early and accurate detection. This research introduces a deep learning-based cotton disease recognition system, leveraging Convolutional Neural Networks (CNNs) with transfer learning to classify diseases. Experimental results demonstrate that our approach achieves high accuracy, offering an efficient, user-friendly, and scalable solution to promote sustainable agricultural practices in Pakistan
Enhancing Students’ Learning Outcomes Using Gamification
Full text linkGamification has emerged as a promising strategy to improve student engagement, motivation, and academic performance. This study investigates the impact of gamification on second-grade mathematics learning by comparing traditional teaching methods with a gamified approach using the Matific.com platform. Employing a quasi-experimental design, the study involved 40 students divided into control and experimental groups, with data collected through pre- and post-tests and motivation surveys. Statistical analysis revealed that students exposed to gamified learning demonstrated significantly higher academic performance and motivation levels compared to those taught through conventional methods. The results indicate that gamification not only enhances cognitive outcomes but also fosters emotional engagement, suggesting its potential as a scalable and effective tool in early-grade education. This research contributes practical insights for educators and policymakers seeking to integrate innovative methods into traditional classrooms, especially within underperforming education systems like Pakistan’s
Synthesis and Characterization of Silver Nanoparticles Conjugated with Folate and Curcumin for Their Anti-Cancer Activity
Full text linkNanoparticles are small particles with sizes ranging from 1 to 100 nanometers. Silver nanoparticles, composed of silver at the nanoscale, have been widely used in various fields including medicine, healthcare, food, and commercial industries. While silver nanoparticles can be harmful to normal cells depending on their concentration and exposure time, they are highly effective for wound healing and antibacterial applications. Historically, silver was used as a natural antibiotic. In this study, silver nanoparticles were conjugated with curcumin and folic acid using the glutaraldehyde method due to their anti-cancer properties. Curcumin is known for its ability to kill cancer cells, while folic acid—an organic form of vitamin B9—helps in the creation and preservation of healthy cells. The silver nanoparticles were first modified with polyethylene glycol (PEG), then conjugated with curcumin and folic acid. Curcumin was attached through the NH2 group, and folic acid was linked via the carbonyl group, both through PEG. The average crystalline size was calculated using X-ray diffraction (XRD), and functional groups were identified using Fourier-transform infrared spectroscopy (FTIR). These silver nanoparticles are considered to be more beneficial and less harmful than traditional chemotherapy or radiotherapy for targeting and destroying tumor cells