International Journal of Innovations in Science & Technology
Not a member yet
813 research outputs found
Sort by
High Isolation Low Profile MIMO Antenna for 5G Applications
Full text linkThis article focuses on the multi-input multi-output (MIMO) antenna with smaller dimensions and improved isolation between the ports which results in good performance and is suitable for modern wireless communication systems, especially USB dongle and 5G mobile applications. Two main problems with the conventional antennas are (i) Large height between the top to ground plate and (ii) Smaller isolation between the ports. These two problems are carefully addressed in the proposed design. The height in the proposed antenna is minimized up to 4mm between the top and to ground plate. Further achieved isolation is more than -18dB by introducing a novel type of periodic slots in the ground plate of the antenna. The antenna structure consists of a ground plate and two top plates, each with defined dimensions. The parameters are optimized using software for the best performance of the antenna to ensure efficient signal transmission and reception. The proposed antenna aims to explore the impact of dimension parameters like impedance matching and fractional bandwidth and isolation between the ports. The antenna resonates for 4.5GHz to 5.75GHz frequency bandwidth. The return loss for impedance bandwidth is below -20dB. The antenna is simulated on software and fabricated on substrate FR4. The simulated and measured results are compared which indicates that this antenna with high isolation and good impedance bandwidth is a more suitable candidate for 5G mobile applications. Compared to the conventional same-class antenna, the proposed antenna has good performance with minimized dimensions
Smart Gas Geyser with Real-Time Data Collection
Full text linkThis paper "Smart Gas Geyser with Real Time Data Collection" presents an IoT-based Smart Gas Geyser system designed to address energy inefficiency, safety risks, and user inconvenience in conventional water heaters. This paper proposes an IoT-based smart gas geyser system that enables remote monitoring and real-time data collection via a mobile application. This data-driven approach allows for automated adjustments and instant alerts, enhancing both safety and performance. The system integrates ESP32 with various sensors to monitor temperature, gas leakage, and flame presence. Data is transmitted to a Firebase real-time database, allowing users to make informed decisions via a mobile app. The user-friendly mobile app provides features such as temperature setting, real-time monitoring, and automatic shutoff, making geyser operation seamless and secure. The proposed system enhances energy efficiency, ensures safety, and provides cost-effective automation for domestic and industrial applications. The study also discusses experimental results, comparative analysis with conventional geysers, and future recommendations
A Hybrid Approach to Fine-Grained Butterfly and Moth Classification Using Deep Features and Rhombus-Based HOG Descriptor
Full text linkButterfly and moth species are crucial for ecosystems as pollinators, pests, and biodiversity indicators, therefore necessitating their precise automated classification for extensive monitoring, conservation initiatives, and agricultural pest control. Nonetheless, considerable obstacles emerge from inter- and intra-species variety in wing coloration, patterns, posture, and the effects of lighting and background circumstances on pictures. This study presents a comprehensive framework that enhances feature representation via a dual-phase methodology. Initially, pictures undergo preprocessing by Contrast-Limited Adaptive Histogram Equalization (CLAHE) to augment distinguishing features. Subsequently, elevated semantic features are derived using a ResNet50 backbone pre-trained on ImageNet, with a baseline accuracy of 92%. A unique Corner Rhombus Shape HOG (CRSHOG) descriptor is suggested to accurately capture detailed geometric and textural wing properties, utilizing rhombus-based grid sampling and gradient orientation encoding. These complementary deep and handcrafted features are carefully integrated to form a hybrid representation, improving resilience to cluttered backdrops and position changes. The integrated feature set is assessed using several classifiers, with an Ensemble Subspace KNN model attaining the greatest classification accuracy of 94.6% on the Butterfly and Moth Image dataset, exceeding traditional CNN (Convolutional Neural Network)-only and HOG-based methods. These findings highlight the benefits of combining domain-specific shape descriptors with deep-learning features to enhance fine-grained insect categorization. Moreover, depending exclusively on standard RGB photos facilitates practical implementation on mobile and aerial platforms for real-time biodiversity surveillance and pest management. Future endeavors will concentrate on expanding this hybrid feature technique to encompass live video tracking and open-set species detection in uncontrolled settings
Generative AI Ethical Challenges: By Creative and Professional Communities
Full text linkThis Paper investigates the ethical transformations and creative dilemmas emerging from the widespread adoption of generative artificial intelligence (GenAI) in content creation. The study examines attitudes regarding authorship, ethical issues, and regulatory rules by conducting interviews with 120 GenAI users from academic, creative, and professional fields. Results show that most participants prefer to give credit to co-authors or themselves when using GenAI and only a small percentage want the AI to have sole authorship. Concerns over ethics are moderate and almost always present, reaching their highest-level concerning liability (3.12), then labeling (3.00), and then bias (2.98) on a 5-point scale. Although individuals frequently used GenAI tools, there was no clear link between the amount of GenAI they used and their sensitivity to ethics. People working in creative fields were more likely than technologists to back stronger government oversight. Users notice GenAI’s ability to generate fresh ideas, though they also have doubts about its accountability, the roles it plays in knowledge, and its ability to replace human creativity. It ends by urging the development of strategies and education focused on ethical principles, ensuring that technology serves society
Delving into the Practices Involved in the Creation and Dissemination of Misinformation
Full text linkThis study investigates the authenticity of news with specific training features validating the same with specific machine-learning techniques. The contents of fake news are created to make credible information that would create mass opinions and provide a strong basis to convince the readers or confuse them utterly. The fake information is usually disseminated using numerous automated algorithms. Therefore, it is very quintessential to identify the sources and authenticity of such information. With recent advancements in information communication technology, there exists a cluster of deep knowledge from which a user intends to retrieve relevant information such as news articles. For data mining and classification tasks such as fake news classification, the approach of machine learning can be employed for effective experimentation. To address the raised issues in this study, a comprehensive and diversified dataset was required that must contain relevant knowledge with sentiment tags such as authentic and fake news. To fulfill the same, a corpus comprising over 44k authentic and fake news items is collected. Moreover, this study emphasizes news classification as fake or authentic using data mining and analytics
A Robotic Simulation for Aerial Monitoring and Disease Detection of Gladiolus Field
Full text linkAgriculture is an essential sector that is witnessing the integration of advanced technologies to improve productivity and efficiency. Aerial crop monitoring using drones has surfaced as a pivotal technology for precision agriculture, allowing farmers to collect detailed data regarding crop health, soil conditions, and pest infestations. A robotic farm monitoring system in simulation can provide an initial platform to test various automated services before deploying them in the real field. This paper presents an agricultural robotic simulator currently developed for the gladiolus field. Simulation has been designed using V-REP (now known as CoppeliaSim) and Robot Operating System (ROS). Autonomous path planning and navigation are achieved through Hector Simultaneous Localization and Mapping (SLAM) and Rapidly Exploring Random Trees (RRT). One of the most common and fatal diseases of the gladiolus plant named ’Fusarium yellow’ has been successfully detected through image processing. This simulation is specifically designed to save resources and reduce the time and cost of developing and testing real-time autonomous aerial robotic systems and test algorithms for crop monitoring. Usability evaluation of the developed system through user survey shows positive results
Detection of Application-Layer Dos Attacks in IoT Devices Using Feature Selection and Machine Learning Models
Full text linkWith technological advancements, innovations like the Internet of Things (IoT) have become widespread, connecting more devices to the Internet. However, as the number of connected devices increases, cyber-attacks—especially Distributed Denial of Service (DDoS) attacks—are also becoming more frequent. This research explores these cyber threats, focusing on DDoS attacks, and proposes strategies to protect IoT devices. It specifically aims to detect DDoS attacks in IoT devices using feature selection methods and machine learning algorithms. The study targets attack detection at the application layer of IoT devices by analyzing a relevant dataset. By applying feature selection techniques and machine learning models, we strive to enhance the accuracy and efficiency of DDoS detection, ultimately improving IoT securit
Developing an Arabic-Urdu Ontology of Quranic Concepts: A Semantic Approach
Full text linkAn Arabic-Urdu ontology system dedicated to Quranic concepts represents a necessity for protecting the semantic value and making religious texts more accessible during Quranic study. Ontology-driven annotation tools show their ability to achieve precise translations and thematic searches by establishing their effects on the translation process. Researchers built this ontology using Protégé 5.6.4 which classifies Quranic concepts into twelve specific sections from Corpus.quran.com: Artifact, Astronomical Body, Event, False Deity, Holy Book, Language, Living Creation, Location, Physical Attribute, Physical Substance, Religion and Weather Phenomena. Validation of the ontology included expert evaluation and a HermiT computational assessment that led to user testing and an accuracy rate of 89.31%. The system uses SPARQL queries as a method to achieve both organized and efficient retrieval of Quranic knowledge. The analysis emphasizes the value of ontological structures as a means to connect Arabic and Urdu semantics which then improves both Quranic interpretation and computational linguistic understanding. While the methodology effectively maps Quranic concepts, challenges such as language nuances and theological precision persist, requiring further advancements in machine learning and natural language processing. Future research should focus on expanding ontology categories, integrating AI-based models, and enhancing phonetic mappings to improve the ontology’s adaptability and usability in diverse linguistic and cultural settings
Effect of Concentration Variation on Zirconium Nickel Cobalt Metal Organic Framework-Based Electrode Material
Full text linkIn this research, two samples of a ZrNiCo ZIF-67 with the change in molar concentration of metal to linker (1:1 and 1:2) were synthesized via the co-precipitation method. Then electrode fabrication was done. An attractive candidate for supercapacitor electrodes, ternary metal oxides ZIF 67 exhibit several desirable properties, including a large surface area, porosity, chemical stability, tailoring ability, redox activity, and low environmental impact. The porous polyhedral structure of ZrNiCo ZIF-67, which incorporates connected nanoparticles of varied compositions, greatly enhances the charge storage capacity. They are essential to a robust and sustainable energy future, and they have social, ecological, and economic significance. Electrochemical methods such as cyclic voltammetry (CV), galvanostatic charge and discharge (GCD), and electrochemical impedance spectroscopy (EIS) are among the various characterizations used to assess the electrode\u27s performance. Other approaches include X-ray diffraction to study the crystal structure. With a specific capacitance of 232 F/g at a current density of 1 A/g, the ZrNiCo ZIF-67 (1:2) electrode material performs better than the other ZrNiCo ZIF-67 (1:1) materials. In order to create nanocomposites ZrNiCo ZIF-67 (1:2) with improved electrochemical characteristics, this research provides an easy and practical method. These materials can then be used as electrodes in supercapacitors for high specific capacitance
Impact of Flood Migration on Education in Flood-Affected Areas of Sindh
Full text linkHeavy rain fell during the monsoon season from June to October 2022 and caused urban flooding in Sindh and Balochistan. The Government of Pakistan declared 85 districts as climate-hit. The 2022 flood caused the migration of many people from rural to urban areas of Sindh. Floods had a socio-economic impact on migrant families, causing damage to property, houses, agricultural land, infrastructure, livestock, health, and education. The present study aims to analyze the impact of flood migration on the education of migrant children and the city administration of immigration. For the current study, a survey method was used to collect data from 384 respondents. Data was collected from two districts of Sindh, Dadu (K.N. Shah) and District Naushahro Feroze. In the 2022 flood, highly affected areas were Dadu, Khairpur, and Naushahro Feroze; these districts faced a large number of migrations toward Hyderabad, Jamshoro, and Karachi (PDMA, 2023). A questionnaire was used as a data collection tool, and respondents were selected randomly from those villages from which people had migrated to cities. Data was analyzed in SPSS and presented in graphs. Recommended measures are suggested for policymakers