International Journal of Innovations in Science & Technology
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Numerical Simulation of Flow Past a Square Object Detached with Controlling Object at Various Reynolds Number
Get PDFA two-dimensional (2-D) numerical study has been conducted for flow past of two different configurations of square objects by using the numerical technique Lattice Boltzmann Method (LBM). In these configurations, one object plays the role of the main object, while the second object acts as a controlling object positioned in two different ways, such as firstly placed at the top right corner of the main object (first configuration) and in the second configuration the control object is put at the bottom right corner of the main object at L = 20d (where d is the size of the object). The primary goal of this study was to investigate the impact of the control object on the main object to reduce fluid force and suppress the vortex shedding. Initially, the code\u27s validity was checked, and the effect of the computational domain was studied to determine accurate upstream (Lup), and downstream (Ldown) distances and height of channel (H). Subsequently, all the numerical computations were performed by considering the range of Reynolds numbers (Re = U∞d/ʮ) Re = 80 to 200. The results are presented in terms of vorticity contour, drag (Cd) and lift coefficients (Cl), and physical parameters, including Cdmean, Cdrms, Clrms, and St. In the vorticity contour, three distinct modes of flow structures were observed for the first configuration (where the control object is placed at the bottom corner of the main object), such as i) Von Karman vortex street (VKVS) flow mode, ii) Two rows vortex street (TRVS) flow mode and iii) Critical flow mode (CF). For the second configuration, two different types of flow modes are identified, dominating the critical flow behavior, those are i) Irregular vortex shedding (IVS) flow mode and ii) Critical flow (CF) mode. The values of Cdmean, Cdrms, Clrms, and St are calculated against the Reynolds number. For the main object in both configurations, the value of Cdmean decreases at the lower range of Reynolds numbers and then continuously increases at larger values of Reynolds numbers. However, for the control object, the mean drag coefficient consistently increased with an increment in the range of Reynolds numbers. The maximum value of Cdmean is attained at Re = 200, reaching the value of 2.0708 for the configuration where the control object is placed at the top right corner. Similarly, the highest value of the Strouhal number is obtained for the control object; placed at the bottom right corner for C2, with a value of 0.1321 occurring at either Re = 100 or Re = 120
Design of a High Gain Dual Band Patch Antenna with T Slot Ground Structure for Millimeter Wave Communication Applications
Get PDFThis paper introduces a novel design approach for achieving high gain, dual-band operation, and enhanced bandwidth in a microstrip patch antenna tailored for 5G applications. The antenna operates at the millimeter-wave bands of 28 GHz and 38 GHz, crucial frequencies for the next-generation 5G wireless communication systems. The proposed design employs two inverted T-shaped slots on the patch to enable dual-band functionality. Simultaneously, a very high gain is attained by strategically inserting two inverted T-shaped slots on the radiating element of the patch. To further improve the antenna\u27s bandwidth, a ground slot structure with three different types of slots U-shaped, L-shaped, and T-shaped are compared on the ground plane. The best bandwidth enhancement is achieved by T shape Slot on both bands. The substrate chosen for the antenna fabrication is Rogers RT Duroid 5880, characterized by a thickness of 0.501mm, a low loss tangent of 0.0009, and a relative permittivity constant of 2.2. The simulations are conducted using Ansys HFSS software proposed antenna design, demonstrate impressive performance metrics. Maximum gains of 17 dB at 28 GHz and 38 GHz are achieved form T shape slot ground configuration, the U-shaped slot configuration yields a maximum gain of 15 dB, and the L-shaped slot configuration achieves 7.8 db. Furthermore, the impedance bandwidth response at the respective resonating frequencies extends to 1 and 2 GHz below the -10dB line, showcasing the antenna\u27s excellent bandwidth characteristics. In terms of form factor, the proposed antenna is compact, measuring 16.2 x 12.8 x 0.501 mm. This compact size, coupled with the high gain and wide bandwidth at both operating bands, making the antenna well-suited for integration into 5G applications
Honey Adulteration Detection through Hyperspectral Imaging and Machine Learning
Get PDFIntroduction/Importance of Study:
The purity and authenticity of honey are paramount for ensuring consumer trust and maintaining the integrity of the honey industry. There is a pressing need for advanced and efficient detection methods to increase the prevalence of honey adulteration.
Novelty statement:
Our research provides a solution to the challenge of predicting the change in adulterated honey properties through hyperspectral imaging and advanced machine learning algorithms, filling a critical gap in existing methodologies.
Material and Method:
A publicly available dataset with spectral features, extracted through hyperspectral imaging, across different classes of honey and adulteration levels has been examined and various machine learning models were developed to identify honey adulteration concentration and type of honey. The dataset was balanced and a five-fold cross-validation technique was used to train the machine learning models.
Result and Discussion:
Random forest was found to perform better in three identified scenarios i.e. (a) type of honey (b) adulteration level (c) both (a, b); with a maximum average accuracy of 99.69% performing better than the one reported in the literature (95%). For both single-output and multiple-output ML models, the trend in feature importance was observed. The single model identifying the class of honey utilized low and mid-frequency spectra while the multi-model used mid-frequency spectrum only.
Concluding Remarks:
The proposed approach aims to provide an accurate and cost-effective solution to address the challenges associated with honey adulteration, contributing to the enhancement of honey quality assessment and consumer confidence
Investigating Threats to ICS and SCADA Systems Via Honeypot Data Analysis and SIEM
Get PDFSupervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) are crucial for managing essential infrastructure, but their exposure to the internet has made them vulnerable to cyber threats, which can lead to significant consequences. This study presents an innovative approach to investigating cyber threats to SCADA and ICS systems by combining open-source honeypot deployment, log analysis, and integration with open-source SIEM solutions to enhance threat detection capabilities and incident response. A Conpot honeypot was deployed in a containerized environment on a cloud platform and exposed to the internet to collect real-world threat data, which was then analyzed by the Wazuh SIEM solution and integrated with TheHive for security orchestration and automated response. The analysis of the honeypot logs and SIEM alerts revealed various types of attacks, including brute force login attempts, reconnaissance and vulnerability scanning, and unauthorized access attempts, originating from multiple countries and targeting different industrial protocols. The integration with TheHive enabled the creation of playbooks for automating response actions, such as blocking malicious IP addresses or isolating infected systems. The study demonstrates the effectiveness of this combined approach using open-source tools in protecting critical infrastructure and enhancing cybersecurity posture for SCADA and ICS systems
Smart Fire Safety: Real-Time Segmentation and Alerts Using Deep Learning
Get PDFFires are the major causes of property damage, injuries, and death worldwide. The ability to avoid or reduce the effects of fires depends on their early identification. The accuracy and responsiveness of conventional fire detection systems, such as smoke detectors and heat sensors, are constrained. Computer vision-based fire and smoke detection systems have been suggested as a replacement for conventional systems in recent years. To tackle the challenges a robust real-time framework has been proposed, whereby, images are taken from cameras and using a custom train YOLOv8 object segmentation model smoke and fires are localized in the image which are then fed to an expert system for alert generation. The expert system makes decisions on the fire status based on its size and growth across multiple frames. Furthermore, A new dataset was meticulously curated and annotated for the segmentation task, to assess the efficacy of the proposed system, comprehensive benchmarking was conducted on the proposed dataset using a suite of benchmarks. The proposed system achieved an mAP score of 74.9% on the benchmark dataset. Furthermore, it was observed that employing segmentation for localization as opposed to detection, resulted in system accuracy improvement. The system can immediately identify fires and smoke and send accurate alerts to emergency services
PERFORMANCE ANALYSIS OF A HYBRID RECOMMENDER SYSTEM
Get PDFIn the prevailing information age, human confrontation with extensive information makes it difficult to segregate the relevant content on the basis of choices and priorities. This gives rise to the need for effective recommendation systems that can be incorporated into distinct and diversified domains such as e-commerce, social media, and news media websites and applications. By giving suggestions, these recommender systems efficiently reduce huge information spaces and direct the users toward the items that best match their requirements and preferences. Hence, they play an important role in filtering out the relevant user-specific information. Based on the working principle, recommender systems can be classified into Content-Based Systems, Collaborative Filtering Systems, or P opularity-Based Systems. However, to cope with the problems of cold-start and plasticity that are associated with standalone recommender systems, hybrid recommendation systems are being introduced. This research is therefore focused on the development of a Weighted Hybrid Model that combines the scores of the three standalone recommender models in a linear fashion. The performance of the proposed hybrid model is tested against all three standalone models on an online News dataset. Using a Top-N accuracy metric, it is found that the accuracy of the weighted hybrid model is higher than the standalone Content-Based, Collaborative, and Popularity-Based models against the same dataset. An efficiency of 90% for the Hybrid model was achieved compared to the best-performing standalone model having an efficiency of 53%
NEUROSCAN: Revolutionizing Brain Tumor Detection Using Vision-Transformer
Get PDF Brain tumor detection is a pivotal component of neuroimaging, with significant implications for clinical diagnosis and patient care. In this study, we introduce an innovative deep-learning approach that leverages the cutting-edge Vision Transformer model, renowned for its ability to capture complex patterns and dependencies in images. Our dataset, consisting of 3000 images evenly split between tumor and non-tumor classes, serves as the foundation for our methodology. Employing Vision Transformer architecture, we processed high-resolution brain scans through patching and self-attention mechanisms. The model is trained through supervised learning to perform binary classification tasks. Our employed model achieved a high of 98.37% in tumor detection. While interpretability analysis was not explicitly performed, the inherent use of attention mechanisms in the Vision Transformer model suggests a focus on important brain regions and enhances its potential for prioritizing crucial information in brain tumor detection
Performance Evaluation of Fake News Detection Using Artificial Intelligence Techniques
Get PDFIntroduction/Importance of Study: As the proliferation of fake news poses significant challenges to traditional fact-checking methods, there is a growing need for robust and automated approaches to combat misinformation.
Novelty statement: This study presents a comprehensive evaluation of artificial models for fake news detection, offering insights into their effectiveness and applicability in addressing the contemporary issue of misinformation.
Material and Method: The research employs various artificial algorithms, including logistic regression, gradient boosting, decision trees, random forest, AdaBoost, passive aggressive classification, XGBoost, naive Bayes, and support vector machines (SVM), to train datasets and evaluate the performance of each model.
Result and Discussion: Through rigorous evaluation, the study finds that XGBoost and AdaBoost classifiers exhibit the highest accuracy rates of 99.83% and 99.77%, respectively, in detecting fake news. Decision Tree, Support Vector Machine, and Gradient Boosting classifiers also demonstrate commendable performance. Conversely, the Naive Bayes classifier exhibits the lowest accuracy, suggesting its limitations in fake news detection.
Concluding Remarks: This research underscores the significance of ensemble methods such as XGBoost and AdaBoost in effectively identifying fake news, laying the groundwork for future advancements in combatting misinformation
Analyzing Changes in Land Use and Cover (LULC) in the Quetta Valley Over Four Decades (1990-2020) Using Geospatial Techniques
Get PDFLULC changes have profoundly impacted environmental conditions, socio-economic development, and resource management in various regions. This study examines LULC changes in Quetta Valley, Baluchistan, Pakistan, over the past four decades (1990-2020) using geospatial techniques. The observed transformations primarily result from rapid urbanization, agricultural expansion, and population growth. LULC changes were analyzed using geospatial data from Landsat satellites, employing GIS and remote sensing (RS) methods. The findings indicate a steady increase in urban areas, with built-up land rising from 3.4% in 2000 to 7.17% in 2020, reflecting ongoing urbanization trends. This urban expansion has been accompanied by an increase in agricultural land, especially between 2010 and 2020, driven by the need to enhance food security. Vegetative cover has shown fluctuations, influenced by climatic variations and changing land management practices. Although barren land remains the predominant land cover type, its proportion has decreased slightly over time. These trends highlight the growing need for sustainable urban planning, effective agricultural management, conservation efforts, and integrated land management strategies. Policymakers should consider these LULC changes and their underlying causes when developing policies to ensure careful land use development and resource management, thereby promoting long-term environmental and socio-economic stability in Quetta Valley. This investigation provides insights into the dynamics of LULC changes and offers recommendations for sustainable land use planning in the region
Concrete Expansion: Urban Growth Estimation Through Geo informatics, A Case Study of Karachi
Get PDFKarachi, once the capital of Pakistan and currently the capital of Sindh province, is the country’s largest city and the 12th most populous city globally. It plays a crucial role in the national economy, contributing 60-70% of the country\u27s total revenue. However, the city faces significant challenges due to rapid urban sprawl—a prevalent issue in developing countries. The uncontrolled expansion of Karachi’s urban area, often described as the “concrete jungle,” presents considerable risks due to inadequate management and lack of long-term planning. This uncontrolled growth has led to increased population density, resource deficits, management challenges, and ecological pressures. Remote sensing and GIS technologies are now being employed for change detection and urban expansion analysis. Satellite data, including both current and historical images in various spatial resolutions, facilitate this analysis. In this study, change detection techniques are used to assess Karachi\u27s urban growth through historical maps, employing Landsat 7 ETM+ images from 2002 and Landsat 8 OLI images from 2022. This analysis, based on satellite images and measurements, reveals that Karachi\u27s population has grown at an annual rate of approximately 4%, driven by high natural increase and substantial migration from other regions. The population increased from 9.34 million in 1998 to 15 million in the 2017 census. Over the past 20 to 25 years, Karachi has expanded at a rate of about 15% annually, adding approximately 2 square kilometers per year to accommodate its growing population, resulting in a notably high population density