International Journal of Innovations in Science & Technology
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Contemporary Study of Machine Learning Algorithms for Traffic Density Estimation in Intelligent Transportation Systems
Get PDFIntelligent Transportation Systems (ITS) provides the state-of-the-art real time integration of vehicles and intelligent systems. Collectively, the prospective of the technologies have capability to communicate between system users, roads, and infrastructure. This study presents a comprehensive examination of many applications and implications of AI and ML in the development of an ITS. The primary objective of this is to effectively mitigate the traffic congestion and enhance road safety measures to prevent accidents. Subsequently, we examined different machine learning methodologies employed in the identification of road traffic based on vehicles and their junctions with the purpose of evading impediments, as well as forecasting real-time traffic patterns to attain intelligent and effective transportation systems. The exponential growth of the population inside the country has resulted in a corresponding rise in the utilization of vehicles and various modes of transportation, thereby it needs a contributing to the exacerbation of traffic congestion and the occurrence of road accidents. Therefore, there exists a need for intelligent transportation systems that possess the capability to offer the dependable transportation services while simultaneously upholding environmental standards to overcome the traffic congestions. Designing accurate models for predicting traffic density is a crucial task in the field of transportation systems. This study compares the ML models which are derived using a variety of machine-learning approaches. Supervised machine learning algorithms, including Naive Bayes, Markov models, KNN, linear regression, and SVM, and KNN are employed. The conclusion result suggests that the Markov model achieves the highest level of accuracy, of 98%. Implementation of ITS with Markov Model provides the best performance in resilient environment
Accountable and Trustworthy IoT Networks, Based on Blockchain
Get PDFThe term "Internet of Things" (IoT) refers to a situation in which intelligent things are linked to a network or the internet. IoT objects have become more prevalent over the past several years in many industries, and fields, and are now used in all facets of our life. The privacy of data is a crucial problem as the number of devices rises. Researchers in this discipline have employed a variety of strategies to address this issue. Regrettably, there is less accountability, data protection, and traceability with these solutions. In this study, a blockchain-based network architecture for accountability, privacy, and traceability is designed (TDA). Blockchain technologies are referred to as a distributed ledger of transaction records, which time-stamped information about a transaction\u27s lifetime. Persistence, decentralization, and audibility are three of blockchain\u27s key characteristics. The budget is reduced and efficiency is increased thanks to these characteristics. This study also discusses the performance of the suggested architecture in order to strengthen the TDA architecture
Identification of Real and Fake Reviews Written in Roman Urdu
Get PDFThe evolution of e-commerce has made reviews a crucial metric for judging the quality of online products or services. These reviews have a significant impact on the decision of the customer. Positive review catches more attraction while negative reviews impact sales of the product. Nowadays, deceptive reviews are being deliberately posted on e-commerce websites and social media stores to promote the product by illegal means. These reviews are sometimes posted in different local languages to build a fake virtual reputation among local customers. Thus, fake review detection is a wider area for ongoing research. This paper proposes several machine-learning approaches to detect fake reviews written in Roman Urdu. Furthermore, a comparative analysis of the performance of nine machine learning models on the given dataset is performed. The dataset is crawled from different e-commerce sites in Pakistan. The results show that the existing Support Vector Machine outperforms the rest of the models with an accuracy of 82%
Geo-Spatial Dynamics of Snow Cover and Hydro-Meteorological Parameters for Gilgit Balistan, Pakistan
Get PDFSnow cover dynamism is an important component of the UIB’s (Upper Indus Basin) hydrodynamics in the context of snow building up and reduction occurring seasonally. This study incorporates investigation into the dynamics of snow covers in relation to the hydrodynamics of the region. Data acquired through remotely sensed MODIS (Moderate Resolution Imaging Spectro-Radiometer) satellite for the duration of 20 years from 2000-2020, together with additional variables of hydrometeorology was utilized in the assessment of spatial and temporal fluctuation in snow-covered areas of Gilgit Baltistan (GB). The snow cover analysis was done temporally with an evaluation of its relationship with the hydro-meteorological variables through the application of Pearson correlation, Principal Component Analysis (PCA), and basin-wise zonal analysis. The investigation revealed that glacial ice covered an area of 25 to 50% and that the SCA (Snow Covered Area) may expand to 80 to 90% of the region on the amassment of snow in the snowy season. Trends from hydro-meteorological correlativity demonstrate a greatly considerable proportionality of R = 0.78, between the maximal and minimal temperature zones and river drains. However, no noticeable correlativity was found between precipitation and river drains (R = -0.04). For the region of Hunza, a statistically important negative correlativity was observed between the river drains and precipitation i.e., R = –0.83. The minus factor indicates an increase in river drainage with increased melting of snow covers due to high temperatures. This investigation infers a close association of river runoffs of the GB area with its snow cover dynamism. Discharge of rivers is a consequence of melting snow in the basin due to rising temperature and thus it speeds up at the beginning of summers mainly during April and May. Snow and ice start melting from the bottom and then reaches the top areas that have greater upstanding glacial mass
Impact of Parental Participation in Career Exploration Programs on High School Learner’s Self-Concept
Get PDFThis study aims to understand the extent to which parents influence their children\u27s career growth and how it influences their self-concept. In this study, we created a career exploration program in-house, which included both parents and their children. For the sensitization of the parents, two focus group discussion sessions were carried out, proceeded with a 52-item as a self-concept assessment. Using real-world case studies and logical debates, a total of 17 fathers were provided with information throughout the focus group session. After that, a posttest between group design was used to assess the impact of career development intervention on three different groups through highly reliable. A total of 62 adolescents were divided into three experimental group: 30 students were classified as alone, 15 students were partnered with their parents (n = 30), and 17 students were matched with 17 parents who had been educated to the issue through focus group discussions. The outcomes show that kids with informed parents did better than students alone and students with non-sensitised parents in terms of self-concept. Regression equation was found significant (F (1, 60) = 6.745, p=0.012), with an R2 of 0.101, which stated that student’s self-concepts holds 8.6% of the explained variability in Career exploration settings. The study\u27s findings will be useful to policy makers, educators, parents, students, and researchers
Historical Development of Urban Planning Theory: Review and Comparison of Theories in Urban Planning
Get PDFDiscipline of urban planning only developed around a century back with the first academic school at the University of Liverpool in 1909. However, the theory of this discipline is relatively older and might date to varying timelines around various parts of the world. However, modern urban planning discipline has got birth in the US and Western Europe. Early development in the theory of urban planning develops challenges for the cities. In response to such challenges, the planning approaches seem to adapt to the needs of emerging regimes evident from the high-scale urban renovation of Paris by Haussmann. This is called the Progressive Model of Planning wherein planners of the time struggled to deal with the challenges imparted by the Industrial Revolution through scientific and engineering-based knowledge. Early urban planning efforts were mostly anarchist movements that reacted to the social issues of the time and include Garden City, Radiant City, Broadacre, etc. This study sought to present these theoretical considerations with respect to certain development classifications in urban planning. Accordingly, the methodology of the research study comprises the following sections/segments for a better understanding of urban planning at different times:
Pre-History of Urban Planning
Foundational Years
Modernism (Rational Planning)
Post Modernism (Post war suburbia)
Current Era
In short, it is concluded that cities have emerged as a result of conscious decisions. Accordingly, looking into the planning theory requires due consideration of the planning approaches utilized over time. This perceives that planning theory is essentially the study of the decisions made from intuition and that is equally right because planning theory and practice development are in parallel
Prediction of Political Instability by Using Pre-Trained Neural Networks
Get PDFThis research aims to enhance and optimise the decision-making process in the political science domain by exploring the potential of machine learning. The aim was to create a pre-trained neural network to predict the political instability in any country (a prediction that aids decision-makers in handling government affairs and crisis prevention). We constructed four pre-trained neural networks, each tailored to a specific indicator (Human Development Index, Currency Strength Index, Tax to GDP Ratio, and Fragile States Index). These indicators are selected based on their strong correlation and how their concurrent performance impacts the political landscape of any country. The neural networks exhibited exceptional performance, achieving accuracy rates above 85%. The model built on the FSI demonstrated an astonishing accuracy of 99.67%, underscoring its potential for comprehensive assessments. The prospect envisions amalgamating the outputs of these pre-trained neural networks into a unified, deep-learning network, poised to yield collective decisions and recommend policy initiatives
Applications of AI in Health Services
Get PDFThe effects of artificial intelligence (AI)-based technologies on the healthcare sector are explored in this study. This research examined numerous practical uses of AI in healthcare, in addition to a comprehensive literature evaluation. Based on these findings, it appears that large hospitals are currently utilizing AI-enabled technologies to assist with patient diagnostic and treatment activities across a wide variety of ailments. Additionally, AI technologies are affecting the effectiveness of nursing and hospital management. Although AI is generally welcomed by the healthcare industry, its implementations present both utopian (new possibilities) and dystopian (overcoming obstacles) scenarios. To present a well-rounded picture of the usefulness of AI applications in healthcare, we address the specifics of these potential obstacles. The rapid development of AI and associated technologies will aid in the improvement of operational efficiency and the creation of new value for patients. However, to gain the benefits of technology like AI, comprehensive service transformation and operations planning are essential
ML/AI Based Flood Mapping in Swat Watershed Using Sentinel-I and Sentinel-II Data
Get PDFThis research uses Sentinel-1 and Sentinel-2 data for flood monitoring and mapping, with a focus on the accuracy and reliability of these remote sensing techniques in identifying flood inundation areas. The objectives of this study revolved around the accuracy and reliability of these techniques in detecting and mapping floodwaters. Water indices, namely NDWI and WRI, were utilized to extract floodwater areas and generate flood inundation maps. Additionally, flood extent maps were generated using Sentinel-1 data to complement the findings from Sentinel-2 data. The study implemented a multi-sensor and multi-index approach, considering both optical and radar data, to provide a comprehensive analysis of flood events. Image selection based on low cloud cover was employed to ensure high-quality and cloud-free imagery for accurate flood extent estimation. The selected images were processed using water indices, NDWI and WRI, which effectively captured the spatial distribution of floodwaters. The results revealed insights into the temporal variation and spatial distribution of flood extents, allowing for the identification of most affected areas. The analysis of Sentinel-2 imagery for July 2022 showcased a progressive intensification of the flood event, with the most affected regions being Charbagh, Mangora, Saidu Sharif, and Chakdara. The flood extents increased in August 2022, affecting areas such as Mangora, Saidu Sharif, Charbagh, Manglor, Barikot, and Chakdara. Furthermore, the flood extent in September 2022 indicated the persistence of floodwaters in areas with relatively fewer sloping surfaces. The integration of Sentinel-1 data provided enhanced comprehension into flood extents, particularly in challenging conditions such as high cloud cover or dense vegetation. The flood inundation maps generated from Sentinel-1 data complemented the findings from Sentinel-2 data, enhancing the accuracy and reliability of flood extent assessments. It is important to note that the high areas observed in the Sentinel-1 flood inundation maps are due to the mosaic of all the images acquired during the respective months. This approach includes all the water detected by Sentinel-1 from the 15 images, resulting in a larger affected area being shown. The flood inundation areas derived from Sentinel-1 data for July, August, and September were 129 km², 431 km², and 66 km², respectively. The analysis of Sentinel-1 data reveals that Kalam, Bahrain, and Madyan are highly vulnerable to intense flooding, as indicated by the high flood levels observed in these regions. The steep terrain, narrow valleys, and high rainfall intensity contribute to the heightened flood risk in these areas. The flood extents in Mangora, Saidu Sharif, and Barikot also reached significant levels, indicating widespread inundation in these regions. Overall, the study demonstrated the effectiveness of Sentinel-1 and Sentinel-2 data in flood monitoring and mapping. The multi-sensor and multi-index approach enhanced the reliability and robustness of the flood extent assessments, enabling better-informed decision-making processes for emergency response planning, resource allocation, and the implementation of effective flood mitigation strategies. The findings highlighted the importance of considering multiple indices and satellite data sources to obtain a comprehensive understanding of flood dynamics, while acknowledging the influence of cloud cover and other factors on the accuracy of the results
Salat Postures Detection Using a Hybrid Deep Learning Architecture
Get PDFSalat, a fundamental act of worship in Islam, is performed five times daily. It entails a specific set of postures and has both spiritual and bodily advantages. Many people, notably novices and the elderly, may trouble with maintaining proper posture and remembering the sequence. Resources, instruction, and practice assist in addressing these issues, emphasizing the need of prayer sincerity. Our contribution in the research is two-fold as we have developed a new dataset for Salat posture detection and further a hybrid model Media Pipe+3DCNN. Dataset is developed of 46 individuals performing each of the three compulsory Salat postures of Qayyam, Rukku and Sajdah and model was trained and tested with 14019 images. Our current research is a solution for correct posture detection which can be used for all ages. We examined the Media Pipe library design as a methodology, which leverages a multistep detector machine learning pipeline that has been proven to work in our research. Using a detector, the pipeline first locates the person\u27s region-of-interest (ROI) within the frame. The tracker then forecasts the pose landmarks and division mask in between the ROIs using the ROI cropped frame as input. A 3D convolutional neural network (3DCNN) was also utilized to extract features and classification from key-points retrieved from the Media Pipe architecture. With real-time evaluation, the newly built model provided 100% accuracy and a promising result. We analyzed different evaluation matrices such as Loss, Precision, Recall, F1-Score, and area under the curve (AUC) to give validation process authenticity; the results are 0.03, 1.00, 0.01, 0.99, 1.00 and 0.95. accordingly