Journal Cover
University of Sindh Journal of Information and Communication Technology
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2521-5582 - ISSN (Online) 2523-1235
Published by U of Sindh Homepage  [8 journals]
  • Simulation and Signal Transmission of Through-Wall Detection/Imaging of
           Metallic Target Using UWB Monostatic Radar

    • Authors: Sajjad Bhatti
      Pages: 38 - 46
      Abstract: This paper introduces a simulation and signal transmission approach for through-wall target detection/imaging using a monostatic UWB radar. The investigation encompasses the detection and imaging of metallic targets through the application of signal processing techniques.  For this purpose, a UWB-balanced antipodal Vivaldi antenna (BAVA) with high gain and standard signal transmission has been designed. Through wall simulation model using monostatic RADAR configuration has been developed in CSTMWS. Through through-wall simulation model has the capability of producing received signals in the time domain for through-wall target detection that are further processed to obtain 2D images. MATLAB programming techniques were employed to process the simulated data at various scan positions. The 2D image of the objects was produced based on the respective scanning positions and distances.
      PubDate: 2024-05-26
      Issue No: Vol. 7, No. 2 (2024)
       
  • Evaluating Diabetes Detection Methods: A Multilinear Regression Approach
           vs. Other Machine Learning Classifiers

    • Authors: Hasnain Hyder, Khawaja Haider Ali, Dr. Abdul Aziz, Lubina Iram
      Pages: 47 - 56
      Abstract: Machine learning has become an important tool in many fields, including healthcare. In this research paper, we aim to implement diabetes dataset in multi-linear regression and compare its performance with different classifiers of machine learning. The novelty of this research lies in the evaluation of the diabetes dataset using multilinear regression and subsequent comparison of its performance against several other classifiers, including Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Support Vector Machines (SVM). This comparative analysis aims to assess and benchmark their respective performances.  etc. Our results show that multi-linear regression achieved an accuracy of 80.5%,  However, other classifiers such as random forest, and logistic regression outperformed linear regression, achieving accuracy scores of 81.4% and  81.25%, respectively. Furthermore, we observed that decision tree, KNN, and SVM, which are often used for classification tasks, did not perform well on this dataset, achieving an accuracy of only 78.7%, 80.5%, and 79.6% respectively. This suggests that the model's performance can be greatly impacted by the classifier selection. Our findings suggest that linear regression can be used for predicting diabetes, other classifiers such as random forest, and logistic regression are more effective for this dataset. To choose the best classifier for a given job, it is crucial to assess and contrast the performance of several classifiers.
      PubDate: 2024-05-26
      Issue No: Vol. 7, No. 2 (2024)
       
  • Profiling Reviewer Mobility Behavior Using Yelp Reviews

    • Authors: Muhammad Usman Zafar, Muhammad Bilal, Nadia Malik
      Pages: 57 - 64
      Abstract: Social media data has become a popular source for uncovering hidden patterns in user mobility. However, the volume of online customer reviews has increased enormously which makes it difficult for individuals and businesses to identify and analyze these patterns. Moreover, very few studies exist that explore the mobility patterns of reviewers. Therefore, this study aims to profile the mobility of reviewers to identify hidden patterns. The data of 1217 reviewers and 139,187 reviews from Yelp is used in this study. Firstly, distance-based mobility profiles which include the average distance and total distance of reviewers are created. The correlation analysis showed that distance-based measures strongly correlate with the number of reviews written by a reviewer, the number of cities, and the number of states visited by the reviewer compared to other features. Clustering is done using k-means and Density-based spatial clustering of applications with noise (DBSCAN) to analyze the different nature of reviewers which showed that reviewers can be grouped into two categories that are “less traveler” and “more traveler”. A comparison of reviewers that have visited the same number of businesses reveals that their mobility patterns can vary significantly. Finally, the classification of reviewers into less and more travelers is done using various machine learning algorithms. Random forest (RF) achieved Area Under the Curve (AUC) of 0.865, which is comparatively better than other algorithms. The feature importance calculated using RF showed that review count, city, and state are more important features compared to others.  
      PubDate: 2024-05-26
      Issue No: Vol. 7, No. 2 (2024)
       
  • A Study on Agile Retrospective Practices in the Software Industry of
           Pakistan: An Examination of Real-World Applications

    • Authors: Ali Raza Raza, Jaweria Kanwal, Maryam Imtiaz Malik, Fatima Gillani
      Pages: 65 - 71
      Abstract: In every industry manual working process is ending and moving to the software technology. Rapid change in the technology software development organization need to update and improve their development process. In software programming techniques agile practices focus on the client comfort and responsive to change. Retrospective practices are important activities for process improvement in Agile software development as they increase the collaboration between the team members. With the help of these retrospective practices, challenges and problem in the development process are identified and suitable solutions are provided. Team velocity are managed according to the progress of the previous sprint. Retrospective practices are initially difficult for new team and take about 3-5 meeting to make mindset on the retrospective meeting. In this research a case study is conducted to analyze whether retrospective practices are followed by the Pakistani software industry. Our analysis shows that retrospective practices are followed by the Pakistani software industry for process improvement but they have both positive and negative impact on the software quality and improvement of the team. In Pakistani industry, various retrospective practices are followed according to software quality requirements but mostly teams are satisfied by the practices followed in their teams.
      PubDate: 2024-05-26
      Issue No: Vol. 7, No. 2 (2024)
       
  • Smart Overcurrent Relay Management in Power Monitoring Systems with
           Raspberry Pi

    • Authors: Syed Sheraz Ul Hasan Mohani , Muhammad Shariq Zaheer , Mohsin Hanif , Syed Hassan Ali , Abdul Basit Abro
      Pages: 72 - 77
      Abstract: In light of the escalating complexity and ever-changing dynamics within power systems, the necessity for resilient and effective protection schemes is paramount to uphold grid stability and reliability. Among these, Overcurrent Relays (OCRs) stand as pivotal components, tasked with the crucial role of detecting and mitigating faults within power networks. This research presents an innovative approach towards enhancing OCRs utilizing the Raspberry Pi Pico, a microcontroller board, in conjunction with adaptive strategies. The primary aim is to augment the performance and versatility of OCRs, ensuring their adeptness in promptly and accurately addressing varied fault scenarios encountered within power systems. Through the integration of adaptive strategies and the utilization of Raspberry Pi Pico, this paper seeks to contribute towards advancing the efficacy and adaptability of OCRs, thereby bolstering the overall resilience and dependability of power system protection mechanisms. The proposed approach for optimizing OCRs is evaluated through experimentation and analysis, demonstrating improved performance and adaptability compared to traditional methods. Results indicate enhanced fault detection and mitigation capabilities, underscoring the potential of the Raspberry Pi Pico-based OCR system in addressing the evolving challenges within modern power systems. The findings of this research contribute valuable insights to the field of power system protection, paving the way for more resilient and dependable grid operation.
      PubDate: 2024-05-26
      Issue No: Vol. 7, No. 2 (2024)
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 34.204.198.73
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-
JournalTOCs
 
 

Publisher: U of Sindh   (Total: 8 journals)   [Sort by number of followers]

Showing 1 - 5 of 5 Journals sorted alphabetically
Government : Annual Research J. of Political Science     Open Access   (Followers: 1)
Intl. Research J. of Arts & Humanities     Open Access   (Followers: 2)
Sindh University J. of Education     Open Access   (Followers: 1)
The Women : Annual Research J. of Gender Studies     Open Access   (Followers: 7)
University of Sindh J. of Information and Communication Technology     Open Access  
Similar Journals
Similar Journals
A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  All
HOME > Browse the 3359 Publishers covered by JournalTOCs 10 11 12 13 14 15 16 17  
PublisherTotal Journals
10 11 12 13 14 15 16 17  
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 34.204.198.73
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-