IMPACT OF SOCIAL MEDIA APPS ON STUDENTSACADEMIC PERFORMANCE
Abstract: The main purpose of this investigation is to decide the positive impact of social media apps on the academic performance of students including Females and Males of Universities and Colleges. Based on arbitrary sampling, a sample of 1500 students was chosen. A Survey-based Questionnaire was utilized as amajor source for information collection. The 100% poll got back from the respondents on which expressive statistics applied for information investigation. Results demonstrate that the impact of social media apps can be positive as this examination decisively resolved the genuine impact of social media apps. In the later time, it encourages the future career of students in Institutions. The Social Media Apps(SMA) like Facebook, Whatsapp, YouTube, Twitter, and other social media apps capture the consideration of students including females and males for study and influence decidedly their scholarly grade considerations.
SARFRAZ NAWAZ Et Al., April. 2022,Volume 41,Pages 177-216, doi: 10.17605/OSF.IO/7KVF6
HAND-WRITTEN DIGITS RECOGNITION USING MISCELLANEOUS MACHINE LEARNING AND DEEP LEARNING ALGORITHMS
Abstract: Identification of Hand-written digits is a rational key point in pattern identification applications. There are many uses of hand-written digits identification like mail sorting in postal, cheques processing in the banks, data entry through forms, etc. The key to the issue lies in the expertise to grow a well-organized algorithm that can accept hand-written numbers and which are submitted by end-users by the scanners, tablets, and other digital devices. This paper gives a viewpoint to handwritten numbers recognition constructed on LANGUAGE SKILLS DIGITAL SKILLS ADDITIONAL INFORMATION PUBLICATIONS 3 / 7 machine learning models, and deep learning models and shows the outcomes in the shape of accuracy. The primary objective of this paper is to guarantee powerful and dependable methodologies for the acknowledgment of handwritten numbers using machine learning and deep learning algorithms. Several machine learning algorithms such as Decision Tree (DT), Naïve Bayesian (NB) classifier, Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), and deep learning algorithms such as Convolutional Neural Network (CNN), AlexNet, and Multilayer Perceptron (MLP) have been used for recognition of hand-written digits in Jupyter Notebook and Matlab.Through some features extraction, and different experiments and analysis of Machine Learning Algorithms (MLA) and Deep Learning Algorithms (DLA), the accuracy of deep learning algorithms is better than the machine learning algorithms. SARFRAZ NAWAZ Et Al., May. 2022,Volume 41,Pages 57-85, doi: 10.17605/OSF.IO/WPHJM.
Link https://jilindaxuexuebao.com/details.php?id=DOI:10.17605/OSF.IO/WPHJM
Abstract: In this paper, the researchers have empirically used the various Machine Learning (ML) and Deep Learning (DL) Algorithms and analyze the findings of different Machine Learning and Deep Learning algorithms on the very well-known Wisconsin Diagnostic Breast Cancer Data-Set (WDBC). This study assessed the degree of their capacity to accurately order the sample images as "malignant" or "benign". The separate utilizing of these algorithms was decided on the grounds of different assessment measurements as accuracy is the main factors of datasets. From the trial results, we rational that the deep learning approaches have given preferable outcomes on assessment grounds over the machine learning algorithms. In quantitative terms, CNN performed most reliably among every one of the considered methodologies for the given breast cancer dataset with an accuracy of CNN Deep Learning Model is 99.48 % and MLP 99.45% individually. The ML algorithm SVM has the betters testing accuracy 97.13% and 98.36% training accuracy. In the consequences of finding breast cancer can be predicted on early basis using the Machine Learning and or Deep Learning Models effectively and efficiently. Early detection of breast cancer (BC) will treat well and save many breast cancer patients. As a result, the BC patient’s rate and death rate can be reduced. Keywords: Breast Cancer detection, machine learning, deep learning algorithms, classifiers, cancer prediction, convolutional neural network, AlexNet, features extraction, Wisconsin dataset. Benign and malignant images.
SARFRAZ NAWAZ Et Al., Jul. 2022,Volume 55,Pages 102-120, doi: 10.17605/OSF.IO/9FYTS.
Link https://youtu.be/fdB7iyPewy4
INTERNET OF THINGS (IOT) SECURITY AND PRIVACY
Abstract The Internet of Things (IoT) contains intelligent objects that are comprised with various types of sensors, networks, electronics devices and process technologies that are integrated and work altogether, where the effective and intelligent services are provided to the users. Smart Cities have been proposed as a solution to urban problems, where the people of that city can live with more comfortable by using the innovative technology. The purpose of this paper is to provide thorough analysis of IoT technology, with major focus on privacy and security risks, attacks surfaces, vulnerabilities. Although the researchers are conducting studies on the services offered and challenges of IoT to make the Smart Cities effective. There is still some space between theory and practical of using Information and Communication Technology, (ICT). In order to highlight the basic requirements and concerns of users in the area of security and privacy, the requirements and problems of IoT users have been defined. In order to conduct this IoT privacy and security study, a systematic literature review is conducted using electronic databases and other sources to search for all articles that met specific criteria, enter information about each research into a personal database, and then create summary tables. Body of research. Consequently, the paper summarizes recent advances in IoT privacy and security, highlights outstanding issues, and suggests topics for further research. Keywords: IoT, ICT, Internet of Things, Information Security, Privacy, Smart Cities, Threats, IoT Attacks, Data Security.
SARFRAZ NAWAZ Et Al., March. 2023,Volume 56,Pages 23-38, doi: 10.17605/OSF.IO/T8YCW. Link https://youtu.be/Ib37XKH8Drg