International Journal of Innovations in Science & Technology 2022-11-11T11:56:28-05:00 Prof Dr. Ali Iqtedar Mirza Open Journal Systems <p>International Journal of Innovations in Science and Technology (ISSN 2618-1630) is an international peer reviewed and "Open Access" multidisciplinary journal designed to explore advances in various disciplines of Science and Technology. Researcher can submit their field observations and the results of laboratory experiments to IJIST within "<a href="" target="_blank" rel="noopener">Aims and Scopes</a>" of the journal. Journal publish issues quarterly. </p> <ul> <li><strong>Rapid Publication:</strong> manuscripts are trasnparently reviewed by internaltional and local reviewers and the first decision is provided to authors within 10-15 Days. </li> <li><span style="font-weight: bolder;">High Visibility:</span> indexed in HEC, MDPI, REPEC and other <a href="" target="_blank" rel="noopener">database</a>......</li> <li><strong>Recognition of Reviewers: </strong>Reviewers providing timely decisions, thorough peer-review reports are acknowledged with a discount in <a href="" target="_blank" rel="noopener">Article Processing Charges.</a></li> </ul> Quantum Key Distribution Protocols for Secure Communication 2022-08-18T06:22:09-04:00 Mahira Najeeb Ammar Masood Adnan Fazil <p>Data protection and information security have been the essence of communication in today's digital era. Authentication and secrecy of secure communication are achieved using key-based cryptographic primitives; the security of which significantly relies upon the underlying computationally complex mathematics. Moreover, these existing cryptographic primitives are considered to be non-deterministic on the basis of the existing computational capabilities. However, the considerable advancements in the development of quantum computers have significantly enhanced parallel computations; thereby, posing a great threat to these existing encryption primitives. Thus, in the future, the physical manifestation of a large successful quantum computer is likely to break all the existing public-key encryption algorithms in no time. This has led to a remarkable surge of interest in propelling quantum mechanics into existence; subsequently, leading cryptographers to research various viable domains to offer quantum-resistant secure communications. Resultantly, quantum cryptography/quantum key distribution has emerged as a futuristic replacement for classical cryptography as it offers unconditionally secure communication along with the inherent detection of any unintended user. Thus, keeping in view the significance of this relatively newer domain of cryptography this research focuses on presenting a consolidated review of the various Quantum Key Distribution (QKD) protocols. A comparative analysis of the working mechanism of the prominent QKD protocols is presented along with an overview of the various emerging trends that have been proposed to optimize the implementational efficiency of the BB84 protocol.</p> 2022-10-30T00:00:00-04:00 Copyright (c) 2022 International Journal of Innovations in Science & Technology Analysis of Job Failure Prediction in a Cloud Environment by Applying Machine Learning Techniques 2022-07-15T02:57:46-04:00 Faraz Bashir Farrukh Zeeshan Khan <p>Cloud Services are the on-demand availability of resources like storage, data, and compute power. Nowadays, cloud computing and storage systems are continuing to expand, there is an imperative requirement for CSP (cloud service providers) to ensure a reliable and consistent supply of resources to users and businesses in case of any failure. Consequently, the large cloud service providers are concentrating on mitigating any failures that transpire in a cloud system environment. In this research work, we examined the bit brains dataset for the job failure prediction which keeps traces of 3 years of cloud system VMs. The dataset contains data about the resources used in a cloud environment. We proposed the performance of two machine learning algorithms which are Logistic-Regression and KNN. The performance of these ML algorithms has been assessed using cross-validation. KNN and Logistic Regression give the optimal results with an accuracy of 99% and 95%. Our research study shows that using KNN and Logistic Regression increases the detection accuracy of job failures and will relieve cloud-service providers from diminishing future failures in cloud resources. Thus, we believe our approach is feasible and can be transformed to apply in an existing cloud environment.</p> 2022-10-30T00:00:00-04:00 Copyright (c) 2022 50sea Enterprise Network Infrastructure Malicious Activity Analysis 2022-09-27T02:28:34-04:00 Muhammad Shujat Ali Ahsan Abbas Abdullah Faisal Anza Riaz Imran Siddiq <p>Inter and intra-network connectivity have become a useful resource for accessibility and flexibility of data for different organizations. Online services are increasing day by day, everything is available online, it generates a huge amount of data, that require cyber security revolves for ensuring secure interconnectivity between devices. Because of an exponential increase in internet users and cyber-attacks, the data security and credibility of various organizations is on stake. In the continued development of the threat environment, cyber security experts deal with numerous threats on daily basis. As multiple attacks on computer networks and systems are becoming stronger each day therefore current security tools are often inadequate to resolve issues relating to unauthorized users, reliability, and reliable network security. To maintain a safe environment, Intrusion-Detection Mechanisms (IDS) enabled to control device functions and detect intrusions should typically be used to supplement with other protection strategies; for which conventional security methods are inadequate. Actual users expect their requested information to be processed in real-time, while malicious traffic needs to be mitigated just as quickly as possible. As traffic increases, this problem becomes more complex. This paper contributes a detailed analysis of network packets to find anomaly detection based on the UNSW NB 15 dataset and investigate the the difference between IP packet behavior for both malicious and legitimate packets. Besides we acquaint with new methodologies to illuminate and appraise the network attack in a very proficient way using different machine learning algorithms which will accomplish locating the malicious traffic in the least execution time with precision.</p> 2022-10-26T00:00:00-04:00 Copyright (c) 2022 50SEA A Qualified review on ML and DL algorithms for Bearing Fault Diagnosis 2022-09-19T14:04:33-04:00 Asma Bibi Syed Bushra Naz Shahnawaz Talpur Shahzad Hyder Yusrah Bablani <p>Moving machinery is the backbone of socio-economic development. The use of machines help in increasing the production of everyday used items, and tools, that generate electricity and mechanical energy, and provides easy and fast transportation and help by saving human efforts, energy, and time. The mechanical industry is totally dependent on the bearing and it is considered as bread and butter of the system. Bearing failure is about 40% of the total failures of induction motors which is why it is a crucial challenge to predict the failure and helps prevent future downtime events through maintenance schedules with the latest techniques and tools of. This paper presents a review of how DL techniques and algorithms outsmarted ML for bearing fault detection and diagnosis and summarizes the accuracy results generated by most common DL algorithms over classical ML algorithms.Additionally this paper reasons different criteria for which DL algorithms have been proved efficient for building productive model in the field of bearing fault detection. Furthermore, some of the most famous datasets by different universities have been discussed and accuracy results are provided by reviewing algorithms on the CWRU dataset by different researchers and comparison chart is listed in the results section.</p> 2022-10-26T00:00:00-04:00 Copyright (c) 2022 50SEA Soil Classification & Prediction of Crop Status with Supervised Learning Algorithm: Random Forest 2022-10-05T01:34:32-04:00 Bakhtawer Bakhtawer Bushra Naz Naseer U Din Waqar Ahmed <p>Crop Management System (CMS) was developed in an Ionic framework with a Real-Time Firebase database for loop backing and decision support. The main two features were; Soil classification where the soil was classified based on temperature, humidity, and soil properties such as soil moisture, soil nutrients, and soil PH level using Random Forest Algorithm. By Bootstrap method using Random Forest, samples from the dataset were selected &amp; then classification trees was generated. The other feature was crop precision where the condition of the crop was and examined using temperature, humidity, soil moisture, soil PH levels, and soil nutrients (N, P, K). IoT device was used to fetch data from the field and then compare with already stored ideal values, suitable for optimal yield, in CMS database then process using the application to suggest the crop for cultivation and to optimize the usage of water and fertilizers. Currently, we classify the soil using Random Forest Algorithm &amp; suggest the suitable crop for the classified type of soil &amp; also measure the soil moisture and soil nutrients of agricultural field Acre based on the reading results we are suggesting the crop to is cultivated and pre-requisite which would be needed in future. The proposed method gives an accuracy of 96.5% as compared to existing methods of Artificial Neural Networks and Support Vector Machines.</p> 2022-10-27T00:00:00-04:00 Copyright (c) 2022 50sea Arsenic (v) Adsorption by Using Synthesized Iron Oxide Nanoparticles (Fe2O3-NPs) and Aluminum Oxide Nanoparticles (Al2O3-NPs) 2022-09-11T09:37:57-04:00 Muhammad Tahir Turi Ma Wei Ittehad Hussain Javid Hussain <p>Arsenic, is one of the most harmful elements to human health that continuously causes a threat to the world. Arsenic is found in combined form in rocks under the earth's surface and when it dissolves, it contaminates groundwater. The current research synthesized iron oxide nanoparticles (Fe<sub>2</sub>O<sub>3</sub>-NPs) and aluminum oxide nanoparticles (Al<sub>2</sub>O<sub>3</sub>-NPs) for removal of arsenic (As) (˅) from an aqueous medium and characterized the synthesized material by different analytical techniques such as FT-IR spectroscopy and XRD spectroscopy. The results show successful synthesis of Fe<sub>2</sub>O<sub>3</sub>-NPs and Al<sub>2</sub>O<sub>3</sub>-NPs. Furthermore, the synthesized material was used as an adsorbent for extraction of as (V) from water. The effect of different parameters such as pH, temperature, contact time, and adsorbent dose on the adsorption process was investigated. The adsorption efficiency was determined by Fe<sub>2</sub>O<sub>3</sub>-NPs at about 20 mg/g and Al<sub>2</sub>O<sub>3</sub>-NPs at 19.5 mg/g. The quantitative removal of as (V) from industrial water required a minimum amount (0.2 g) of Fe<sub>2</sub>O<sub>3</sub>-NPs and Al<sub>2</sub>O<sub>3</sub>-NPs. various kinetic and isotherms were investigated in the current study. The result showed that the obtained data for Fe<sub>2</sub>O<sub>3</sub>-NPs was more fitted to Pseudo second order kinetic and Freundlich equation, while for Al<sub>2</sub>O<sub>3</sub>-NPs the data was more fitted to Pseudo second order kinetic and Elovich model equation, which confirms the interaction among as (V) and adsorbents. Thermodynamic parameters were also investigated which shows the process is spontaneous and endothermic. This model was used to estimate the site energy distribution for each adsorbent. Thermodynamic parameters were also investigated which shows the non-spontaneous and endothermic nature of the adsorbent. According to the results of the analysis of the approximate site energy distribution, adding Fe<sub>2</sub>O<sub>3</sub> and Al<sub>2</sub>O<sub>3</sub>-NPs to arsenic decreased the area under the frequency distribution curve of the sorption site energies, which in turn decreased the number of sorption sites that were open to arsenic. This might be explained by the hydrophobic interaction between synthesized materials and arsenic being reduced due to the blocking of the Fe<sub>2</sub>O<sub>3</sub> and Al<sub>2</sub>O<sub>3</sub>-NPs hydrophobic surface.</p> 2022-10-30T00:00:00-04:00 Copyright (c) 2022 50sea Role of agile methodologies for ensuring quality in complex systems: A systematic literature review 2022-09-03T09:27:00-04:00 Ihsan ullah Jaweria Awan Fatima Gillani Iqra Shahzad <p>In software development, the selection of a software process model set the base for the success of a software product. An inappropriate selection may lead to a delay in project release, introduce defects and make the project difficult to update. This lack of quality characteristics may lead to the risk of losing customer expectations as well as the failure of the project itself. In the case of complex systems, the problems become more severe. To meet such expectations, agile methodologies are used to ensure quality in software and meet customers’ expectations. There is currently no literature that gives insights into the role of agile methodologies in ensuring quality in complex systems. The purpose of this paper is to evaluate the effectiveness and the impact of agile methodology in achieving the quality of complex systems.</p> <p>For this, we perform a Systematic Literature Review (SLR) and define a review protocol. By performing a thorough search and screening, we selected 39 papers related to agile methods and complex systems. Our analysis shows that complex systems have various requirements of quality attributes some of the complex systems mainly focus on security, reliability, and efficiency whereas other emphasizes safety, response time, and maintainability. Our analysis also shows that agile methodologies are widely used for the development of complex systems because ensuring the quality requirements of complex systems is not possible with the use of traditional methods of software development.</p> 2022-11-05T00:00:00-04:00 Copyright (c) 2022 50sea Meme Detection of Journalists from Social Media by Using Data Mining Techniques 2022-09-20T03:17:34-04:00 Sajawal Khan Adeel Ashraf Muhammad Shoaib Muhammad Iftikhar Imran Siddiq Muhammad Dawood Khan Abdullah Faisal <p>With regard to today's social media networks, memes have become central character where millions of memes are shared per second on different social media networks. The detection of memes is a very concentrated and demanding subject in the current era. Today's social media (What's App, Twitter, and Facebook) is widespread around the world. People in all countries use these networks and spend their plentiful time on daily basis. As social media has an enormous amount of data overall in the world. Meme detection from media networks can be done by using their authenticated APIs. For this analysis we used some opinion mining techniques and sentiment analysis like statistical descriptive and content analysis. In our society, it is the better way to analyze about any journalist because social media can provide very huge amounts of data about any journalist however the authenticity is compromised, what is true or false, no one bother to check. Anyone can make approximate correct perceptions by using sentiment analysis and text mining techniques. It will provide highly wanted and hidden characteristics and perceptions for searchers and demanding people about journalists. Finally use for sentiment analysis by using Python.</p> 2022-11-07T00:00:00-05:00 Copyright (c) 2022 50sea The Significance of Cognitive Distortions and Risk Factors Due to Android Games Addictions for Adults 2022-08-30T05:36:02-04:00 Imran Siddiq Imran Hussain Anza Riaz Abdullah Faisal Ameer Hamza <p><strong>Background:</strong></p> <p>The primary purpose of this study is to find out the Significance of Cognitive Distortions and Risk Factors due to Android Game Addiction for Adults.</p> <p><strong>Methods:</strong></p> <p>The research population includes three main parameters Physical Effects, Mental Effects, and, Cognitive Effects each part contains 10 questions. The sample comprises 200 school students (male=150, females=50) between the ages of 8 to 20 years recruited from schools in Lahore, Pakistan. A total of 200 copies of the Questionnaire are distributed among the students. Only 180 copies are received and filled so the total response rate of the data collection was 90%. The SPSS tool was used to analyze the results of the Questionnaire. It aims to investigate the impacts that Android Games have on adults. As most of the researchers have done Quantitative research in this area, so I decided to conduct both Quantitative and Qualitative research designs which can help us get a deeper understanding of Android Game Addiction.</p> <p><strong>Findings:</strong></p> <p>By going through the results, it is revealed that the all above-mentioned three main parameters are found positive in adults. According to the range of the frequency, it is concluded that all three factors are in the medium range not more harmful.</p> <p>There was a strong positive association between the addictions of students to Android mobile games and their physical and mental well-being in terms of physical, mental, and cognitive health.</p> 2022-11-08T00:00:00-05:00 Copyright (c) 2022 50sea