The Asian Bulletin of Big Data Management最新文献

{"title":"Efficient PV Systems for Karachi: Environmental Benefits and Performance Analysis","authors":"Jamil ahmed Memon, Sajid Yasin, Shahzad Karim Khawer, Syed Zain Mir, Abdul Khaliq, Atif Aziz","doi":"10.62019/abbdm.v4i02.177","DOIUrl":"https://doi.org/10.62019/abbdm.v4i02.177","url":null,"abstract":"\u0000\u0000\u0000\u0000This study makes a strong argument for Karachi, Pakistan to install solar photovoltaic (PV) systems in light of the declining availability of traditional energy sources and growing environmental concerns. By utilizing Karachi's enormous solar potential, the study presents an effective, environmentally friendly, and sustainable photovoltaic system tailored to the area's particular geographic coordinates. The recommended approach provides significant environmental benefits with an ambient temperature of 26.85°C and a daily solar irradiation of 5.184 kWh/m2. With the Pvsyst simulation, the study carefully considers a range of power losses, such as soiling, inefficiencies of the modules, losses from the inverter, and temperature-related drops. This results in a performance ratio (PR) loss of only -0.41% and a remarkable yearly energy output of 1678 MWh. The research goes further into the nuances of output ratios and various forms of power loss in order to examine the viability of a 1 MW solar PV power project. By using the PVSYST toolset to forecast the system's performance, it is possible to reduce carbon dioxide emissions by 5317.367 tons over a ten-year period, which is the same as the impact of planting up to 244,598.822 trees. The system's cost-effectiveness is highlighted by the financial analysis, which projects a yearly maintenance cost of $2,500 after accounting for a 2% inflation rate. This study establishes a standard for next solar projects in addition to proving that solar energy is feasible in Karachi. It emphasizes the critical role of performance analysis in the design and maintenance of renewable energy systems, advocating for a strategic shift towards sustainable development and a greener future.\u0000\u0000\u0000\u0000","PeriodicalId":518249,"journal":{"name":"The Asian Bulletin of Big Data Management","volume":"07 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hybrid Deep Learning Based Fake News Detection System Using Temporal Features","authors":"Ali Raza, Raja Sher Afgun Usmani, Shafiq Ur Rehman Khan","doi":"10.62019/abbdm.v4i02.179","DOIUrl":"https://doi.org/10.62019/abbdm.v4i02.179","url":null,"abstract":"Detecting fake news and missing information is gaining popularity, especially after social media and online news platforms advancements. Social media is the main and speediest source of fake news propagation, whereas online news websites contribute to fake news dissipation. In this study, we propose a framework to detect fake news using the temporal features of text and consider user feedback to determine whether the news is fake or not. In recent studies, the temporal features in text documents gain valuable consideration from Natural Language Processing and user feedback and only try to classify the textual data as fake or true. This research article indicates the impact of recurring and non-recurring events on fake and true news. We use different models such as LSTM, BERT, and CNN- BiLSTM to investigate, and it is concluded that from BERT, we get better results, and 70% of true news is recurring, and the rest of 30% is non-recurring.","PeriodicalId":518249,"journal":{"name":"The Asian Bulletin of Big Data Management","volume":"121 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Driven Decision-Making: Accurate Customer Churn Prediction with Cat-Boost","authors":"Bismah Hasan, S. Zubair, Abdul Shaikh, Ghalib Khaliq, Nadeem, Shahrukh Ali Shaikh","doi":"10.62019/abbdm.v4i02.175","DOIUrl":"https://doi.org/10.62019/abbdm.v4i02.175","url":null,"abstract":"\u0000\u0000\u0000\u0000In the cutthroat telecommunications industry, customer churn poses a significant threat to profitability and brand reputation. To combat this, we propose a data-driven approach to predict churn, leveraging advanced Machine Learning (ML) algorithms on a comprehensive dataset of 7043 customer records with 21 features sourced from the IEEE website. By applying one-hot encoding to transform categorical data, we optimize ML algorithm performance. Our study pits three ML algorithms against each other: Cat Boost, Stochastic Gradient Boost, and Extreme Gradient Boost. The results are striking, with Cat Boost incipient as the top performer, boasting an impressive 94% accuracy, 90% precision, 97% recall, 94% F1-score, and an AUC of 0.94. These discoveries emphasize Cat Boost's extraordinary ability to predict churn, empowering telecom companies to proactively prevent attrition, target high-risk customers, and make data-driven decisions to make the most of profits. In today's speedily developing technical landscape, correctly predicting churn is critical to meeting the ever-changing potential of customers. Our research marks a noteworthy innovation in addressing churn trials, flooring the way for long-term accomplishment in the telecom industry.\u0000\u0000\u0000\u0000","PeriodicalId":518249,"journal":{"name":"The Asian Bulletin of Big Data Management","volume":" 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the Impact of Monetory Policy Shocks on Output, Inflation and Exchange Rate Dynamics of Pakistan","authors":"H. Wagan, Iftikhar Ahmed, G. H. Wagan, J. K. Bajkani, Rani Abro, Bushra Gul","doi":"10.62019/abbdm.v4i02.171","DOIUrl":"https://doi.org/10.62019/abbdm.v4i02.171","url":null,"abstract":"The major objective of this study is to analyse that how monetary policy of Pakistan had impact on output, inflation and exchange rate fluctuations during Global Financial Crisis 2008-2009 in Pakistan. Quarterly data was used for this purpose. Data spans from 1990Q1 to 2022Q4. Time varying parameter – vector auto regression model (TVP-VAR) of Jouchi Nakajima (2011) has been used for analysis. Through this model we selected the time horizons such as 2008, 2009 and 2015 mainly to study the impact of monetary policy on different selected variables. Our empirical finding indicates that monetary policy shock plays an important role in the economic dynamics of Pakistan. The impulse response of inflation is negative to tight monetary policy shock (εi↑→ p), this is in accordance with the standard economic theory. In case of Pakistan, after 1990 there is declining trend in volatility of output and a considerable reduction in business cycle fluctuations. Policy rate is more volatile during 1998. After 2000 there is decline in volatility of policy rate but in 2008-09 there is rise in interest rate because of Global Financial Crises in 2007 and 2008. The response of policy rate to inflation shock is in rising trend in 2007 and 2008. The inflation is increasing as a response to positive output shocks after 12th quarter this rising trend is also evident during 2008 where the impact of output on inflation is long term.","PeriodicalId":518249,"journal":{"name":"The Asian Bulletin of Big Data Management","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141371799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Computer Security through Formal Verification of Cryptographic Protocols Using Model Checking and Partial Order Techniques","authors":"Mahlaqa Saeed, Muhammad Ibrar, Dua Mahmood, A. Delshadi, Aqdas Saleemi","doi":"10.62019/abbdm.v4i02.176","DOIUrl":"https://doi.org/10.62019/abbdm.v4i02.176","url":null,"abstract":"This study explores the integration of partial, order reduction techniques with model checking to enhance the verification of cryptographic protocols. Cryptographic, protocols are essential for securing digital communications, and data; however, ensuring their robustness and reliability through traditional verification methods can be challenging due to, their complexity and the vast number of potential executions, paths. This research utilizes the SPIN, model checker and PROMELA language to model and verify three types of, cryptographic protocols: a basic authentication handshake, an encrypted data transfer, protocol, and a complex, multi-factor authentication process. Each protocol was, verified with and, without the application of partial order reduction techniques. The findings demonstrate that partial order, reduction, not only significantly reduces the computational resources, required—by approximately 40%—but also maintains the thoroughness needed to identify critical, security flaws such as replay, attacks, data integrity breaches, and authentication failures. This study, underscores the, efficacy of combining partial order reduction with model, checking, proposing that this, approach significantly improves the scalability and efficiency, of cryptographic protocol, verification. The results advocate, for the continued development and application of these, techniques to ensure the secure and reliable deployment of cryptographic protocols in various digital environments","PeriodicalId":518249,"journal":{"name":"The Asian Bulletin of Big Data Management","volume":"94 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Leaf Diseases of Different Crops using modified ResNet50","authors":"Muhammad Suleman Memon, Mumtaz Qabulio, Pardeep Kumar, Asia Khatoon Soomro, Sheeba Memon","doi":"10.62019/abbdm.v4i02.166","DOIUrl":"https://doi.org/10.62019/abbdm.v4i02.166","url":null,"abstract":"  Every country’s income depends heavily on the agriculture sector. Agriculture output is influenced by a wide range of factors, including crop health, weather, water availability, and type of land. Low output is primarily caused by crop disease. The Identification and treatment of diseases early on are very crucial. Traditional approaches don't offer a reliable evaluation. The spread of the disease must be determined using automated techniques. One of the cutting-edge technologies that aids in automating disease detection is deep learning. Deep learning has been used in numerous studies to detect crop diseases. The goal of this research is to effectively diagnose leaf diseases in all major crops using a novel deep learning model using a modified ResNet50. The research employed different deep learning models like VGG16, Inception V3 and modified ResNet50 on publicly available dataset of plant village. A novel approach has been proposed by extracting features from different models. The ResNet50 uses the skip connection which is considered to training and test error. The modified ResNet50 model helps add diversity in different crops for providing accuracy across all crops on public dataset of plant village. The dataset was obtained from a publicly available database on Kaggle. The dataset contains 13 different crops and a total of 38 diseases classes. On modified plant villages the proposed model generated a very good accuracy of 99.49% with precision of 0.966 and f score of 0.995. The proposed model is reliable for the classification of various leaf diseases in all the major crops. ","PeriodicalId":518249,"journal":{"name":"The Asian Bulletin of Big Data Management","volume":"41 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141270121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shedding Light on Financial Sustainability: Analyzing the Economic Implications of Energy-Efficient Lighting in the Construction Industry","authors":"Nijah Akram, Muti ul Haq, Ahsan Khalil, Laraib Zaman, Muhammad Fahad, Fahad Asghar","doi":"10.62019/abbdm.v4i02.141","DOIUrl":"https://doi.org/10.62019/abbdm.v4i02.141","url":null,"abstract":"Energy is widely recognized as a critical production factor across numerous industries. Energy efficiency is a method by which the expansion of energy consumption is managed and restrained. The main goal of this research is to investigate the financial implications of energy-efficient lighting in the building sector.The objectives of the paper were to: evaluate the current adoption and market share of energy-efficient lighting technologies in the construction sector, assess the economic impact of energy-efficient lighting, including cost-effectiveness, potential energy savings, and greenhouse gas emission reductions, analyze the effects of energy-efficient lighting on worker safety, productivity, and overall project costs in the construction sector, identify barriers to adoption and recommend policy changes and incentives to promote the widespread use of energy-efficient lighting in the construction sector in Pakistan.The methodology of the research follows the quantitative approach, data which was used to achieve the objectives includes literature, data sources, and project details on the construction industry from 2015 to 2021. Literature analysis and Data metrics were used to analyze of data.Studies found that energy-efficient lighting not only provides significant long-term cost savings but also improves environmental sustainability by lowering greenhouse gas emissions and energy consumption. Further, the literature analysis shows the beneficial relationship between adequate lighting and improved worker well-being as well as project efficiency, even though empirical data on these topics may be scarce.The study suggested that to overcome barriers and optimize the advantages of energy-efficient lighting, it is important to increase knowledge, provide financial incentives, improve access to eco-friendly construction materials, and strengthen compliance with building energy codes.","PeriodicalId":518249,"journal":{"name":"The Asian Bulletin of Big Data Management","volume":"85 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140665441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Study between Growth of offering supply chain finance with Non-offering Banks in Pakistan.","authors":"Munibah Munir, Altamash Khan, N. Bhutta","doi":"10.62019/abbdm.v4i1.123","DOIUrl":"https://doi.org/10.62019/abbdm.v4i1.123","url":null,"abstract":"This study empirically examines a comparative study between banks growth of offering supply chain finance with Non-offering Banks in Pakistan. This study used dynamic generalized method of moments (GMM) technique, based on 22 Pakistani Banks (10 offer supply chain finance and 12 non-offering supply chain finance) data during 2012-2022. The results show a significant difference between banks growth by offering and non-offering supply chain finance. Furthermore, the results show a significant positive impact of banks growth with supply chain finance on banks financial performance. This study encourages banks to offer supply chain finance that contributes towards their growth and financial performance.","PeriodicalId":518249,"journal":{"name":"The Asian Bulletin of Big Data Management","volume":"13 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140754293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fake News Identification in Urdu Tweets Using Machine Learning Models","authors":"Zahid Iqbal, Fida Muhammad Khan, Inam Ullah Khan","doi":"10.62019/abbdm.v4i1.105","DOIUrl":"https://doi.org/10.62019/abbdm.v4i1.105","url":null,"abstract":"\u0000\u0000\u0000\u0000There is an increasing number of people who generate and distribute content online, especially via social media platforms, which is primarily responsible for the proliferation of fake information. Fake information can cause controversy and distort people's perspectives, so it needs to be addressed immediately. The goal of this work is to detect false information in Urdu tweets, a difficult task given the language's large user population and particular grammatical difficulties. We offer an all-inclusive machine learning system that reliably classifies tweets in Urdu as legitimate or false. The methodology we use consists of several key steps: preprocessing, which includes normalizing, tokenizing, removing stop words, and stemming to prepare the data for analysis; data collection, which involves compiling and annotating a sizable dataset of Urdu tweets; and feature extraction, which makes use of technique TF-IDF to extract the semantic and syntactic nuances of the language. We investigate various machine learning models, including RNNs and CNNs, and more sophisticated neural networks like SVM, Random Forest, Logistic Regression, Naive Bayes, and Decision Tree to find the most efficient method for resolving this classification problem. The models are put through a rigorous training and assessment process using measures including the F1 score, accuracy, precision, and recall. Furthermore, a thorough examination of their confusion matrices is done. Our study's findings suggest that deep learning models hold much promise for resolving the problem of inaccurate information in Urdu. This opens the door for additional research and the creation of real-time algorithms for spotting false information. The subject of information integrity in Urdu language content is improved by this work, which also sheds light on the applicability of machine learning techniques in many linguistic contexts. Using SVM, Random Forest, Logistic Regression, Naive Bayes, and Decision Tree we achieved accuracies of 74%,91%, 76%, 78%, and 67% respectively. Meanwhile, CNN and RNN are the classifiers with the highest accuracy levels at 91% and 99% respectively. The results demonstrate that the CNN Model achieved 99% highest accuracy in detecting fake news from Urdu Tweets.\u0000\u0000\u0000\u0000","PeriodicalId":518249,"journal":{"name":"The Asian Bulletin of Big Data Management","volume":"921 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Behavioral and Rational Explanation of Stock Price Performance Around Mergers and Acquisitions in Pakistan: Evidence from Decomposition of Market to Book Ratio","authors":"Ayyaz Nadeem, Muhammad Irfan, Zulfiqar Ali, Shoukat Ali","doi":"10.62019/abbdm.v4i1.102","DOIUrl":"https://doi.org/10.62019/abbdm.v4i1.102","url":null,"abstract":"\u0000\u0000\u0000\u0000This study aims to analyze the firm-specific errors (FSSE) as a proxy for mis-valuation and the long-run value-to-book ratio as a proxy for growth opportunities during mergers and acquisitions. In this study, the researcher used the RKRV methodology, which was developed in 2004, to analyze firm-specific errors and future growth opportunities by decomposing the market-to-book ratio in the merged organization of the Pakistani financial sector. The researcher used the mergers and acquisitions data from the financial sector from 2000 to 2021. For the purpose of analysis, the researcher prepared one year of pre-merger and three years of the merger performance of merged financial institutions and compared it with nonmerger financial institutions. The results of non-zero log differences between market values and fundamental values show that in Pakistan firms, firm-specific errors (FSSE) exist in the merged and nonmerger financial institutions. The second component of this methodology was to calculate the long-run value of the book (LRVTB) by taking the difference between the estimated market value and the firm's book value as a proxy for long-run growth opportunities. The results show that no long-run growth opportunities exist in Pakistan's merged and non-merged financial institutions because the difference between estimated market value and book value is negative. The third component of this study is to estimate time series sector error (TSSE) by taking log differences between fundamental value and estimated market value if the firm-specific error exists in the valuation of firms and there are no long-run growth opportunities. The results show that time series sector error also exists in the valuation of financial institutions. The researcher concludes that the mergers and acquisitions intentions are to meet the minimum capital requirement enforced by The State Bank of Pakistan. The merger and acquisition activities that took place during the years 2000 to 2011 are the legislative mergers. The results satisfy the economic shocks theory. There is no issue of agency problems, whether mergers are successful.\u0000\u0000\u0000\u0000","PeriodicalId":518249,"journal":{"name":"The Asian Bulletin of Big Data Management","volume":"54 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140528329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}