Intangible resources are ever-increasingly vital to individual businesses' success and competitiveness in today's emerging knowledge economy. Against this background, this study explores and empirically models the influence of Intellectual Capital on Sales Growth, with Corporate Governance as a moderator, in the Information Technology industry of Karachi. In this research, Intangible Resources comprise three dimensions of Intellectual Capital-Human Capital, Structural Capital and Relationship Capital; and three theoretical perspectives-Resource Based View, Knowledge Based View and Agency Theory. Knowledge resources are accumulated through the process of knowledge integration resulting in revenue growth through increased efficiency and effectiveness, while effective Corporate Governance determines alignment [...]

Recognition of faces with partial occlusions poses an exceptionally difficult challenge for contemporary researchers working on biometric identification tasks. Various real-life use cases in areas such as border crossings, healthcare services, smart surveillance, etc., usually require performing face identification despite masks, sunglasses, scarfs, and similar accessories blocking parts of the face, causing poor recognition performance. In this study, we conduct a systematic analysis of occluded face recognition research works that have been carried out between 2021 and 2026. After the application of inclusion/exclusion criteria to our initial database consisting of relevant peer-reviewed articles in IEEE Xplore, ScienceDirect, Springer, and arXiv [...]

The retail fuel price forecasting of emerging economies is important for the energy policy, fiscal planning and consumer welfare. Pakistan, where petrol and diesel prices are periodically revised (twice every fortnight) using an Import Parity Pricing (IPP) mechanism, is strongly influenced by changes in the international crude oil benchmarks (Brent and WTI) and Pakistani Rupee (PKR) to US dollar exchange rate. The paper presents a hybrid machine learning model that combines an autoregressive integrated moving average (ARIMA) model for trend extraction with a Random Forest (RF) regressor of nonlinear residual patterns. With real-time crude prices, historical PKR/USD weekly data (2026), [...]

The effects of COVID’19 have been witnessed by every individual, and it has impacted everyone in a different way. Due to the pandemic, work from home (WFH) has become a policy priority for most organizations. We expect that the people will seem to be satisfied with their job while working from home, because of certain factors like productivity, motivation, reduces in expense, work family-life balance. And to understand the effect of social, behavioral, and physical factors on the well-being of office workstation users during COVID19 work from home (WFH), we conducted an online survey to capture the experience of people, [...]

Abstract: Data duplication is one of the core issues in data warehouses pertaining to the quality of data. By finding and removing duplicate data, you can decrease the amount of space needed to store your data. For smooth, efficient, and fast analysis the duplicated data needs to be filtered out before using it for further process. However, very few research has been done on data deduplication for textual data. Realizing this gap, this paper presents analysis of bloom filters to detect duplicates in textual data. The paper discusses the challenges and the need for textual data deduplication. Existing literature on [...]

Abstract: This study applies k-means and mini-batch clustering dataset for customer segmentation. Based on the analysis, various clusters were formed and validated. It was found that the results of both clustering techniques are same with an enhancement in processing speed via the use of mini-batch k-means. Customer segmentation can be used for market intelligence to identify interested clients by giving corporate entities in the retail sector pertinent and relevant facts periodically. It can be used as methods for examining customer purchase patterns and sales trends. The clustering has been applied over a dataset extracted from Kaggle. After performing the exploratory [...]

Abstract: With the growing interest in academics and industry towards research and the availability of large number of publishers, the frequency of publications is also on the rise. It is found that researchers are very careful about the selection of the related periodicals and the proceedings with good research index and well-known publishers. In this direction, this research performed a quantitative analysis of the publication by various authors in leading journals. We identified their interests/ selection of the periodicals for their research publication. For this purpose, a scrapping tool was developed, and data was scrapped for scientific publications published in [...]

Abstract: The focus of this study is the design, development, and impact of an example of no-code software application builder using drag and drop. By using programming visually, automating workflows, and integrating autonomously with different backends, this research also mitigates the traditional software development and the non-programmer spends on software development, simplifying and making app building development speedier, easier, and more scalable. This study focuses on architectural structures and the logic building processes coupled with the database, and aims at custom tailoring unmodifiable software that belt on security worries, unreachable deployment, and more, toward problematic customization, unchecked triangulation, unbounded cooperation, [...]

Testing is a crucial process for evaluating whether a software system meets the needs of users and functions effectively in its designated environment. Sometimes, mistakes by programmers and testers or untested code can cause problems after the software is released. This can be costly to fix. The main goal of this research is to use regression testing to make sure any changes based on customer requests are reliable. We also want to prioritize important test cases to save time but still evaluate the software effectively. Additionally, the goal of this comparison is to modify, minimize and prioritize test cases in [...]

COVID-19 has affected the life of every individual and has changed the mode of working. According to the studies, the COVID-19 brought the toughest time for everyone around the world. It disturbed the regular lives of people, bringing in supply shortages in stores and hospitals, disturbing the business markets and lead to downsizing in companies. However, the COVID-19 has also resulted in the emergence of several startups. This paper analyzes the trends of startups during COVID-19 period. The paper first hypothesized that there is a growing interest towards startup during and post COVID-19 era. Then, a questionnaire was constructed to [...]

Sentiment analysis in Roman Urdu is increasingly crucial for enhancing consumer decision-making in diverse product domains. This paper addresses the challenge of extracting sentiment from product reviews written in Roman Urdu, leveraging Context-Free Grammar (CFG) to classify reviews into positive, negative, or neutral sentiments. Our study utilizes a dataset of online product reviews sourced from e-commerce platforms, focusing on the automation of sentiment classification. We propose a comprehensive methodology for sentiment extremity classification, demonstrating promising results through sentence-level analysis. This research contributes to advancing sentiment analysis in NLP, particularly in under-resourced languages like Roman Urdu, highlighting both methodological innovations and [...]

The challenge of predicting rainfall is undoubtedly a difficult one because there are an extensive number of different factors and elements that influence the conditions of the climate. It is of extreme significance to have accurate rainfall forecasts, particularly for the agricultural sector, which is highly reliant on timely and sufficient rainfall for the growth and yield of crops. The contribution that agriculture makes to the economy is another factor that highlights the need of accurate rainfall forecasts. There are many different ways that have been utilized all around the world in order to forecast the patterns of rainfall. These [...]

During the past few years, cybersecurity has gained a lot of prominence. In this paper, phishing attacks are discussed in detail. In a phishing attack, an attacker disguises itself as a genuine user and deceives the end-user to gains its personal information such as credit card, usernames/ passwords etc. There are a number of ways to perform phishing attacks such as via email, spear phishing, clone phishing, whaling etc. This paper talks about phishing attacks in detail along with its techniques, counter-measures and recent trends directions for research. Phishing is a significant threat to every individual, organization, and in general [...]

Cloud computing is the transformation of IT infrastructure into a utility which allows the users to plug-in infrastructure over the internet, and utilizes the computing resources without their installation and maintenance on-premises. It is time-consuming and a headache to manage resources on your own. Cloud computing provides shared and dedicated resources to facilitate large scale tasks or time-consuming tasks. To get the full benefit from cloud computing we need algorithms to allocate resources according to client needs and algorithms for fast access to the cloud servers. Here comes load balancing. Load balancing is a technique to distribute load among various [...]

Deep neural networks are now widely employed in artificial intelligence applications that have significantly changed human livelihoods in a number of ways. Weather forecasting has long been considered an important area of study with far-reaching consequences for disaster management and public safety. This research analyzes the use of deep learning and machine learning techniques to create a mobile application that offers users real-time weather predictions. The primary goal of this study is to look into the usefulness of machine learning and deep learning algorithms in predicting weather patterns using historical weather data Rainfall. To analyze various data sources and develop [...]

The internet has opened up vast opportunities, connecting people globally and revolutionizing industries. The emergence of internet lead to multiple societal transformations such as the way people interact and perform their daily operations. However, it also presents several challenges of big data. The exponential growth of data generated from various sources requires advanced technologies and analytical tools to effectively process, store, and analyze this massive volume of information. Managing this huge data is crucial for organizations to make data-driven decisions and stay competitive in today’s data-driven world. The challenges posed by massive datasets have led to the development of new [...]

Requirements engineering is the cornerstone of software development and a pivotal steps of the whole life cycle of software development. As much time we devote on requirement engineering, the higher the likelihood of success in later part of software development. It is right said that compelling requirements engineering steps guarantees and predicts effective software artifacts. This paper presents a study on software requirements engineering practices followed in little, medium and large-sized organizations of Pakistan. The study is directed by-polls that show how most of prerequisite designing models and practices are applied in software development in Pakistan. [...]

Data science plays an increasingly important part in modern society as a result of its quick rise to prominence across a number of industry sectors. The increased importance of this calls for a flexible and innovative approach to teaching. In order to accommodate the changing needs of data science education, this research article proposes a hybrid teaching scheme (a combination of flipped and conventional approach) that places a special focus on programming languages and tools. This study has performed a survey of 100 participants from diverse domains and inquired them about their preferred teaching methodology and the language of choice [...]

Abstract: With the expansion of online platforms, new rooms are opening to extract full insights from huge textual data. Aspect-Based Sentiment Analysis (ABSA) is rising as an essential Artificial Intelligence (AI) discipline that employs Natural Language Processing (NLP) to associate sentiments with specific attributes of entities. In this systematic review, we focused on ABSA studies published between 2020 and 2024, encompassing 66 primary research articles focusing on advancements in AI methodologies, including transformer-based models and generative AI techniques. The novel ABSA taxonomy developed in this study defines methodological categories and analyzes the use of commercial and domain-specific datasets. This research [...]

Abstract: During the past few years, there have been a surge in the use of social media. Various social media platforms have emerged such as Twitter, WhatsApp, Facebook and LinkedIn. The existing studies reveal that there has been a relation between social media usage and mental health. The objective of this study is to explore age biasness related to mental health data on social media platforms. Some studies have already been done on analysis of the effect of social media platforms on mental health. However, it has been observed that there is an issue of lack of inclusiveness towards the [...]

Risk assessment is one of the complex issues that determines the probability of occurrence of the events. The purpose of this paper is to present the various types of cyber risks and attacks that can significantly affect organizational performance. This research study is qualitative research in which data has been collected from various respondents operating in the field of cyber security in Pakistan’s telecommunication sector. This paper follows the convenience sampling technique to collect data from various participants. The sample size of 5 participants consisting of cyber security experts has been chosen for this research. In-depth qualitative interviews were conducted [...]

Abstract: This paper talks about a novel comparison of two deep learning models namely Long short-term memory (LSTM) and Gated recurrent unit (GRU). The target domain is e-commerce sales prediction applied on behavioral data from a Shopify store. The dataset comprises 87000 records stepped by hours. The dataset contains details about various features such as session duration, bounce rate, cart addition, checkout activity. It has been found that LSTM and GRU both were able to learn the spatiotemporal dependencies present in the data. These models predict with good accuracy the probability of making a purchase in a session. By means [...]

Abstract: This work presents a systematic literature review of smart library management system. By studying different papers for past 10 years to address several research questions, the findings are presented. A smart library is based on employing smart information and communication technologies (ICT) to facilitate readers. Five major research questions are answered. What is smart library, the major research challenges in implementing smart library, the advantages of smart library, and the enabling technologies for smart library. Finally, this work presents a smart library management system based on deep learning technique. An android mobile application is developed that digitizes the book [...]

This paper investigates how fake news in Urdu language can be identified on social media content. There has been increasing usage of social media and the new is ubiquitous and spread daily over the social media. It is very difficult to identify original news from a fake news. As we all know, billions of users are using social media from all over the world and they spread all types of news. It is very difficult to identify if a news is fake or real and this poses serious concerns as the ultimate objective of the original poster may be malicious. [...]

Sentiment analysis has been a topic of intense research. This study performed sentiment analysis to explore user perception and attitude towards ChatGPT using sentiment analysis. In this study, sentiment analysis techniques are applied to two datasets. These datasets include i.e. tweets and Reddit posts. It contains references to ChatGPT within the context of education. The study aims to reveal the user’s sentiment, opinions, and comments expressed on these social media platforms regarding the use of ChatGPT in educational environments. The study adopts the following methodology. A diverse quantity of textual data is collected in real time from Twitter and [...]

Large Language model (LLMs) has been used extensively in the past few years for natural language processing (NLP). But they have a major flaw: they often produce hallucinated results. Hallucination refers to a large language model creating coherent but unreal/ non-factual text. In other words, the model can generate consistent but wrong responses. They often lack a clear source for the information. This research examines the critical issue of hallucinations in LLMs. It focuses on ChatGPT and Gemini. To explore this, we created a dataset of Roman Urdu questions and their human-generated answers. We prompted ChatGPT and Gemini to answer [...]

The usage of social networks is rising in the past few years. Most of the frequently used information is widely shared via social media networks including Facebook, Twitter, Instagram etc. Via these platforms, people can show their feelings, expressions, emotions, and opinions. These platforms provide meaningful content about users and their personality traits. Such information can be exploited to analyze the personality of the end-users. In this research, we explored a novel technique for personality modelling using Big 5 traits. We propose a comprehensive framework and machine learning pipeline that can be adopted for personality prediction. In order to solve [...]

This qualitative study investigates the relationship between cyberbullying and social withdrawal in 12–20 years teenage population. The study reviews survey data collected from students at different frequencies from own educational institutions to analyze the extent of cyberbullying, its emotional effects and leading to the behavioral changes. The results show a notable correlation between cyberbullying and social withdrawal; a large number of the surveyed teenagers reported experiencing anxiety, trust issues, and indivisibility: the preference to chat with each other online instead of meeting in person. The findings highlight the importance of systemic change and networking support in facing this growing issue. [...]