Universal Journal of Computer Sciences and Communications

In this paper, we will examine a rather complex case of the paradoxical nature of certain conclusions that may arise when studying the numerical convergence of a specific nonlinear recursive sequence, known in the literature as Muller’s sequence. To analyze the peculiar computational behavior of this sequence, it is necessary to employ a powerful mathematical framework in order to understand... In this paper, we will examine a rather complex case of the paradoxical nature of certain conclusions that may arise when studying the numerical convergence of a specific nonlinear recursive sequence, known in the literature as Muller’s sequence. To analyze the peculiar computational behavior of this sequence, it is necessary to employ a powerful mathematical framework in order to understand the nontrivial issues that can arise when the software implementation of this seemingly simple mathematical problem. These challenges often stem from the limitations of numerical methods and the inherent errors in computer arithmetic, which can affect the accuracy and stability of the results, particularly when dealing with iterative methods like Muller's sequence. Read more

Preference learning plays a crucial role in AI applications, particularly in recommender systems and personalized services. Traditional pairwise comparisons, while foundational, present scalability challenges in large-scale systems. This study explores alternative elicitation methods such as ranking, numerical ratings, and natural language feedback, alongside a novel hybrid framework that dynamically integrates these approaches. The proposed methods demonstrate improved efficiency, reduced cognitive... Preference learning plays a crucial role in AI applications, particularly in recommender systems and personalized services. Traditional pairwise comparisons, while foundational, present scalability challenges in large-scale systems. This study explores alternative elicitation methods such as ranking, numerical ratings, and natural language feedback, alongside a novel hybrid framework that dynamically integrates these approaches. The proposed methods demonstrate improved efficiency, reduced cognitive load, and enhanced accuracy. Results from simulated user studies reveal that hybrid approaches outperform traditional methods, achieving a 40% reduction in user effort while maintaining high predictive accuracy. These findings open pathways for deploying user-centric, scalable preference learning systems in dynamic environments. Read more

Sentiment analysis, a vital aspect of natural language processing, involves the application of machine learning models to discern the emotional tone conveyed in textual data. The use case for this type of problem is where businesses can make informed decisions based on customer feedback, identify the sentiments of their employees, and make decisions on hiring or retention, or for that... Sentiment analysis, a vital aspect of natural language processing, involves the application of machine learning models to discern the emotional tone conveyed in textual data. The use case for this type of problem is where businesses can make informed decisions based on customer feedback, identify the sentiments of their employees, and make decisions on hiring or retention, or for that matter, classify a text based on its topic like whether it is about a particular subject like physics or chemistry as is useful in search engines. The model leverages a sequential architecture, transforms words into dense vectors using an Embedding layer, and captures intricate sequential patterns with two Long Short-Term Memory (LSTM) layers. This model aims to effectively classify sentiments in text data using a 50-dimensional embedding dimension and 20 % dropout layers. The use of rectified linear unit (ReLU) activations enhances non-linearity, while the SoftMax activation in the output layer aligns with the multi-class nature of sentiment analysis. Both training and test accuracy were well over 80%. Read more

Maintenance of large-scale software is difficult due to large size and high complexity of code.80% of software development is on maintenance and the other 60% is on trying to understand the code. The severity of the code smells must be measured as well as fairness on it because it will help the developers especially in large scale source code projects.... Maintenance of large-scale software is difficult due to large size and high complexity of code.80% of software development is on maintenance and the other 60% is on trying to understand the code. The severity of the code smells must be measured as well as fairness on it because it will help the developers especially in large scale source code projects. Code smell is not a bug in the system as it doesn’t prevent the program from functioning but it may increase the risk of software failure or performance slowdown. Therefore, this paper seeks to help developers with early prediction of severity of code smells and test the level of fairness on the predictions especially in large scale source code projects. Data is the collection of facts and observations in terms of events, it is continuously growing, getting denser and more varied by the minute across different disciplines or fields. Hence, Big Data emerged and is evolving rapidly, the various types of data being processed are huge, but no one has ever thought of where this data resides, we therefore noticed this data resides in software’s and the codebases of the software’s are increasingly growing that is the size of the modules, functionalities, the size of the classes etc. Since data is growing so rapidly it also mean the codebases of software’s or code are also growing as well. Therefore, this paper seeks to discuss the 5V’s of big data in the context of software code and how to optimize or manage the big code. When we talk of "Big Code for Big Software's," we are referring to the specific challenges and considerations involved in developing, managing, and maintaining of code in large-scale software systems. Read more

A user is considered to be someone who would be actually using the system. The definition of a user is contextual and for any specific software project it depends on the software development methodology being developed. Satisfaction is the fulfillment of one’s wishes, expectations, needs or the pleasure that is derived from using a software product therefore “user satisfaction “is... A user is considered to be someone who would be actually using the system. The definition of a user is contextual and for any specific software project it depends on the software development methodology being developed. Satisfaction is the fulfillment of one’s wishes, expectations, needs or the pleasure that is derived from using a software product therefore “user satisfaction “is a reflection of quality from different perspective than measuring defects or code structure. Measurement in software engineering makes comparison possible and when you compare you have a choice and quality is quantified in terms of values. Therefore, this paper aims to explore the impact of software measurement scales on user satisfaction. Read more

Digital forensics in cloud computing environments presents significant challenges due to the distributed nature of data storage, diverse security practices employed by service providers, and jurisdictional complexities. This study aims to develop a comprehensive framework and improved methodologies tailored for conducting digital forensic investigations in cloud settings. A pragmatic research philosophy integrating positivist and interpretivist paradigms guides an exploratory sequential... Digital forensics in cloud computing environments presents significant challenges due to the distributed nature of data storage, diverse security practices employed by service providers, and jurisdictional complexities. This study aims to develop a comprehensive framework and improved methodologies tailored for conducting digital forensic investigations in cloud settings. A pragmatic research philosophy integrating positivist and interpretivist paradigms guides an exploratory sequential mixed methods design. Qualitative methods, including case studies, expert interviews, and document analysis were used to explore key variables and themes. Findings inform hypotheses and survey instrument development for the subsequent quantitative phase involving structured surveys with digital forensics professionals, cloud providers, and law enforcement agencies, across the globe. The multi-method approach employs purposive and stratified random sampling techniques, targeting a sample of 100-150 participants, across the globe, for qualitative components and 300-500 for quantitative surveys. Qualitative data went through thematic and content analysis, while quantitative data were analysed using descriptive and inferential statistical methods facilitated by software such as SPSS and R. An integrated mixed methods analysis synthesizes and triangulates findings, enhancing validity, reliability, and comprehensiveness. Strict ethical protocols safeguard participant confidentiality and data privacy throughout the research process. This robust methodology contributed to the development of improved frameworks, guidelines, and best practices for digital forensics investigations in cloud computing, addressing legal and jurisdictional complexities in this rapidly evolving domain. Read more