Journal of Computing and Emerging Technologies

Execution Performance of Dijkstra’s Algorithm: A Cross-Language Analysis

2026-07-14T05:58:29+00:00

This present study looks at the intensive performance analysis of the algorithm of DIJKSTRA executive in four programming languages: Python, Java, C++, and MATLAB. This study explains how the performance of the DIJKSTRA algorithm changes across these four languages, which is often used to find the shortest distance. It considers key performance factors and different types of graphs. The researchers analyzed various graph complications to mimic real-world problems, including small, large, rare, and dense examples. They tested how well the algorithm worked in terms of time to run, memory usage, complexity, and efficiency in situations using the selected language. The results show how the output changes with the choice of programming languages, and have a big effect on the performance of the algorithms, with each language having its own strengths and weaknesses. Python takes less memory but a long time to run, while C++ is faster but uses more memory for execution in dense graphs. MATLAB is strong in computation but uses a lot of memory, and Java offers a good balance between time and memory. These results show the importance of choosing the right languages depending on the specific task we need. This study adds to the discussion about adopting algorithms. It helps programmers make informed choices about which language to use for implementing the Dijkstra algorithm based on their needs and challenges.

From Automation to Personalization: Evaluating AI's Influence on Educational Outcomes

2026-07-14T11:04:01+00:00

The concept of Artificial Intelligence (AI) already finds some recognition in education as a factor that can make it more progressive and efficient. The spheres, in which AI was utilized, are the advanced teaching, individual learning, student-teacher coordination, and the use of technology in evaluation, which has been examined by former researchers. Application of AI in reaction and identification of the emotions exhibited by the students in the learning process is one of such systems. Modern technologies can identify facial expressions, tone gaps, and interpret written feedback to provide correction feedback and specially designed emotional feedback. Neither is optimizing the group of students based on the help of the uplifting of the village academic performance but the students emotional well-being and interest. The discussed article is a literature review where no experiment is conducted. It reveals the methodologies, concerns and infusion of emotional intelligence in AI-based learning. Also, ethical concerns that include privacy, fairness, trust among others are covered. The synthesized overview of the study given would be instructive in the context of another study and play part in acquisition of intelligent educational systems, which are supporting, interactive, and useful.

Exploring Execution Time Patterns in Python Sorting Algorithms: A Statistical Approach

2026-07-14T11:07:04+00:00

Organizing objects in computing? Otherwise, I hope that you get lucky in that digital mess. In my plan to examine this project, therefore, I was interested in test how some Python sorting algorithms actually perform in relation to speed. We mean the classics: bubble sort (rough), selection sort, merge sort and short quicksort as all obviously. I simply wrote code to execute each of them in Python and gave them a pile of random lists (large ones, small ones, whichever you want), executing each one and recorded the runtime. All those numbers? Goldmine to figure out which algorithms do make your time valuable, and which just a waste of it. In fact, what this is doing is to have a feel of how these sorting gimmicks apply to real-world data so that the next time somebody is trembling under the choice of algorithm, we actually have the receipts. The following sections discuss the existing literature, identify the methodology, report and discuss the results, and conclude about the inferences and recommendations.

Intelligent Attendance and Database Integration Using Facial Recognition

2026-07-14T11:13:25+00:00

Face recognition technology is used in practically every industry in the modern world. Face recognition? It’s everywhere now, and honestly, schools are jumping on that bandwagon too. Instead of wasting time with some teacher squinting at a list, calling out names like it’s 1995, this new system, let’s just call it the “Face Check Thing,” does all the hard work. You stroll into class, the camera grabs your mug, and boom, attendance sorted. No more barcode scanning, no more “Oops, I forgot my ID.” None of that. Here’s the deal: every student gets their face snapped during sign-up, and that pic sits in a database somewhere safe (well, as safe as you can hope in 2024). When class starts, the camera just scans the room, matches faces with the database, and logs who’s actually there. It’s all automatic, so teachers can finally stop playing secretary and start, you know, teaching. Plus, the system cranks out attendance reports for the class, for the whole school, whatever you need. So, schools save time, cut down on paperwork, and probably spend less on those cheap plastic ID cards nobody remembers to bring anyway. Streamlined, efficient, and just a little bit Big Brother, but hey, welcome to the future.

Fine-Tuning Open-Source LLMs for Social Good: The Case of Cyber Falcon in Cyberbullying Detection

2026-07-14T11:17:49+00:00

Shopping is a very important issue of concern due to its ability to harm and affect the victims in a negative manner. In the recent past, there has been increasing literature that seeks to explore the application of machine learning models to identify acts of cyberbullying. The paper presents a new approach to detecting posts related to cyberbullying, which is based on the Large Language Model Falcon 7b. The training was performed on a set of 50,000 tweets where an effort was made to ensure that there was a balance of cyber- and non-cyberbullying was evenly distributed in the data set. Fine-tuning on the training dataset was then done on the model. After the fine-tuning, the analysis and evaluation of the specific dataset was fully carried out to establish the testing dataset. The above model returned a precision score of 0.334 and accuracy score of 0.334; both of which were much higher than the precision and accuracy scores of naive NLP detection model. In this research, it was found that Falcon 7b model may be a fresh approach to identify any cases of cyberbullying. It has a high capacity of learning complex patterns of language, and this factor is indicative of cyberbullying. Also, it shows the large competence in applying the gained knowledge in new environments, which depicts the ability of the generalization. Besides, performance by the model results in efficiency because it is able to classify a tweet within a few seconds time frame. The implications of the study findings on preventing and detecting cyberbullying in the future are greater. The discussed model has the potentials to be used in the creation of automated systems with a specific role of identifying the cases of cyberbullying. Such systems can be used on different social media. Such systems show that they can identify the cases of cyberbullying at the nascent stage and engage in measures to stop the damage that has been caused to people who victims of this practice. Besides, the adoption of this model demonstrates its ability to be further extended in its application to educate human moderators who are to monitor cases of cyberbullying. The ability of the moderators to identify and retrieve the cyberbullying messages in the social networking sites will be enhanced by attaining a balanced understanding of deliberate linguistic codes employed as recognizable indicators of cyberbullying. Thus, the research indicated that Falcon 7b model has a number of feasible elements that can be implemented in the sphere of cyberbullying detection. This model has great accuracy, efficiency and scalability, therefore a potentially marketable and innovative means of approaching and handling this monumental problem.

AI-Powered Cavity Detection: Leveraging Transfer Learning in Dental Images

2026-07-14T11:20:45+00:00

Dental cavities are a prevalent concern regarding one's dental health; untreated dental cavities can lead to pain, infection, and tooth loss. The conventional approaches employed in the diagnosis are radiographic evaluation and visual examination, which are limited by subjectivity and the risk of user error. This work demonstrates the diagnosis of dental cavities in dental images through automation with artificial intelligence (AI) using deep learning and transfer learning techniques. The proposed model is based upon an ensemble YOLO architecture for real-time detection of cavities and then leverages test-time augmentation to improve accuracy. The identified dental pathology is subsequently classified into observable changes with and without cavitation using transfer learning. Performance assessments using metrics demonstrated improved accuracy and efficiency in diagnosis. The challenge of the model differentiating cavities exposes the need for more precise optimization, despite the model's superior performance in caries detection. This work illustrates the positive implications AI-dental diagnosis systems can have on patient care, potentially decreasing diagnostic errors and improving early detection. Future work can strategically focus on expanding datasets and refining AI models to assist in the accurate and reliable identification of dental disease.