Dependency parsing in Myanmar face many challenges due to agglutinative morphology, flexible word order, and lack of explicit word boundaries. This paper addresses these challenges by investigating the performance of Myanmar dependency parsing based three pretrained-transformer models: XLM-RoBERTa-Large, XLM-RoBERTa-base-Longformer-4096, and language-specific model, MyanBERTa. The proposed methodology utilizes a parameter-efficient model with adapter layers and a biaffine parsing mechanism by optimizing multi-task learning objective across six linguistic prediction tasks. The experimental results demonstrated that XLM-RoBERTa-Large achieves the highest Labeled Attachment Score (LAS) and Unlabeled Attachment Score (UAS) on both development set and test set. Despite XLM-RoBERTa-base-Longformer-4096 model can extended its input capacity and MyanBERTa is pretrained on a language capacity, their performance drop compared to XLM-RoBERTa-Large. These findings suggest that the scale and architectural strength of models are more critical for high-performance dependency parsing than models with an extended input capacity or those trained on a language-specific corpus.

Traffic accidents are becoming a major disruptive issue and emergency services play a key role in rescuing injured people from traffic accidents to reduce the loss of life and property. Today's technologies can trace the current place of the traffic victims and send the user's information with that location to the nearest emergency station to get the timely rescue service. The Motion Sensing Method can detect traffic accidents from the values of the sensors in the smartphone, and determine the accident levels. The Difference Angle Method shows the intended destination of the automobile with endless GPS signals. To obtain detailed results, the Modern Positioning Method is used to communicate with the sensors to get the accident location and track the victim. The Object Detection in Fence Algorithm searches for the nearest emergency services to provide medical assistance by sending the accident information. This system can detect the location of injured persons, analyze the severity levels of accidents, send timely messages to rescue stations, and identify the nearest routes.

Many students find it challenging to comprehend the communication mechanism that is a part of TCP/IP because learning about it through textbooks and lectures is not enough.Network simulators are helpful resources for computer network education.Because the complex operations that occur in the computer network during data exchange between network devices are completed in incredibly tiny time slices, the learner is unable to follow and understand them.The main objective of our simulator is to display visually these processes.Our suggested system allows students to play with network activity to help them understand it.Using specific packets, step counts, and the network configuration setting as input, our approach calculates packet transmission step-by-step.Our method enables the students to comprehend network behavior.With our simulator, students may analyze network behavior in a variety of settings without requiring specialist gear.

Globally, Apple iOS has 17.3% of smartphone market share, which aligns with the findings from Cellebrite in 2024.It highlights iOS devices as dominant digital evidence in digital forensics investigations.However, accessing the data in locked iOS devices remains a significant challenge due to limited data accessibility.This study evaluates the effectiveness of Before First Unlock (BFU) extraction in recovering user data from locked devices.We compare data availability and integrity between BFU and Full File System (FFS) extractions using mobile forensics tools and file hash comparison.The analysis revealed a 63.48% match rate (338,062 of 532,509 files) between BFU and FFS extractions, indicating some data loss in BFU.While some documents were recoverable, critical application data was inaccessible.This highlights the limitations of BFU extraction in retrieving complete datasets from locked iOS devices.However, the recovered data with verified integrity remains valuable for forensic investigations.

This paper presents modelling of a physicalSpirograph Toy, a mathematical instrument for the curve generation of roulette family, using two-link planar robotic arm whose forward kinematic equations can yield parametric equations of the trochoid or spirographic family of curves if both joints are rotated simultaneously respecting a proportional relation.The software version of spirograph toy was simulated in MATLAB using two-link planar robotic manipulator having all joints revolute type and spirograph curves were obtained for different parameters values.Here cases of different patterns of spirograph curves such as hypocycloid, hypotrochoid, epicycloid and epitrochoid are dealt with examples.The generated curves were found in agreement with that of generated by parametric equations and a physical spirograph toy.The implemented soft version of spirograph provides scope for selecting multiple trace points at different positions, even beyond the region of rolling disc, simultaneously which is not possible with the physical spirograph toy.In the digital version of spirograph, it has been found possible to draw a fixed-frame crossing spirograph which is not possible to complete with the physical spirograph toy and this practicability makes digital version of spirograph superlative while providing feeling of physical spirograph motion in the course of obtaining spirograph drawings.The proposed robotic model was also used to draw spirograph of fixed size but of the same shape as that of produced by spirographic motion using inverse kinematics and Affine transform.Such a model can be used further to make spirographs on wood frame or walls for decoration.

The paper recommends a new hybrid model for software projects delivery with fine blend of the both agile and waterfall models by addressing the drawbacks of both models.The traditional waterfall model development goes one phase after the other and any changes to previous phase is addressed late in the delivery cycle thus most of the times implemented software and customer expectation will not by in sync or sometimes poles apart.The trending agile focuses on small chunks development with iterative cycles, going back to the customer for feedback at every phase and reaching the expectation and delivering the functionality which client was looking for.The agile model may not be suitable for projects where the requirements are very intricate and cannot be easily fragmented into smaller iterations and requirements are definite and constant, and where deviations to the requirements are improbable.The waterfall model may not be suitable for projects where the requirements are uncertain or likely to change, as changes to the requirements need changes to previous phases.The paper recommends a new fine blended model with the best of agile and waterfall approaches with the collaboration of Delivery with Sprints and POD teams, and gets the delivery with optimal cost and speed.The projected novel model is a fine blend of best of both models by eliminating shortfalls of the Agile and Waterfall.

Heart disease is one of the most challenging tasks in health care.Several works have been proposed to address this challenge.In the recent past, some of the research articles are focused on the design of different machine learning algorithms for heart disease in health care, in this paper, we present an overview of supervised, unsupervised, and reinforcement learning algorithms used in the prediction of heart disease wherever possible.This review paper classifies some machine learning algorithms into different categories named Naïve Bays, Decision trees, support vectors, KNN, random forest, genetic algorithms, and structures with their relative performances.This paper also compares different algorithm techniques for heart diseases against accuracy and reliability.

Maintaining airspace safety and minimizing potential hazards have become urgent concerns as a result of the exponential expansion in aviation traffic. Proposed Aircraft Recognition System (ARS) is created to improve aviation safety by properly identifying and classifying aircrafts for the given input. The ARS uses sophisticated computer vision and deep learning techniques to quickly and automatically identify different kinds of military aircrafts. ARS performs recognition task using EfficientNet B3. Comparison with other existing models is done to evaluate the accuracy of proposed system. The accuracy of this model was observed to be 95.76% in comparison with VGG16, InceptionV3 and ResNet50 whose accuracy was observed to be 79.1%, 88.57%, and 56.2% respectively.