VFAST - Virtual Foundation for Advancement of Science and Technology (Pakistan)
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A Diagnostic Study of the Dyslexion Factor in Urdu Language at Early Classes in Public Schools of South Punjab
No full textDyslexia, a specific learning disability, exerts profound effects on language acquisition and educational attainment. This study delves into the underexplored domain of dyslexia within the context of the Urdu language, as encountered by students in public schools of South Punjab, Pakistan. Employing a comprehensive battery of standardized tasks and tests, the study diagnoses the presence of dyslexia and assesses its impact on the reading and writing processes. The findings reveal a spectrum of dyslexic characteristics among students, as evidenced by challenges in phonological awareness, letter-sound recognition, rapid automatized naming, and comprehension. Dyslexia significantly affects their ability to decode unfamiliar words and comprehend text. Concurrent qualitative interviews shed light on the diverse attitudes and reactions of students toward dyslexia, from determination to anxiety. This study\u27s results underscore the necessity for tailored interventions, early screening, and teacher training to support students with dyslexia in mastering the Urdu language. By focusing on both the diagnosis of dyslexia and its implications for language learning, the study hopes to contribute to more inclusive and effective education in South Punjab\u27s public schools.
The findings reveal a spectrum of dyslexic characteristics among students, as evidenced by challenges in phonological awareness, letter-sound recognition, rapid automatized naming, and comprehension. Dyslexia significantly affects their ability to decode unfamiliar words and comprehend text. Concurrent qualitative interviews shed light on the diverse attitudes and reactions of students toward dyslexia, from determination to anxiety.
This study\u27s results underscore the necessity for tailored interventions, early screening, and teacher training to support students with dyslexia in mastering the Urdu language. By focusing on both the diagnosis of dyslexia and its implications for language learning, the study hopes to contribute to more inclusive and effective education in South Punjab\u27s public schools.
Keywords: Dyslexia, Urdu Language, Public Schools, South Punjab, Language Learning, Standardized Tasks, Reading, Writing
Machine Learning-based Prediction of African Swine Fever (ASF) in Pigs
Get PDFAfrican Swine Fever (ASF) is a contiguous viral disease of the pig with serious economic threats to the pork industry. Early identification of ASF infection is important to support sustainable developments in the ASF industry. There is also a need for a solution to identify the ASF infection as early as possible based on apparent symptoms of ASF to screen the infected animals, that are not targeted in the existing literature. Many machine learning (ML) solutions have been proposed in recent years for the prediction and identification of human, animal, and plant diseases. To deal with ASF in pigs ML-assisted model is proposed for the early identification of ASF infection without medical diagnosis and expert opinion. The data regarding apparent symptoms are collected from Chinese small pig farms. The loss of appetite, weakness, diarrhea, vomiting, coughing, skin redness, and breathing difficulty levels are taken as major apparent symptoms of ASF infection. Moreover, different ML models are also evaluated for their performance in the prediction of ASF infection based on selected apparent symptoms of ASF infection. In this regard, Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), Decision Tree (DT), Random Forests (RF), and Gaussian Naïve Bayes ML models are evaluated for ASF infection prediction. The implementation of the proposed solution reveals that the GNB model is more accurate as compared to the other evaluated models for the identification of ASF infection from the apparent ASF symptoms in infected pig animals, with 94.31\% accuracy. The proposed solution would be very effective in the early screening of ASF-infected pig animals without medical diagnosis and expert judgment
QoS Authentic Routing protocol for Home-based IoT-based System
No full textIn recent years, IoT has emerged as one of the most transformative technologies of the 21st century. By connecting everyday objects such as home automation systems, manufacturing tools, agricultural equipment, healthcare devices, insurance platforms, transportation systems, kitchen appliances, cars, thermostats, security cameras, baby monitors, and more to the internet, IoT enables seamless communication between people, processes, and devices.Current trends in IoT technology are focused on designing and developing local trust values for routing nodes through enhanced K-means evaluation systems. Specifically, the goal is to improve routing evaluation by refining K-means clustering to create a more reliable network. In home-based IoT networks, the focus is on local trust values for routing nodes, addressing existing weaknesses by designing an advanced K-means algorithm to improve network reliability and security.In IoT networks, devices communicate with each other, transferring information from source to target nodes for processing, storage, and analysis. The proposed QoS-ARP protocol leverages this advanced K-means-based global trust value algorithm. By incorporating metrics such as packet delivery ratio (PDR) and monitoring for malicious nodes, the protocol aims to enhance network efficiency, reduce power consumption, and extend the network\u27s lifespan
A Robust Machine Learning Framework for Fraudulent Mobile App Detection
Get PDFThe rapid development of mobile applications has led to a significant rise in the number of fraudulent applications. The biggest risk now is financial loss and possible security compromise. Thus, the "Fraud App Detection" framework goal is to develop a reliable system that can recognize and categorize fraudulent apps utilizing cutting-edge machinelearning and artificial intelligence approaches. The process of identifying fraudulent patterns involves gathering data, preprocessing applications, extracting features, and training several machine learning models. The model’s performance will be assessed based on evaluation criteria like recall, accuracy, and F1-score. To improve detection efficiencyand accuracy, this uses cutting-edge techniques such as neural networks, decision trees, and ensemble approaches. These results can be used in enhancing mobile app security protocols, thus safeguarding consumers from the probable threats of fraudulent applications
A study of coefficient-related problems for symmetric starlike functions connected with a tan hyperbolic function
Get PDFThe article aims to determine the sharp bounds of coefficients, Fekete-Szegö, Zalcman inequalities for the family SS∗ tanh of starlike function with respect to symmetric points linked with tan hyperbolic function. We also estimate determinant of |H2,2 (f)| is also obtained for the same class. Further, we study the logarithmic and inverse coefficients for the same class
EOG Based Text and Voice Controlled Remote Interpreter for Quadriplegic Patients
No full textElectrooculography is considered as one of the significant electro-physiological signals. These signals carry data of eye movements which can be employed in human-computer interface (HCL) as a control signal. This project focuses on creating a text and voice-based interpreter for quadriplegic patients using electrooculography (EOG) signals. EOG is a technique that measures the electrical activity of the eye muscles responsible for eye movements and can be used to track changes in eye location to reveal information about human eye activities. The EOG signal is commonly used in human-computer interface (HCI) systems as an alternative input for patients suffering from quadriplegia, ALS, and locked-in syndrome. The BioAmp EXG Pill Sensor is used to acquire EOG signals of left and right eye movement, as well as up and down eye movement. The signals are processed using an ESP32 microcontroller and Arduino IDE, and an algorithm is created to analyze the observed ranges and generate text and voice-based outputs. The accuracy of the system was tested by asking 10 healthy participants to perform each of the four types of motions ten times, and the results showed an overall accuracy of 81.04%. The system involves detecting EOG signals using sensors that are placed around the patient\u27s eyes, and the text-based output is displayed on an LCD screen, while the voice-based output is played on an MP3 player. The output is then displayed on an application enabling communication with the patient remotely, potentially improving the quality of care and increasing the patient’s sense of security. Future developments could include increasing the degrees of motion and addition of an eye-blink sensor for more convenient user experience. This project provides a valuable solution for quadriplegic patients, enabling them to communicate effectively and empowering them with a sense of independence. However, further research and testing are needed to fully evaluate the efficacy of the system on actual quadriplegic patients.
The Role of AI Chatbots in Revolutionizing Gaming Experiences - A Survey
No full textThis paper investigates the integration of artificial intelligence (AI) chatbots into video games to enhance player experiences. In the realm of modern entertainment, video games often grapple with the constraint of linear storytelling, limiting replayability and the diversity of player interactions. Addressing this challenge, the study advocates for the adoption of AI chatbots, offering a detailed analysis of their features in comparison to traditional chatbot systems. The literature review explores recent research on AI chatbots in gaming, emphasizing their impact on transforming storytelling and fostering stronger connections with players. Ethical considerations related to data access and job displacement concerns are highlighted, emphasizing the necessity for player-centric development. Through a combination of comprehensive literature review and practical analysis of free and open-source games, the research assesses key features, including game type, engine, platform, AI capabilities, player engagement, NPC (non-playable character) behavior, visuals, and identified issues. The comparative analysis distinguishes the strengths and weaknesses of AI chatbots and traditional counterparts in gaming, providing valuable insights for developers aiming to create more immersive and dynamic gaming experiences
AnnoVate: Revolutionizing Data Annotation with Automated Labeling Technique
Get PDFThis research introduces AnnoVate, an innovative web application designed to automate the labor-intensive task of object annotation for computer vision applications. Focused on image annotation, the study addresses the escalating demand for data refinement and labeling in the field of artificial intelligence (AI). Leveraging the power of YOLOv8 (You Only Look Once), a high-performance object detection algorithm, AnnoVate minimizes human intervention while achieving an impressive 85% overall accuracy in object detection. The methodology integrates active learning, allowing labelers to selectively prioritize uncertain data during the labeling process. An iterative training approach continuously refines the model, creating a self-improving loop that enhances accuracy over successive loops. The system\u27s flexibility enables users to export labeled datasets for their preferred AI model architectures. AnnoVate not only overcomes the limitations of traditional labeling methods but also establishes a collaborative human-machine interaction paradigm, setting the stage for further advancements in computer vision
Evaluating Congestion Control Methods for enhanced Throughput
Get PDFWith easy access and many services such as social networks, online shopping, video streaming the data traffic over the Internet is increasing. On the other side, the traditional congestion control strategies of TCP due to the huge data are not sufficient. The TCP protocol uses such traditional techniques to minimize the network congestion. Moreover, handling applications with smartphones is challenging in terms of congestion due to the long delay networks such as 4G. Many TCP variants have been proposed for the network congestion particularly for long delay networks such as TCP (Binary increase congestion control) BIC and CUBIC. These proposed techniques for TCP are also not perfect and they require extensive experimentation over long delay networks. In this research, the performance evaluation is accomplished between three variants of TCP for network congestion i.e., TCP, BIC and CUBIC. Particularly the simulations are proposed in order to trace the throughput and fairness of these congestion control techniques. Over a number of simulations it is concluded that under the proposed topology TCP CUBIC improves the throughput and increases the bandwidth fairness approximately by 38% when compared with the basic TCP
Issues, Challenges, and Solutions in Data Acquisition in Virtual and Augmented Reality Environments
Get PDFThis paper looks at some of the challenges associated with data acquisition in VR and AR environments, principally by incorporating the privacy of digital forensic and sensor technology. While VR and AR technologies are mainly seen as providing an immersive experience, they also pose significant challenges in collecting data and protecting data collected in environments for privacy. It will look into advanced sensor technologies of high-resolution cameras, inertial measurement units, and biosensors for data accuracy and efficiency. This further researches the methods of data fusion, in particular, Kalman filtering and machine learning-based fusion. Lastly, the role of edge computing in local data processing to reduce the demands for latency and bandwidth is analyzed to allow for real-time processing. It also discusses privacy-enhancing technologies, such as differential privacy and homomorphic encryption, to ensure the protection of user data while maintaining ethical standards. The present article is aimed at implementing a comprehensive framework integrating these technologies to address both technical and moral problems associated with data acquisition through VR and AR for secure and efficient application in these fields