International Journal of Artificial Intelligence & Mathematical Sciences

DeepPoseNet: A Comprehensive Study on Human Pose Estimation with Deep Learning Technique

Syed Mujtaba Haider — 2024-05-03

The article is a thorough analysis on the deep learning techniques used in image processing to estimate human poses. This entails examining several essential architectures such as CNNs and why traditional methods are unfit. It clarifies attentive mechanisms and transfer learning parts. This approach uses a two stage CNN model, whereby first network identifies some body parts, while the other focuses on these identified body bits. We use an intricate VGG16 to pinpoint body parts with accuracy. These models are compared using benchmark data sets and performance measures of special interest in the application of the MPII dataset for model training as well as verification. Deep pose estimation has huge social and economic consequences. They include human-computer interaction, sports analysis, healthcare, and many more. Conclusion gives an outline of important insights made above, highlighting positive aspects identified as well as gaps that require additional research including call towards cooperation between disciplines for enhanced growth in this field.

A Systematic Review on the Role of Blockchain Technology in Healthcare: Challenges and Solutions

Ruqiya Abbasi — 2024-05-03

Blockchain technology is used to record information in a way that makes it problematic or impossible to modify, hack or cheat the method. Blockchain has gained significant attention in various applications such as IoT, cybersecurity, finance, network data management, supply chain, and insurance. Many applications extend beyond financial services such as the healthcare industry. The healthcare industry has also adapted blockchain technology in its numerous sub-domains such as electronic health records (EHR), clinical research, genomic market and medical supply chain management, the healthcare system is gradually incorporating artificial intelligence (AI) into its systems, but it is not a one-size-fits-all solution these challenges. This research paper is conducted on a systematic literature review to discover the state-of-the-art research studies conducted on the issues of healthcare applications and different frameworks researchers suggested as their solutions through using blockchain technology. This paper also presents some challenges and future research directions, which can be develop with various methods of artificial intelligence and blockchain technology, as well as patients who can diagnose and treat using blockchain technology for safe and secure data sharing, it will transform healthcare systems with personalized, authentic and secure access to patient clinical data, and these data can be used for more health development and clinical research.

Look-Alike Face Recognition Using Deep Learning

Abdul Basit Mughal — 2024-05-03

The identification of similarities and differentiation between look-alike and non-look-alike features in facial images is a burgeoning research area, holding significant promise for applications in imposter identification. This task is complicated by variations in poses, illumination, and gestures within images of the same individual. Convolutional Neural Networks have emerged as a potent tool for face recognition, demonstrating notable efficiency. This research presents a novel methodology that combines image processing and CNN techniques to predict look-alike facial images with a high degree of accuracy. Achieving an impressive 95% accuracy on a dataset comprising 5000 images, our approach surpasses current cutting-edge methods in the field. In the realm of look-alike face recognition, the IMDB-wiki dataset serves as the foundational dataset. For face recognition, we employed various VGG models, such as VGGFace, VGG16, and VGG19. These models possess the capability to extract diverse facial features. To determine the similarity between two faces, we utilized methods such as cosine similarity, KNN (K-Nearest Neighbors) Euclidean distance, and Manhattan distance. This research contributes to the advancement of effective techniques in the evolving landscape of look-alike face recognition.

Analysis of Textual Feedback of Students for Course Evaluation in Universities Through Machine Learning Algorithms

Naseer Ahmed — 2024-05-10

Many educational institutions worldwide make significant efforts to collect student feedback to understand their perspectives on the courses and faculty. This feedback is used to enhance the institution's environment. In this modern world, institutions use data collection techniques to gather feedback. However, they lack the proper techniques to analyze and utilize this data to improve the educational quality of the institute using textual feedback. This study presents techniques for analyzing the written feedback from students, which was collected for course evaluation over a year. This paper focuses on techniques including Multinomial Naive Bayes Classifier, Long Short-Term Memory(LSTM), and Random Forest to enhance the outcomes of sentiment analysis. Ultimately, our efforts resulted in the LSTM achieving 97.45% accuracy during model testing for three types of sentiments: positive, neutral, and negative. This paper also aims to identify a clear research gap in this field and discusses the work of other researchers, including their less accurate models from the past. We also discuss the processes of collecting a sufficient amount of data to train this model, and then utilize a set of 25,689 data points for training. Furthermore, this paper primarily focuses on enhancing the quality of education. The initial model has been implemented at Balochistan UET Khuzdar, and it has produced satisfactory results. In the future, efforts will be made to find the perfect way to enhance the quality of education.

Towards Novel Free Space Propagation Model for Humidity Based Mediums in Wireless Communication Systems

Ahmed Faraz — 2024-05-13

: This Free space propagation model in wireless communication systems is widely used for calculating the power
received at the receiver side using different parameters in wireless communication systems which are power transmitted
by the transmitter side, transmitter antenna gain, receiver antenna gain, the transmitter-receiver separation by a distance
which is far located, system losses which are also known as path losses. The prominent characteristic of free space
propagation model is that this model is only used and applied for the airy medium and it does not cope with the signal
attenuation problems when the medium of transmission of signals change from air to humidity or rainy medium. In this
paper I propose a novel free space propagation model which will address and solve the signal attenuation problem when
the weather is humidity based or moisture based. The novel proposed model will be used to solve the issue of weak
signal propagation in the humidity based medium when there is a rainy season in the locations where signal propagation
takes place