Iraqi Journal of Information and communication technology

High-density point clouds expressing attractive 3D images are attracting attention. These gigantic media require large bandwidth allocations, making them problematic to stream to resource-constrained hand-held devices. This paper proposes a method for point cloud compression and streaming of large point clouds using a web server. Storing large point cloud videos on a web server allows users to publish data sets without using additional applications or sending large amounts of data ahead of time. HTTP/2 improves transfer efficiency by compressing headers into binary format and reduces latency by sending many multiplexed requests using a single TCP session. The large size of the point cloud data and the current limitations of wireless channels demand point cloud compression methods. MATLAB is used for the purpose of analysis, at results show an average compression ratio of 30:1. Our proposed system achieves lower average latency between successive frames resulting in an increased frame rate to 72.46 fps when streaming 4M points, and 82.51 fps in the case of streaming 800K points compared to much lower rates in conventional work.

A software-defined network (SDN) based 5G architecture that utilizes three controllers to improve network performance is proposed in this paper. The controllers are responsible for managing functions related to access mechanism, core connectivity, and network transport. The proposed architecture aims to address solutions for the challenges of traditional 5G networks such as scalability, flexibility, and resource allocation. The paper investigates two networks: 5G without SDN and 5G with SDN. The performance of these networks is evaluated by simulation tests using OMNeT++. The results show a considerable improvement in performance measures such as throughput, packet error rate (PER), and energy consumption. Under favorite test conditions, improvements of46%, 88% and 94%, in throughput, PER, and energy consumption are achieved. The main finding of the paper is that the proposed SDN-based 5G architecture with multi-controllers is a promising arrangement to overcome the challenges of 5G networks

Due to a lack of resources and the tokenization issue, it is challenging to identify the languages inscribed in cuneiform symbols. Sumerian and six dialects of the Akkadian language-Old Babylonian, Middle Babylonian Peripheral, Standard Babylonian, Neo-Babylonian, Late Babylonian, and Neo-Assyrian-are among the seven languages and dialects written in cuneiform that need to be identified. This problem is addressed by the Cuneiform Language Identification task in Var Dial 2019. This paper presents ten machine learning algorithms derived from four types of machine learning that were used (supervised, ensemble, instance-based, and Artificial Neural Network) learnings. The Support Vector Machine (SVM), Naà ̄ve Bayes (NB), Logistic Regression (LR), and Decision Tree (DT) algorithms within supervised learning, the K-Nearest Neighbors algorithm (KNN) within instance-based learning, the Random Forest (RF), Adaptive Boosting (Adaboost), Extreme Gradient Boosting (XGBoost),and Gradient Boosting (GB) algorithms within ensemble learning. Also, one of the natural language processing algorithms, n-gram, is used to identify the cuneiform dialect. The best result belongs to an ensemble of Random Forest classifiers working on character-level features with a macro averaged F1 score of 96%, and the best outcome for the n-grams algorithm is 0.82% of di-gram

The iris of the eye is used as an identifying and affirmative biometric in many vital applications such as banking and airports. Iris identification is one of the most reliable and accurate biometric identification systems available due to its stability and lack of sensitivity to longevity. In this article, authorized standard processes such as iris segmentation, normalization, and feature extraction were utilized to examine the iris of the eye. The circular hough transform is used for iris segmentation, while the daughman rubber-sheet model is used for iris picture normalization. Using a combination transform consisting of four consecutive transforms, feature encoding was employed to restore the most distinguishing aspects of irises. The 2-D fast fourier transform (FFT), radon transform, 1-D inverse fast fourier transform (IFFT), and 1-D discrete multi-wavelet transform are all examples of transforms. For pattern clustering, the kohonen self-organizing feature map (SOFM) was employed to the group that distinguished information vectors of the iris image features extracted using the combination transforms in order to visualize the close proximity of comparable irises features and scatter dissimilar ones. The matching process is carried out between test iris images and target iris photos of 224 people from the IITD database, with the similarity measured using the euclidian distance metric A part investigates the change of the parameters: False Rejection Rate (FRR), False Acceptance Rate (FAR), and Total Successive Rate (TSR) of different collections of the Person Input Database (PID) and Person Output Database (POD) with the threshold levels (90:10, 80:20, 70:30, 60:40,50:50) that resulted Equal Error Rate (EER) for the various thresholds are 0.070, 0.070, 0.080, 0.090 and 0.095respectively. The suggested approach yielded a TSR of 98% when compared to conventional methods

This paper intends to provide a better understanding of how these models produce predictions, particularly in complex medical diagnoses, and at the same time bridging the gap between technical model output sand clinical applications. This study addresses the critical problem of transparency of machine learning (ML) models in health care where interpretability is an essential aspect for ethical decision making and trust building. The goal of this paper is to present a clearer understanding of how these models generate predictions, especially in important fields like complex medical diagnoses, thereby bridging the gap between technical model outputs and clinical applications. The crucial issue of transparency in machine learning (ML) models within healthcare is addressed in this study where interpretability plays a vital role in ethical decision-making and fostering trust. The focus o f the research is enhancing model transparency by using SHapley Additive exPlanations (SHAP) and Deep Learning Important FeaTures (DeepLIFT), two crucial methods that are designed to elucidate the decision-making processes of ML models. This mode of approach helps to have more distributed comprehension of the decision pathways by models thus aiding in knowing how each feature contributed to the last prediction. It is this method that has been employed to showcase the efficiency of predicting melanoma and also diabetic retinopathy which are two vital medical diagnostic areas. In healthcare, SHAP along with Deep LIFT has improved the models’ explain ability and trustworthiness significantly and hence making them easy for those in the field. The advanced interpretability methods presented in this document enhances ML model transparency especially when dealing with health issues. As a result, interpretability becomes an even bigger issue and they are supposed to be able to use these tools for reliable and open decisions when it comes to medical specialists

In the contemporary digital age, the increasing complexity and frequency of cyber threats underscore the need for efficient network intrusion detection systems (NIDS). This paper provides a comprehensive comparative analysis of two prominent NIDS, Snort and Suricata, focusing on their architecture, detection capabilities, and performance metrics. It explores the historical development, operational frameworks, and technological foundations of these systems, highlighting their respective benefits and limitations in different network environments. Snort, known for its extensive rule-based detection, and Suricata, which leverages multi-threading for high-speed traffic handling, are evaluated based on specific security requirements, including traffic volumes, processing speeds, and threat types. The paper also discusses future advancements in NIDS, particularly through the integration of machine learning and AI, to enhance predictive and adaptive capabilities. This analysis aims to inform cyber security professionals about the qualifications and capabilities of Snort and Suricata, providing insights for their effective deployment in modern network security infrastructures. The discussion on future trends emphasizes the importance of continuous improvement in NIDS to address evolving cyber threats

Generative Adversarial Networks (GANs) have earned significant attention in various domains due to their generative model’s compelling ability to generate realistic examples probably drawn from sample distribution. Image security indicates the process of protecting digital images from unauthorized access, modification, or distribution. This requires a guarantee of image privacy, integrity, and authenticity to prohibit them from being exploited by malicious attacks. GANs can also be utilized for improving image security by exploiting its generation ability in encryption, steganography, and privacy-preserving techniques. This paper reviews GANs-based image security techniques providing a systematic overview of current literature and comparing the role of GANs in image encryption, image steganography, and privacy preserving from multiple dimensions. Additionally, it outlines future research directions to further explore the potential of GANs in addressing privacy and image security concerns.