Turkish Journal of Computer and Mathematics Education (TURCOMAT)
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Efficient VLSI Implementation of Hybrid LDPC-STBC Codes for Enhanced Satellite Communication Systems
No full textSatellite communication systems play a pivotal role in facilitating global connectivity, necessitating the development of robust error correction codes to ensure reliable data transmission. In contemporary communication systems, the demand for higher data rates and improved spectral efficiency has led to the integration of advanced error correction techniques. However, existing systems often face challenges in striking a balance between complexity, power consumption, and performance. This work addresses the limitations of current systems by proposing a hybrid approach that combines the advantages of LDPC (Low-Density Parity-Check) codes and STBC (Space-Time Block Coding) techniques. The hybrid LDPC-STBC codes aim to enhance the error correction capabilities of satellite communication systems, ensuring robust data transmission in the presence of channel impairments. Despite the advancements in error correction coding, current systems encounter drawbacks such as increased computational complexity, higher power consumption, and potential performance degradation under adverse channel conditions. The proposed hybrid LDPC-STBC codes mitigate these issues by leveraging the strengths of both coding schemes, achieving a synergistic balance between error correction performance and computational efficiency
EXPLORING THE MODERN MYTHOLOGY: ANALYSING KAVITA KANE’S KARNA’S WIFE: THE OUTCAST’S QUEEN AND SITA’S SISTER
No full textIn modern-day literature, the study of mythology has presented authors with a vast and intricate framework to craft elaborate narratives that delve deeply into the intricacies of human behaviour, interpersonal connections, and societal standards. This research paper embarks on a profound and comprehensive analysis of two compelling literary works by the esteemed author Kavita Kane, explicitly focusing on Karna’s Wife: The Outcast’s Queen and Sita’s Sister. Through a meticulous and rigorous examination of the character progression depicted in Karna’s Wife, the primary objective of this study is to uncover and elucidate how Kane adeptly illustrates the transformation of Uruvi’s character, the significant relationships that shape her journey, and the innovative perspective she brings to the enigmatic persona of Karna. Simultaneously, the scrutiny of themes and symbolism in Sita’s Sister delves deeply into the core thematic foundations of the novel, the symbolic elements skilfully utilised by Kane to enhance the storyline, and the crucial influence of familial and societal expectations in moulding the characters within the narrative. By closely analysing these critical components, this research paper strives to illuminate the intricate layers of storytelling and the contemporary reinterpretation of classical mythology in Kane’s literary creations, thereby contributing to the enhanced comprehension of the enduring fascination of mythology within present-day literature
Sentiment Analysis of Arabic Tweets: Detecting Revilement
No full textSocial media systems play an necessary function in shaping public opinion and reflecting societal sentiments. This study focuses on sentiment analysis in Arabic tweets with a particular focus on offensive or offensive content. The aim of this research is to boost a dependable sentiment evaluation model that can accurately classify Arabic tweets as positive or negative, with a particular focus on identifying offensive language. A multinomial Naive Bayes classifier is trained on pre-processed data to perform sentiment classification. The classifier is fine-tuned to differentiate between positive and negative emotions, with a particular focus on identifying offensive or swearing language. The model is evaluated the usage of a complete set of metrics along with precision, precision, recall, and F1score. Experimental consequences point out promising overall performance of the developed sentiment evaluation model. The model achieved an accuracy of 93%, effectively classifying Arabic tweets into effective and bad sentiments. The precision, recall, and F1-score metrics similarly validate the model\u27s capacity to precisely become aware of revilement and offensive language. These outcomes spotlight the conceivable of the proposed strategy in successfully examining Arabic tweets for sentiment and offensive content, contributing to higher grasp on line behaviors and sentiments in the context of revilement
RADIAL BASIS NEURAL NETWORK FOR THE SOLUTION OF OPTIMAL CONTROL PROBLEMS VIA SIMULINK
No full textThis study uses radial basis neural network to solve optimum control problems via simulink. Using Pontryaginí\u27s principle, the optimal control problem\u27s optimum system is constructed. MATLAB is used to simulate the optimality system and generate the simulink architecture for the trial value. A radial basis neural network is then used to train the system and produce the optimal solution. This approach\u27s effectiveness is evaluated using a few control problems, and it is shown to be effective because of the reliable, accurate, and consistent results that are produced. The performance of this strategy is superior to that of other approaches
A Review on Breast Cancer Prediction Using Machine Learning and Deep Learning Techniques
No full textBreast cancer is one of the most prevalent and chronic disease that affect women. To overcome this disease, effective medical treatment is required. Early detection of the disease plays an important role for suitable medication and survival of patient. To identify the breast cancer in the patients, standard imaging technique mammography is used. Due to the subtle and varied nature of cancer tissues interpreting mammogram images can be a challenge to doctors. Machine learning (ML) and Deep Learning (DL) techniques offer promising solutions that provide efficient breast cancer detection from mammograms. In this review paper a comprehensive review of ML and DL algorithms and their applications in mammogram image analysis are presented. Various supervised and unsupervised learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), random forests, and other popular ML and DL models are discussed in paper. The integration of these DL methods that are efficiently used in image preprocessing techniques, feature extraction, and classification strategies. The overall survey focusses on various performance metrics, datasets, and benchmarks used in existing studies. Further the strengths and limitations of different approaches used by various researchers are identified. By understating current research trends this paper aims to contribute to the ongoing development of more accurate and reliable breast cancer detection systems using advanced ML techniques
CNN2D Algorithm for Detection of Ransomware Attacks Using Processor and Disk Usage Data
No full textCommonly, ransomware encrypts data, turns off antivirus protection, and destroys the target computer and everything on it. The techniques used today to detect this kind of WannaCry include monitoring the files, system requests, and processes on the system that is being targeted and analysing the data collected. Monitoring numerous processes has a substantial overhead; more current ransomware may interfere with the monitoring and alter the collected data. A dependable and practical technique for locating ransomware operating within a virtual machine, also called a VM, is provided in this study. We construct a framework for detection by applying an automated machine learning (ML) evaluation to the whole virtual machine (VM) using data collected from the physical host computer pertaining to specific processors and disc I/O events. This approach eliminates the need to continuously watch every action on the system that is being targeted and lessens the likelihood that ransomware would contaminate data. It also endures shifts in the amount of labour that users must do. It provides fast and very likely detection of known ransomware (used to train this machine learning model) and also of unknown ransomware (not utilised for teaching the model). Out of the seven artificial neural network classifiers that we looked at, the randomly generated forest (RF) classification gave the best results. Across six different customer loads plus 22 instances of ransomware, the RF model detected malware with a 0.98 confidence in 400 milliseconds
CHRONIC KIDNEY DISEASE STAGE IDENTIFICATION IN HIV INFECTED PATIENTS USING MACHINE LEARNING
No full textOne of the leading causes of illness and mortality in the world\u27s medical communities is chronic kidney disease (CKD). Patients often misdiagnose CKD since there are no symptoms in the early stages of the illness. Individuals living with HIV are more likely to develop critical care kidney disease (CKD). Early diagnosis of CKD prevents the illness from worsening and enables patients to get treatment more quickly. The application of machine learning algorithms for illness categorization and prediction in healthcare has increased due to the availability of pathology data. The categorization of CKD using machine learning models is presented in this research. For individuals with CKD, the CKD stages are also determined based on the glomerular filtration rate. The DNN model performs better, diagnosing CKD patients with HIV with 99% accuracy.
 
Applying Software Engineering Based on Peer to Peer Communication
No full textThe JavaScript programming language was selected to create software creation that facilitates the creation of a video connection between users because it enables the creation of cross-platform apps relatively quickly. For example, “Web Real-Time Communication (WebRTC)” standards do not specify exactly how two browsers initiate and manage communication with one another. The reason for this is that WebRTC does not specify the signaling technique or protocol. This paper\u27s main goal is to design and construct a WebRTC simultaneous video conference between peers utilizing Google Chrome and the Socket.io signaling technology. A Local Area Network was used in this experiment (LAN). Furthermore, an assessment was conducted on the quality of experience (QoE), the Socket.io signaling method, and resources, including bandwidth consumption. This paper describes the simultaneous execution of peer-to-peer video calls with user identification (user-id)
From Detection to Prediction: AI-powered SIEM for Proactive Threat Hunting and Risk Mitigation
No full textThe evolution of cybersecurity has witnessed a transformative shift from reactive defense measures to proactive threat-hunting and risk-mitigation strategies. In response to the rapidly evolving threat landscape, the integration of Artificial Intelligence (AI) into Security Information and Event Management (SIEM) tools has emerged as a crucial solution. Historically, SIEMs primarily aggregated security data but struggled to analyze the vast, complex datasets effectively. The integration of AI, especially Machine Learning (ML) and Deep Learning (DL), revolutionized these systems. AI algorithms enable SIEMs to extract meaningful insights from massive datasets, allowing for the identification of subtle anomalies and hidden threats that may not be detected by traditional detection methods. This transition marks a fundamental shift from simple data aggregation to intelligent analysis, empowering SIEMs to move beyond detection towardproactive threat hunting. This paper highlights the role of AI in predicting threats, leveraging historical data to forecast potential risks, and continuously learning to adapt to evolving threat landscapes. It also explores the real-world use cases of AI-powered SIEMs in proactive threat hunting and risk mitigation
AUTOMATIC CLASSIFICATION AND DETECTION OF COUNTERFEIT BANKNOTES BASED AI
No full textOn the basis of the look, people can easilydifferentiate banknotes and coin denominations. The coincurrencies can be identified visually impaired people basedon touch, but the note currencies cannot be identified easilyas it has similar texture and appearance, it can be challengingfor visually challenged people to distinguish the currencies. Demonetization has boosted the availability of fake cash inrecent years. People face difficulty in distinguishing betweenreal and fake banknotes because they are unaware of thesecurity elements utilized in modern currencies. Additionally, these fake cash mislead persons who don’t haveproper vision. So, it becomes important to identify thedenominations and detect fake and real banknotes in-orderto avoid the problems caused due to these currencies orbanknotes. This issue highlights the requirement for anaccurate banknote identification model. By spotting thecounterfeit currency, inflation and currency devaluation canbe stopped. The suggested model aims to identify thedenomination and categorize if a money note is real orfraudulent. The banknote denomination is determined using the machine learning algorithms