Turkish Journal of Computer and Mathematics Education (TURCOMAT)
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COMPUTING THE PLANKTON-OXYGEN DYNAMICS MODEL USING DEEP NEURAL NETWORKS IN THE CONTEXT OF CLIMATE CHANGE
No full textThis study proposes a novel approach to computing the plankton-oxygen dynamics model using deep neural networks (DNNs) within the context of climate change. By leveraging advanced computational methods, particularly deep learning algorithms, we aim to enhance our understanding of how plankton populations and oxygen concentrations interact in response to changing environmental conditions. The integration of DNNs offers several advantages, including the ability to capture complex nonlinear relationships and patterns from large datasets, making them well-suited for modeling dynamic systems such as aquatic ecosystems. By training DNNs on observational data and environmental variables, we can develop predictive models that simulate the behavior of plankton-oxygen dynamics under different climate scenarios. This research builds upon existing studies in ecological modeling and deep learning techniques to advance our knowledge of plankton-oxygen dynamics and their implications for ecosystem resilience in the face of climate change. By computationally modeling these dynamics, we can gain valuable insights into the mechanisms driving ecosystem responses to environmental stressors and inform conservation efforts and policy decisions.
 
Design of Reconfigurable Logic Block Based Sequential Circuits Using Look Up Table Logics
No full textReconfigurable sequential circuits find applications in various digital systems, including communication networks, data processing units, embedded systems, and FPGA-based designs. Their ability to adapt and reconfigure their functionality onthe-fly allows them to accommodate dynamic requirements and optimize the use of hardware resources. Traditional implementations of sequential circuits involve static configurations, where the logic and functionality are fixed during synthesis. While these methods are straightforward to design and implement, they lack adaptability and cannot be modified without redesigning the entire circuit. The proposed method involves the utilization of a dedicated Reconfigurable Logic Block (RLB) within the sequential circuits, allowing for dynamic configuration changes without altering the overall circuit structure. The RLB can be programmed to provide different logic functions using look up tables, multiplexers, enabling the sequential circuit such as counters and shift registers to change its behaviour
PREDICTING FIRE ALARMS USING MULTI SENSOR DATA: A BINARY CLASSIFICATION APPROACH
No full textFires pose significant threats to human life, property, and the environment. Early detection of fire incidents is crucial to prevent extensive damage and to ensure the safety of occupants. Traditional fire alarm systems typically rely on a single type of sensor, such as smoke detectors or heat sensors, to detect specific fire indicators. These systems operate based on predefined thresholds and triggers. However, they can be prone to false alarms triggered by non-fire-related events (e.g., cooking fumes or dust) and may not provide early warning signs in certain scenarios. To address these limitations, researchers and engineers have turned to advanced technologies, such as multi-sensor data analysis and machine learning algorithms, to develop more reliable and efficient fire alarm prediction systems. On the other hand, the need for a more robust and accurate fire alarm prediction system stems from the shortcomings of traditional methods. False alarms not only lead to wasted resources but also desensitize occupants, potentially leading them to ignore genuine alarms. Additionally, a delayed response to a fire incident can result in severe consequences, making it essential to develop an intelligent system that can effectively and timely predict fire events. Therefore, this work presents the utilization of multi-sensor data and binary classification to develop a more reliable fire alarm prediction system. The experiments are conducted using a dataset collected from various sensor inputs, including air temperature, humidity, CO2 concentration, molecular hydrogen, ethanol gas, and air pressure etc. Then applied binary classification algorithm to learn patterns from the data and classify fire-related events accurately. The results showed promising improvements in prediction accuracy, reduced false alarm rates, and early detection of fire incidents
REDUCTION JAMMER DETECTION AND RECOVERY ALGORITHMS FOR DSRC SAFETY APPLICATION IN VANET
No full textIntelligent Transportation Systems (ITS) encompasses technologies, services, and applications facilitating communication between vehicles (V2V) and between vehicles and fixed infrastructure (V2I and I2V). This mutual interaction constitutes a Vehicular Ad-Hoc Network (VANET) which supports a plethora of applications targeting critical transportation aspects, such as safety, mobility, and environmental considerations. Dedicated Short Range Communications (DSRC), operating on the 5.9 GHz band, is pivotal for such exchanges. We introduced an innovative algorithm designed to identify jamming attacks and transition the Safety Application to a secure fail-safe mode. This algorithm leverages a dual-metric strategy, incorporating both distance and PDR measurements. Field tests confirm that our algorithm adeptly recognizes the activities of deceptive jammers, ensuring a prompt shift of the safety application into its fail-safe state. This paper delves into these countermeasures, evaluating their efficiency via mathematical modeling, simulations, and on-ground testing. Findings acknowledge that these strategies bolster the reliability of safety applications in jamming scenarios. Furthermore, the approaches propounded align with ongoing standardization endeavors by relevant authorities, ensuring communication mediums remain unhindered.
 
RUNGE–KUTTA LIKE METHOD FOR THE SOLUTION OF OPTIMAL CONTROL MODEL OF REAL INVESTMENT AND FISH MANAGEMENT
No full textThis study develops the Runge-Kutta Like Method (RKLM), which uses Pontryagin\u27s principle to solve optimal control problems numerically using forward-backward sweep methods. It is based on the Patade and Bhalekar methodology. The RKLM\u27s stability properties and its convergence are examined. The Forward-backward sweep algorithm and the RKLM algorithm are implemented using MATLAB code. Physical optimum control problems are solved with the RKLM. The first problem\u27s conclusion demonstrates that, when investment declines, the capital first grow to boost production before it depreciates. The outcome of the second problem demonstrates that a larger weight parameter causes the harvesting rate to reach zero more quickly and the total fish mass to reach its maximum level more quickly. The findings obtained demonstrate the effectiveness of using RKLM in conjunction with forward-backward sweep methods to solve optimal control problems
USING MULTI-CRITERIA DECISION MAKING METHODOLOGIES FOR STRATEGIC SUPPLY CHAIN DESIGN CHOICES; SELECTION OF THE BEST TRANSPORTATION SETUP AND DISTRIBUTION NETWORK EXPANSION PLAN
No full textSupply chain is considered as one of the key areas of companies’ success and it should be designed appropriately to be compatible with companies’ objectives and strategies. In this thesis, a case study of supply chain network redesign in oil and gas industry will be thoroughly studied, analyzed and concluded. This research has been applied and deployed on an oil and gas local company called “X-LUBE”. In brief, Multi-Criteria Decision Making (MCDM) approach is used to come up with the best transportation setup for X-LUBE distribution network among five available alternatives which are compared using predefined criteria. Several MCDM techniques were used such as AHP, TOPSIS, PROMETHEE and ELCTRE. Similarly; AHP technique were used to determine the expansion plan and strategies of X-LUBE distribution network along with associated cost and fleet type selection
OPTION CHAIN DYNAMICS: ANALYSING OPEN INTEREST, TRADING VOLUME, AND LAST TRADED PRICE RELATIONSHIPS
No full textThis paper investigates the dynamic interplay between option chain metrics—specifically open interest (OI), trading volume, and last traded price (LTP)—within financial markets. Utilizing data spanning multiple market cycles, we explore how changes in these metrics influence one another and impact underlying asset prices. Drawing upon existing literature and empirical analysis, we seek to elucidate the predictive power of these indicators on market movements and volatility
ADVANCED AUTONOMOUS SURVEILLANCE ROBOT FOR ENHANCED MONITORING AND INDIVIDUAL IDENTIFICATION
No full textThe primary objective is to detect and identify suspicious activities and potential threats in a precise manner while prioritizing human safety leveraging surveillance technology and machine learning. The implementation of this system involves coding in Python using the OpenCV library. It utilizes Wi-Fi connectivity as a means of communication. The robot is equipped with a Raspberry Pi along with a USB web camera, which captures video footage and employs object detection algorithms to identify unknown individuals. When a person or an object is detected, the system sends an email to the dedicated email addresses including an image of the unrecognized individual. The proposed system is designed as a unified unit responsible for monitoring the environment for hazardous conditions and delivering real-time video feedback. The proposed system is simulated and tested in real-time to observe its functionality, and it is observed that the system works properly as per given input conditions
A COMPREHENSIVE ANALYSIS OF ENERGY EFFICIENT WIRELESS SENSOR NETWORK (WSN) PROTOCOLS
No full textWireless Sensor Networks (WSNs) include many tiny and low energy and cheaper nodes in different areas for measuring the environmental parameters like temperature and humidity. Hence, the usage of WSNs has become an adaptable means of data transfer across different fields due to the advancement of wireless technology. It is noted that these networks,which use nodes for event detection and identification, gather information by means of simple calculations and send it to a particular Base Station (BS) via gateway nodes. The applications of WSNs can be classified into monitoring and tracking,which are used in areas such as environmental monitoring, health diagnostics, military surveillance, and many others.nergy consumption is still a major issue of concern, sensor nodes in particular, use energy in sensing data and transmitting it. Energy savings techniques like clustering and routing protocols are important for increasing the life time of the network.hus, this research aims at analysing the different aspects of improving energy efficiency and network performance,especially in the context of the clustering algorithms, such as the Multi-level Stable and Energy Efficient Clustering (MSEEC) protocol. The paper discusses major issues and suggests some new combined routing approaches for WSNs with the problems connected with fault tolerance, topology, scalability, and power consumption
Machine Learning for Cloud-Based Privilege Escalation Attack Detection and Mitigation with CATBOOST
No full textThe exponential growth in attack frequency and complexity in the past few years has made cybersecurity a major concern with the advent of smart devices. Cloud computing has changed the way businesses operate, but users may find it more challenging to use dispersed services, such as security systems, due to their centralization. Organizations and cloud service suppliers exchange massive amounts of data, which poses a significant risk of accidental or intentional disclosure of sensitive information. Because of their increased access and potential to do substantial harm, an antagonistic insider poses a serious threat to the company. Only approved individuals within the organization have access to sensitive data and assets. This research details a machine learning-based strategy for classifying insider threats and finding out-of-the-ordinary events that can indicate privilege escalation security issues. The system uses a systematic approach to detect these irregularities. Machine learning and prediction accuracy are both enhanced by ensemble learning, which considers several models simultaneously. Using anomaly and weakness detection, some studies have attempted to identify security issues or hazards associated with privilege delegation in network systems. However, the assaults cannot be definitely identified from this research. Ensembles for machine learning (ML) are suggested and assessed in this research. The objective of this endeavor is to classify insider assaults using machine learning approaches. The dataset it uses has been modified from many files beneath the CERT dataset. The dataset is subjected to four machine learning techniques: Light GBM, XG Boost, Ada Boost, and three Random Forest (RF) methods. In terms of overall performance, light was superior. In contrast, RF and AdaBoost are two algorithms that may be better at preventing assaults from inside, such as attacks using behavioral biometrics. Consequently, it is possible that various internal threats may be better classified by combining various machine learning algorithms. With a 97% dependability rate, the Light GBM method outperforms the other suggested techniques; RF, AdaBoost, and XG Boost all have 88% accuracy rates