ABSTRACT. The talk will be about latest technologies to be updated.

ABSTRACT. In this paper, a Q-switched fiber laser with dual wavelength operation has been developed by using a newly developed saturable absorber (SA) FIrpic. A stable dual-wavelength Q-switch pulse train was obtained as the pump power was increased within 35 mW to 213 mW. The output wavelength has two peaks located at 1558.1 nm and 1562.6 nm giving a spacing of about 4.5 nm. The dual wavelength operation was produced by using a microfiber in the fiber laser cavity. The pulse width was decreased from 8.4 µs to 3.6 µs While, the repetition rate increased from 32.15 kHz to 78 kHz as the input pump power increased.

ABSTRACT. In today’s changing world incorporating Internet of Things (IoT) technologies, into maintenance strategies is seen as a crucial innovation to improve business operations. This study delves into the use of driven maintenance systems and their notable impact on enhancing business efficiency cutting costs and ensuring operational dependability. By making use of real time data from sensors and devices these systems can predict equipment failures in advance reducing downtime and prolonging the lifespan of vital assets. The study provides a look at how maintenance strategies have evolved over time the technology behind powered predictive maintenance and how it revolutionizes business processes. The insights gathered from case studies and analysis of current executions reveals how business have transformed and achieved enhancement in operations. The paper also reflects challenges with their related solutions and suggested strategies to solve them. The findings show that implementing IoT for maintenance is not a technology hop but a necessity for companies struggling to stay in today’s competitive market. In addition to this it investigates developments and possible innovations in this area emphasizing the evolvement and rising significance of IoT, in the upkeep of business industry.

ABSTRACT. Software Defined Network (SDN) is taking over traditional networks for better performance, resource management, and lower network operating costs. In this paper, we discuss load balancing and energy awareness in SDN. We explore how these features correlate and minimize energy consumption and traffic congestion through traffic engineering. We use a round-robin load balancing technique and observe round trip time (RTT). TCP and UPD traffic flows are analyzed using parameters: transfer, bitrate, jitter, and packet loss. For energy consumption, we measure CPU utilization before and after load balancing. Results highlight that load balancing reduces RTT by up to 44.88%. Even distribution of load reduces CPU idle time to less than 90% and improves utilization. Furthermore, load balancing reduces jitter by 46% while maintaining bitrate.

ABSTRACT. Underwater Acoustic Sensor Network (UASN) is based on the sensory elements which detect the environmental conditions of submerged region and transmit the information through acoustic waves at the sink node placed on top of the water. All these sensory elements are battery operated just like the Terrestrial Wireless Sensor Network (TWSN). However, these nodes are not stationary, unlike the nodes in TWSN. Thus, routing with limited energy and ever-changing topology decreases the reliability of the network and increases the chances of Void existence. This research paper focuses on the comparative analysis by critically reviewing of some of the state-of-the-art routing protocols which are based on the effective usability of the limited energy resource, reliability of the network and Void occurrence issues. An analytical method is employed to examine the strengths and shortcomings of existing routing protocols, with a specific emphasis on the identified difficulties. It provides a solid foundation related to UASN routing protocols which are primarily designed with a focus on resolving issues such as limited energy, low reliability and high chances of void occurrence. The contribution of this comparative study helps the research partners from academy and industry which are new to this field of study and would like to contribute.

ABSTRACT. Sand-dust weather causes low contrast as well as color distortions in outdoor shots, which has a significant impact on outdoor vision applications, particularly on autonomous cars. This autonomous car system analyses still images (photos) in an offline manner. By processing these images offline, this proposed study aims to improve visibility in dusty conditions. An effective strategy for enhancing sand dust photos is provided in this study to mitigate the color cast and low contrast in the image produced by weather with sand and dust. In this proposed study, color compensation and color correction methods are used to enhance sand-dust images to a higher level. Prior to white balancing, a color compensation method is used to fix the initial color cast, which damaged the dusty image. In this proposed method, color compensation algorithm uses numerous yellow channel information produced by sand dust scattering to compensate for the blue and green channel information. While color compensation removes the sand-dust cast, it also reduces the image detail. After that, using color correction algorithm the obtained image is converted from the RGB to the HSV color space, and the new color divergence is avoided by using the CLAHE to raise the V component. In order to maintain edges and minimize noise, the Laplacian filter were used prior to converting the final image back to RGB. Finally, this proposed method exhibits a 1% higher mean value, a 1.13% higher standard deviation, and a 1% higher entropy compared to the existing methods used.

ABSTRACT. This study investigates the prediction of taxi trip durations in New York City using machine learning (ML) models and neural networks (NN). Three models Linear Regression, Random Forest Regressor, and a Neural Network were developed and evaluated on a dataset containing various trip details. The analysis highlighted the importance of geographic and temporal features in predicting trip durations. The Neural Network model demonstrated the best performance, capturing complex patterns within the data. The findings contribute to enhancing smart mobility solutions by providing accurate travel time predictions and supporting the development of more resilient urban transportation systems.

ABSTRACT. In recent years, there has been a growing interest in UWSNs as a viable networking strategy for use in a variety of aquatic settings. Improvements in data transmission efficiency and the practical use of routing systems are both made possible via the minimization of energy usage. Due to its fluid nature, limited bandwidth, quick energy depletion, and severe delay, Underwater Wireless Sensor Networks (UWSNs) deliver major hurdles for the development of routing systems. In this paper, we'll take a look at the different strategies to routing in UWSNs. There are now two main groups of routing protocols in use across the world, and each has its own route decision maker. The effectiveness of various routing options is then analyzed by means of a comprehensive review. Additionally, a thorough analysis is performed to investigate prospective areas for further study on routing methods in UWSNs.

ABSTRACT. Wireless Sensor Networks (WSN) are playing significant role in emerging technologies by enabling the real-time data collection and monitoring in several applications, such as environmental monitoring, industrial automation, and smart cities etc. Although wireless sensor networks (WSNs) have widely been deployed in numerous fields, they encounter significant challenges due to their limited bandwidth, memory, inherit dynamic nature, and the resource constrained environment. These challenges make the traditional traffic management techniques unfeasible for WSNs and results in traffic congestion, increases latency and makes data routing inefficient. These problems can be addressed by Software defined networking (SDN) approach as it implements centralized control and automates the network management using the programming techniques. This research investigates the performance and efficiency of software defined networks (SDN) in WSNs to optimize traffic management. The proposed framework consists of an SDN controller, shortest path routing algorithm and a grid topology to simulate the WSN environment. The paper evaluates the performance of SDN approach in WSNs for their traffic management, by comparing it with the traditional technique. Results and simulations with the improved latency, throughput and packet loss signify the potential of SDN to optimize the traffic management in WSNs.

ABSTRACT. Despite significant advancements, lung cancer remains a prevalent global health issue. Recent studies have highlighted alarming statistics, with approximately 200,000 new cases reported each year. This survey paper aims to analyze and synthesize the existing literature to identify current methods and models employed for the diagnosis and prognosis of lung cancer, while evaluating their strengths, limitations, and potential future developments. Early detection holds promise for improved prognoses in lung cancer patients. The methodology adopted involves an extensive review and summary of research, providing a comprehensive understanding of the methods and models utilized in lung cancer diagnosis and prognosis. The paper discusses recently proposed methods and models, including deep learning architectures such as CNN, Google Net, VGG-16, U-Net, 3D CNN, and RNN, as well as machine learning algorithms like XGBoost, SVM, KNN, ANN, Random Forest, and hybrid models. These approaches are employed in lung cancer detection through various applications, such as feature selection, nodule categorization, risk factor analysis, and early-stage prediction. The potential of these methods to enhance lung cancer detection and prognosis is emphasized in this study. Deep learning architectures have demonstrated high accuracy in identifying abnormal lung tissue development from CT images and classifying lung nodules, while optimization models and machine learning algorithms have shown improved prognoses using microarray datasets. However, certain limitations have been identified, including the requirement for robust computational resources, challenges with non-numerical features, overfitting, and extensive training cycles. This publication highlights several research gaps that need to be addressed, including the interpretability of deep learning models, validation of proposed approaches in real-world scenarios, and the integration of diverse techniques. The findings of this study can benefit researchers, healthcare professionals, and policymakers involved in lung cancer detection and prognosis. By highlighting the potential of different methods, recognizing their limitations, and suggesting avenues for further research, this work aims to contribute to the battle against lung cancer and improve patient outcomes.

ABSTRACT. Today, image processing plays a significant role in early disease detection, allowing doctors to treat conditions like breast, lung, and brain malignancies. A brain tumor is a serious condition that can be fatal. Brain tumor diagnosis is the most important task for early therapy, and automatic visual classification might be extremely important. Itis essential to classify brain cancers in order to access the tumor’s and determine the best course of treatment for each class. To find brain tumors, numerous imaging modalities are used. However, MRI is frequently utilized since it produces superior images and uses no ionizing radiation. This research aims to provide a review of different Machine Learning (ML) techniques for detection and classification of brain tumor. Also different feature extraction techniques are analyzed and their benefits are discussed. This review is conducted by different research papers which helps new researchers to start immediately a literature review and knowing different machine learning techniques their advantages and disadvantages to classify brain tumor through MRI. Also they know the accuracy of each algorithm which leads them to directly target the problem not implementing the existing techniques.

ABSTRACT. Early detection of Acute Lymphoblastic Leukemia (ALL) is critical for effective treatment and improving patient outcomes. While effective, traditional diagnostic methods are time-consuming and subject to human error. Advances in machine learning have introduced automated, accurate, and efficient diagnostic tools that can analyze medical images and data, significantly enhancing early detection capabilities. This study evaluates the effectiveness of three pre-trained models—Inception V3, VGG-16, and VGG-19—for early detection of acute lymphoblastic leukemia (ALL) using machine learning models. Inception V3 consistently outperformed the VGG models across all metrics, achieving superior accuracy and computational efficiency. The study highlights the potential of Inception V3 for clinical deployment due to its robust architecture and efficient computation, while VGG models offer simplicity and adaptability.

ABSTRACT. Non-orthogonal multiple access is an attractive radio access solution for cellular networks to assist Internet of Things devices due to massive connectivity, higher throughput and low latency. In this work, a user selection strategy is proposed based on subscribed and non-subscribed user scenario while the analysis has been carried out on the harvested energy of each user in the considered Rayleigh fading based downlink multiple-input single-output system. Specifically, we have considered two performance metrics namely, outage probability (OP) and energy harvesting (EH) capability. We demonstrate that the user proposed user selection scheme is efficient both in terms of OP and EH. Finally, Monte-Carlo simulations are done to validate the expressions obtained for OP and energy against different parameters.

ABSTRACT. The rapid evolution of communication technology has led to a sharp emergence of computational intensive and delay-sensitive applications. However, the capabilities of Mobile Devices (MDs) are constrained by their computing power and battery capacity, which pose limitations on these functions. Mobile Edge Computing (MEC) can efficiently offload computations to meet application requirements and reduce the load on MDs. In scenarios with multiple servers and tasks, selecting the optimal components for offloading becomes complex because of the needs to balance data transfer volume and communication latency, posing challenges for effective computation offloading strategies. To address these challenges, we propose an Energy Efficient Deep learning-based Parallel Tasks Offloading Technique (EEDPTOT). Here, we use deep learning (DL) algorithms to train a decision-making system. This system selects the best combination of application components considering various factors, such as energy consumption, network conditions, computational load, data transfer volume, and communication latency. We develop a comprehensive cost function that incorporates all these factors and compute the cost for every possible combination of component offloading policies. By analyzing an extensive dataset, we identify the optimal policies. Additionally, we employ a DL network as an efficient alternative for this computational task. Simulation results demonstrate that our proposed approach achieves most favourable outcomes by minimizing both latency and energy consumption.

ABSTRACT. This paper addresses the PV module’s parametric estimation challenge using the information of manufacturer’s datasheet. A cost function is created to estimate the design parameters. Quadratic spline interpolation (QSI) is employed for a mono-crystalline Sanyo-HIT215 solar panel and its performance is compared with crow search algorithm (CSA). The results are presented in tabular form along with a I-V characteristics over different values of operating temperature. Such fine tuning of the mathematical models of PV cell through optimization algorithms has an added advantage in the digital twin environment to better reflect the real-world characteristics of a solar cell and ensure that the predictions and simulations from digital twin closely match the actual operating data.

ABSTRACT. There is an urgent need for improved security measures in Flying Ad hoc Networks (FANETs) to secure sensitive data transmissions. Moreover, there is a notable lack of surveys or reviews in this domain. This study examines the effectiveness of signcryption, a novel cryptographic technique that combines encryption and digital signature features, in mitigating security vulnerabilities in FANETs’ communication channels. Through a review of recent advancements and a comparative assessment of signcryption solutions, this study provides valuable insights into bolstering the security resilience of FANET networks. By highlighting key findings and future research directions, this review contributes to the ongoing efforts to enhance the security posture of FANETs networks and ensure the confidentiality, integrity, and authenticity of transmitted data.

ABSTRACT. Magnet Resonance Imaging (MRI) is a medical imaging technique used to produce high-quality images of human body organs. MRI is mostly used to analyze brain images for diagnosis of brain tumors. MRI images suffer from noise and low varying contrast. The noise is the main factor that degrades the image quality and affects the contrast of the image. The image enhancement technique is proposed, and it contains only three stages. The first stages handle the noise level of MRI brain images by using a sub-space least square estimator, the second stage is for contrast normalization by using the Retinex algorithm and the third stage is for contrast enhancement by using independent component analysis. The Oasis image database is used and contains data from 38 patients and three sets of images views of each patient as axial, coronal, and sagittal images. Peak signal-to-noise ratio (PSNR) and contrast improvement ratio are used as evaluation parameters. It is observed that the axial image database and coronal gave an efficient contrast improvement ratio of 1.4 at 2.5db and 1.29 at 2.29db respectively as compared to sagittal image view 1.18 at 2.35db.

ABSTRACT. Cancer, characterized by uncontrolled cell proliferation, is a significant global health problem. Breast cancer, the most common cancer among women, can lead to reduced mortality rates through early detection. Medical imaging plays a crucial role in the identification and diagnosis of breast cancer by providing vital diagnostic information. This article provides an overview of recent advances in the field, focusing on using machine learning (ML) and deep learning (DL) techniques for breast cancer detection. It reviews breast cancer classification using various ML and DL methods in different imaging modalities, analyzing the differences between these modalities using datasets from multiple studies. Finally, the article discusses the challenges related to the classification and detection of breast cancer, highlighting the effectiveness of various approaches in this area.

ABSTRACT. This study investigates the application of machine learning models in forecasting weather patterns, concentrating on the distributions of temperature, humidity, and precipitation in the Pakistan region. The research forecasts weather conditions using a variety of methods, such as logistic regression, decision tree classifiers, random forest classifiers, and support vector classifiers, using data from the Open-Meteo weather API for the years 2020 to 2024. Important factors impacting weather forecasts are identified using feature significance analysis with a Random Forest classifier. The study illustrates the potential of machine learning in weather forecasting using visualisations of temperature, humidity, and rain distributions for the cities of Lahore, Peshawar, and Karachi. It also projects the amount of rain that will fall in each city over the next five years, highlighting the significance of region-specific weather prediction models. The results highlight the usefulness of machine learning, in particular the Random Forest model, in improving the accuracy of weather predictions and provides information for guidance in agricultural planning and disaster management in Pakistan's various climate zones.

ABSTRACT. Abstract— The field of the tissue engineering has witnessed remarkable-progress in recent years, largely propelled by the convergence of the smart biomaterials and AI-driven methodologies. Biomaterials with their great ability to interface with the biological systems but also have revolutionized tissue engineering by providing the scaffolds that mimics the native extra-cellular matrix. Concurrently, the well-known AI-artificial intelligence has become a formidable tool for the data analysis, prediction, and an optimization. Assimilating the vast data-sets from the genomics, proteomics, and the clinical studies, AI-driven algorithms guide biomaterial-designs patient specific treatments strategies, and the diseases modeling. This review paper systematically examines recent studies and developments where the synergy of the smart biomaterials and AI-driven approaches have contributed to the tissue engineering advancements. Moreover, this study-paper highlights how AI-algorithms deciphers the complex biological data-sets, offering insights into disease mechanisms and aiding in the customization of biomaterials-based interventions. This as-well under-scores their collective potentials to re-define the landscape of tissue engineering, creating personalized treatments, accelerating drug discovery, and ultimately improving the patients outcomes.

ABSTRACT. The history of AI began in 1938 with the development of the Turing bombe by Alan Turing, followed by the Turing Test. Turing's work raised the question of whether machines can think, sparking extensive research. The progression of AI continued with the introduction of LISP in 1958 and Expert Systems in the 1960s. Technological advancements, such as, computing power, networking, and the rise of machine learning led to AI's rapid development. Today, AI is widely applied in various fields. This paper comprises an introduction, a literature review, a proposal for an Ethical Framework for AI development, and a conclusion. The introduction provides a concise history of AI, advance of AI, and delves into terms ethics and culture. The literature review examines different areas of applications of ethical AI and AI Ethics. Subsequently, a unique framework for ethical considerations in AI is suggested, which concludes the paper.

ABSTRACT. Urdu, spoken by over 100 million people worldwide, exists in two primary written forms: the traditional Urdu Nastalique and the increasingly popular Roman Urdu, driven by social media use. This paper addresses the lack of a comprehensive framework for converting between these two variants, identifying the flaws in current transliteration systems and the absence of a standardized dictionary for Roman Urdu. A detailed literature review reveals significant research gaps in natural language processing (NLP) for Urdu, especially in the domains of sentiment analysis, machine translation, and automatic speech recognition. This study aims to develop a robust methodology for the interconversion of Urdu Nastalique and Roman Urdu, considering the linguistic challenges posed by non-standardized spellings and morphological complexities. By creating an extensive corpus and employing advanced NLP techniques, this research seeks to bridge the gap between the two variants, enhancing the usability and visibility of Urdu in digital communication. The proposed framework not only addresses the transliteration issues but also sets the stage for future research in Urdu NLP, promoting a standardized approach to Roman Urdu. The paper concludes with a proposed methodology for data collection, sample selection, and analysis, contributing to the development of a comprehensive Roman Urdu dictionary and improving the accuracy of transliteration systems.

ABSTRACT. This research presents a strategy to optimize performance of the conventional power grid systems incorporating the photovoltaic and wind energy sources using Dynamic Economic Load Dispatch (ELD). The proposed model tackles the uncertain and variable characteristics of wind and solar power outputs by accounting for disparities between observed and forecasted power outputs leveraging GAMS-based optimization. The approach seeks to reduce the overall operational expenses of generators while effectively distributing power generation to committed units. This methodology is applied to a power system incorporating the solar and wind plant linked to 3 and 6 generating units, respectively. The outcomes of simulations carried out in GAMS substantiate the efficacy of the proposed approach.

ABSTRACT. The stability of energy is at risk due to the growing demand for reliable electricity supply. The share of primary energy usage in the residential sector is very high and contributes 1/3rd of the global greenhouse gas emissions. A major portion of the building’s energy is consumed for space heating and cooling of the building to attain thermal comfort. Around 1.9 billion air condition systems were used in 2019 to fulfill the requirement of thermal comfort in the building sector all over the world. Considering current energy consumption and indoor thermal comfort concerns, the idea of thermal energy storage in buildings has gained great significance. PCM is the material that stores thermal energy and experiences a phase change. PCM enhances the building's inertia and stabilizes temperature changes in the interior air by storing and releasing heat within a defined temperature range. It is thought to be a potential solution for lowering energy consumption in buildings. PCM is imposed inside the walls to regulate the room temperature. An effort has been undertaken to compare the inner wall temperature of a constructed modular building unit both with and without PCM use. The PCM-imposed modular construction unit exhibits decreased temperature variations since it releases and absorbs surplus heat from the room's exterior, keeping the interior temperature steady. When compared to a plain wall, the walls of a modular building unit enforced by PCM can lower the heat load by 10% to 30%. The result of this study indicates that there is a 2-3 °C drop in room temperature, and it is determined that the modular building unit mandated by PCM offers greater prospects for energy savings than the conventional modular building unit. To discourse the ineffectiveness of building structures and degraded comfort level in buildings. The integration of PCMs into the building envelope can minimize the building’s energy demand.

ABSTRACT. In current era of digitalization, where most of our daily routine tasks are dependent on internet connectivity, especially while connected through mobile applications. These private tasks require strong and enough security to avoid any malicious activity. There are various security techniques already implemented to secure the users connectivity in various mobile applications but still these security techniques are not feasible in every application. Cryptography is playing a significant role in network security domain by providing a secure environment for the user’s personal information, photos, and accounts using cryptographic algorithms. Various algorithms are already being implemented, with their individual pros and cons. This research work provides comprehensive comparison and performance analysis of popular encryption algorithms that can be used in various mobile applications. The paper discussed the key-based encryption techniques used in mobile applications. The performance analysis of encryption techniques is based on their memory requirement and consumption, encryption time, decryption time, and the avalanche effect. Results will help to further select the best algorithm in relevant mobile applications.

ABSTRACT. The agriculture sector is ranked as the highest freshwater consuming sector globally due to the ample amount of water required for the agricultural production. An understanding of the profound nexus between the crop yield, soil properties, climate conditions, farming practice and water is essential for a sustainable agricultural production. The conventional methods for the monitoring of soil and climate patterns during the crop growth have been found less effective due to the greater time, labor use and the non-availability of a continuous and reliable data. This generally leads to soil deterioration, excessive or deficient application of irrigation, and reduced crop yields. Therefore, this empirical study was conducted in the Gadap Town, Sindh – Pakistan to investigate the application of IoT based smart soil and weather sensors for the monitoring of soil and weather conditions over the area during the crop growing period. The results obtained from the soil sensors showed satisfactory measurements of daily soil available moisture, temperature, TDS, salinity levels and nutrient content, whereas the smart weather tracker provided acceptable results of air temperature (Tmax and Tmin), heat index, and relative humidity over the study area. Thus, it was concluded that the IoT based smart soil and weather sensors can be adopted in place of the conventional methods for the real time monitoring of soil and weather conditions, that may assist the farm managers for a better water resource management, soil conservation, and to attain the desired crop water productivity and the per hectare yield.

ABSTRACT. This study addresses the issue of class imbalance in multiclass time series classification by using various resampling techniques. In this study, four prominent strategies are used that are Synthetic Minority Over-sampling Technique (SMOTE), Adaptive Synthetic (ADASYN) sampling, Random Under-sampling, and a hybrid method (SMOTETomek). The techniques are applied to the MIT-BIH Arrhythmia Dataset as a case study. The performance and impact of each technique are measured on classification performance. Our methodology employs a Random Forest classifier and a comprehensive set of evaluation metrics. Results demonstrate that while all resampling methods improve minority class recognition, the hybrid SMOTETomek approach shows the most promising results, achieving the best balance between overall performance and class-wise metrics.

ABSTRACT. Using a paper attendance record, the outdated procedures for tracking student, employee, and sometimes teaching staff attendance are thought to be time-consuming and exhausting. Because to the illnesses that the globe has encountered in the past, in addition to mistakes and repeats or neglecting to register attendance, regardless of manually fingerprinting, it has grown unfavorable to certain individuals as it is thought to have a way of spreading infection. In this study, we present a technique for recording attendance that combines face recognition software with immediate video processing. We do this by accessing the camera device, taking a picture of the subject, processing and framing the image, matching the apprehended faces with images from the database, carrying out face recognition, handling file processes, and organizing time-associated tasks. When the required individual is located, attendance is noted, the exact time is put in in a spreadsheet, and the exact moment of the day that when attendance was noted is preserved in the file. The system’s structure enables effective real-time tracking and detection, demonstrating a high degree of precision and effectiveness in face detection.

ABSTRACT. In today's networking environments caching algorithms plays a very important role to get the best use of resources and maximizing performance of networks. In this paper, we conduct a comparative analysis of the three caching algorithms: Dependent set, Cover set and Mixed set. Every algorithm is intended to solve one problem of rule dependency management and cache optimization in the network switches. Each has its strengths and trade-offs, and a detailed comparison across dozens of dimensions, throughput, adaptability, memory usage, cache-hit rate, execution time, resource efficiency, scalability (both operational- and system-level), flexibility in supporting different workloads, robustness to system behavior changes independent of user behavior (like the slow generation asymptote on the left above!), and operational stability just to name a few popular ones, are relevant for many high-scale web services products. There are many other dependencies in the region, so pay a lot of memory which is not acceptable for the very first versions. The Dependent Set algorithm is designed to be an optimized memory, easy to adapt and resource-efficient rule dependency manager. This further enforces the emphasis on rule coverage policies of the Cover Set algorithm, improving cache performance and scalability. The Mixed Set algorithm combines the strengths of both methods providing a rounded approach, with throughput, adaptability and resource allocation. The study examines the benefits of each algorithm in real-world situations. It shows how they improve network efficiency and adaptability. This research expands our understanding of caching algorithms. It explains how they optimize networks. This paves the way for better network management and performance. By comparing these algorithms, the paper provides useful information. It helps network administrators and researchers enhance caching mechanisms in networks.