bluuebunny/crossref_metadata_2025 huggingface dataset:
Project Based Learning: Promoting Cross-Cultural Understanding using Android Application at an Indian University in Gujarat
Authors: Davierwala Shehrevar , Bhatt Nirav
Published: September 2023

Students in a project-based learning environment get experience with actual, hands-on projects. In this article, we present the experience obtained in a Mobile Application Development course. In addition, available multi-country criteria of this research enable us to investigate probable cross-cultural variances. The purpose of this project was to design and create apps (or simply app) for Android-based devices. The chosen methodology, based on Project-Based Learning, boosted both cooperation and rivalry among students. This research offers the outcomes from a heuristic assessment by students at Indian university in Gujarat for the students of three nations. Total 120 students are recruited for this research from a university in Gujarat. "It was established that a project-based learning approach is an effective and engaging way for translation students to experiment with their translation skills and different translation theories, achieve linguistic and cross-cultural understanding of different cultures while participating actively in various organised learning activities.'' More significantly, they have elevated their intercultural awareness and strengthened their intercultural communication ability. As students engage in this kind of project-based, introspective, and exploratory learning, they grow in their confidence as communicators (both verbally and in writing), their willingness to take intellectual risks, and their awareness of cultural variety. Its educational ramifications are examined.
A Deep Reinforcement Learning-Based Model for Optimal Resource Allocation and Task Scheduling in Cloud Computing
Authors: Alhazmi Lamia
Published: September 2023

The advent of cloud computing has dramatically altered how information is stored and retrieved. However, the effectiveness and speed of cloud-based applications can be significantly impacted by inefficiencies in the distribution of resources and task scheduling. Such issues have been challenging, but machine and deep learning methods have shown great potential in recent years. This paper suggests a new technique called Deep Q-Networks and Actor-Critic (DQNAC) models that enhance cloud computing efficiency by optimizing resource allocation and task scheduling. We evaluate our approach using a dataset of real-world cloud workload traces and demonstrate that it can significantly improve resource utilization and overall performance compared to traditional approaches. Furthermore, our findings indicate that deep reinforcement learning (DRL)-based methods can be potent and effective for optimizing cloud computing, leading to improved cloud-based application efficiency and flexibility.
Medical Diagnosis with Multimodal Image Fusion Techniques
Authors: Prathipa R. , Ramadevi R.
Published: September 2023

Image Fusion is an effective approach utilized to draw out all the significant information from the source images, which supports experts in evaluation and quick decision making. Multi modal medical image fusion produces a composite fused image utilizing various sources to improve quality and extract complementary information. It is extremely challenging to gather every piece of information needed using just one imaging method. Therefore, images obtained from different modalities are fused Additional clinical information can be gleaned through the fusion of several types of medical image pairings. This study's main aim is to present a thorough review of medical image fusion techniques which also covers steps in fusion process, levels of fusion, various imaging modalities with their pros and cons, and  the major scientific difficulties encountered in the area of medical image fusion. This paper also summarizes the quality assessments fusion metrics. The various approaches used by image fusion algorithms that are presently available in the literature are classified into four broad categories i) Spatial fusion methods ii) Multiscale Decomposition based methods iii) Neural Network based methods and iv) Fuzzy Logic based methods. the benefits and pitfalls of the existing literature are explored and Future insights are suggested. Moreover, this study is anticipated to create a solid platform for the development of better fusion techniques in medical applications.
Deep Learning based Densenet Convolution Neural Network for Community Detection in Online Social Networks
Authors: Selvakumar M. , Kathiravan A. Vijaya
Published: September 2023

Online Social Networks (OSNs) have become increasingly popular, with hundreds of millions of users in recent years. A community in a social network is a virtual group with shared interests and activities that they want to communicate. OSN and the growing number of users have also increased the need for communities. Community structure is an important topological property of OSN and plays an essential role in various dynamic processes, including the diffusion of information within the network. All networks have a community format, and one of the most continually addressed research issues is the finding of communities. However, traditional techniques didn't do a better community of discovering user interests. As a result, these methods cannot detect active communities.  To tackle this issues, in this paper presents Densenet Convolution Neural Network (DnetCNN) approach for community detection. Initially, we gather dataset from Kaggle repository. Then preprocessing the dataset to remove inconsistent and missing values. In addition to User Behavior Impact Rate (UBIR) technique to identify the user URL access, key term and page access. After that, Web Crawling Prone Factor Rate (WCPFR) technique is used find the malicious activity random forest and decision method. Furthermore, Spider Web Cluster Community based Feature Selection (SWC2FS) algorithm is used to choose finest attributes in the dataset. Based on the attributes, to find the community group using Densenet Convolution Neural Network (DnetCNN) approach. Thus, the experimental result produce better performance than other methods.
Secured Scheme for Privacy Preserving and Node Authentication Mechanism for a Special Mobile Ad hoc Network
Authors: Mishra Sushil Kumar , Gupta Ruchika
Published: September 2023

Opportunistic networks are a special type of Mobile Ad hoc network which are wirelessly interlinked nodes with the absence of end to end connectivity. All nodes in an opportunistic network are free to move in an environment. Due to the high degree of mobility of nodes, opportunistic networks differ significantly from the existing traditional networks and it works on store, carry &forward mechanism in which, each node has a communication range. Within its proximity, if any node comes, it can send and receive messages. In an opportunistic network, there is no proper infrastructure available for communication and node have limited storage and computational capabilities. The major problem being faced in an opportunistic network is the identification of normal and malicious nodes because due to the open nature of the opportunistic network, malicious nodes also can join the network and perform some malicious activities like Sybil attack. We propose a remedy to address the authentication and privacy issue that can arise in an opportunistic network. According to the findings of the simulation, the proposed research work satisfies the authentication and privacy criteria of an opportunistic network.
Intelligent Buffer Management Algorithm to Prevent Packet loss in Mobile Adhoc Network
Authors: Hemalatha S. , Pallathadka Harikumar , Chinhewadi Rajesh P , Kalaiyarasi D. , Thangakrishnan M. Suresh
Published: September 2023

In a mobile ad hoc network, which is self-organized and operates without any fundamental infrastructure, packet transmission from the source node to the destination node is completed after sending the route request and route reply. A reliable path is then selected depending on the protocol choice.  Data that the sender intends to deliver is broken up into packets and given sequence numbers before being transmitted over the channel. With the aid of an internal buffer that helps to receive packets and forward them to the next destination, each and every packet travels along the allotted path until it reaches its destination. If there is more traffic on the MANET, the buffer may overflow, which will result in packet loss during transmission..  The source node must retransmit to the destination if any packets were lost during the initial transfer. This article proposes the Intelligent  Buffer Management (IBM) active buffer management algorithm to prevent such a scenario by enhancing the MANET nodes' buffers to prevent packet loss. The Network Simulator is used to help build this suggested approach, and the results are compared to those of the current buffer management method to show that IBM is superior.
PAARGAMAN: Passenger Demand Provoked (On-The-Fly) Routing Of Intelligent Public Transport Vehicle with Dynamic Route Updation, Generation, and Suggestion
Authors: Ladha Akhilesh Yatiraj , Chaubey Nirbhay Kumar
Published: September 2023

Demand-based public bus service meets the need of passengers with less money, time, and resources by reducing the number of private vehicles on the road. In contrast, dynamic real-time demand-based routing faces challenges like elevated travel time due to the requested assignment based on the paths and vehicle availability. Hence, this research introduces a novel framework named Passenger Influence Bus Service-Intelligent Public Transport System (PIBS-IPTS) for efficient routing of available vehicles based on the demand of passengers. For this, optimal paths are elected from the known routes of the general vehicle through the Cuckoo Search (CS) optimization algorithm. Then efficient route prediction is employed by the Artificial Neural Network (ANN) for passenger flow. Here, the unavailability of the passenger request, such as source location or Destination locations, or the unavailability of both locations is updated while employing the path generation process. The path generation process ensures the reduction of request drops generated by the passenger, which elevates the usage of the general bus service. Here, for the optimal selection of routes from the identified routing paths, a multi-objective function based on traffic density, route condition, and route mobility is employed for the selection of a near-optimal global solution. The method’s performance is analyzed using MAE, RMSE, and MAPE and obtained the best values of 0.69, 0.72, and 0.74, respectively.
On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection
Authors: Saini Mehak , Grewal Surender K.
Published: September 2023

This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment.
A Novel Chaos Quasi-Oppositional based Flamingo Search Algorithm with Simulated Annealing for Feature Selection
Authors: Durgam Revathi , Devarakonda Nagaraju
Published: September 2023

In present situations feature selection is one of the most vital tasks in machine learning. Diminishing the feature set helps to increase the accuracy of the classifier. Due to large number of information’s present in the dataset it is a tremendous process to select the necessary features from the dataset. So, to solve this problem a novel Chaos Quasi-Oppositional based Flamingo Search Algorithm with Simulated Annealing algorithm (CQOFSA-SA) is proposed for feature selection and to select the optimal feature set from the datasets and thus it shrinks the dimension of the dataset. The FSA approach is used to choose the optimal feature subset from the dataset. In each iteration, the optimal solution of FSA is enriched by Simulated Annealing (SA). TheChaos Quasi-Oppositional based learning (CQOBL) included in the initialization of FSA improves the convergence rate and increases the searching capability of the FSA approach in choosing the optimal feature set. From the experimental outcomes, it is proved that the proposed CQOFSA-SA outperforms other feature selection approaches in terms of accuracy, optimal reduced feature set, fast convergence and fitness value.
Business Analytics Using Predictive Algorithms
Authors: Jangam Dhanshri Satish , Deshpande Arati R.
Published: September 2023

In today's data-driven business landscape, organizations strive to extract actionable insights and make informed decisions using their vast data. Business analytics, combining data analysis, statistical modeling, and predictive algorithms, is crucial for transforming raw data into meaningful information. However, there are gaps in the field, such as limited industry focus, algorithm comparison, and data quality challenges. This work aims to address these gaps by demonstrating how predictive algorithms can be applied across business domains for pattern identification, trend forecasting, and accurate predictions. The report focuses on sales forecasting and topic modeling, comparing the performance of various algorithms including Linear Regression, Random Forest Regression, XGBoost, LSTMs, and ARIMA. It emphasizes the importance of data preprocessing, feature selection, and model evaluation for reliable sales forecasts, while utilizing S-BERT, UMAP, and HDBScan unsupervised algorithms for extracting valuable insights from unstructured textual data.
Empowering the EV Community: A Comprehensive Solution for Electric Vehicle Charging
Authors: Sadiq Shaik Mohammad , Ganesh Chillara Sai , Vignesh Modepalli , Madhuri Vinjam , Ramya Medarametla Sai
Published: September 2023

The rapid transition from conventional vehicles to electric vehicles (EVs) has created a significant demand for electric vehicle charging infrastructure. The availability of adequate EV charging infrastructure is crucial to meet this demand and support the widespread adoption of EVs. However, EV users often face challenges in accessing information about EV charging infrastructure providers, limiting their ability to plan longer journeys effectively. In response to this issue, this research paper presents a proposed web application that aims to connect EV users and enable them to assume dual roles as both service providers and service users. The web application offers interactive maps for tracking travel and destinations, facilitates user matching based on specific requirements, and provides information on payment methods and charging rates for available chargers according to their locations. By bridging the information gap and promoting communication within the EV community, this research seeks to empower EV users and foster the growth of the electric vehicle ecosystem. The comprehensive platform provided by the proposed web application is intended to facilitate information exchange, enhance user experiences, and further encourage the widespread adoption of electric vehicles.
Boosting English Proficiency in Peruvian Children from 6 to 12 Years Old: Development of an Innovative Hybrid Application
Authors: Lida Violeta Asencios-Trujilo , Lucia Asencios-Trujillo , Carlos Jacinto La Rosa-Longobardi , Djamila Gallegos-Espinoza , Cristina Piñas-Livia , Rosa Perez- Siguas , Hernan Matta-Solis
Published: September 2023

The present research work tries to design a hybrid application to learn English in children from 6 to 12 years old where it has 5 modules that are . Home, materials, evaluations, cards and notes (score table). A Design Thinking methodology was implemented since it was considered to be the most appropriate for the development of the research since in each phase it would be detailed what the research would consist of and the necessary requirements to be able to design it. Likewise, a survey was conducted with 30 people to know their opinion on the use of technologies in learning, resulting in a positive way about the implementation of technologies when teaching and learning.

It was concluded that the implementation of a hybrid application would facilitate student learning.
An Advanced Knowledge Based Graphical Authentication Framework with Guaranteed Confidentiality and Integrity
Authors: Golar Priti C. , Sharma Rika
Published: September 2023

The information and security systems largely rely on passwords,which remain the fundamental part of any authentication process. The conventional authentication method based on alphanumerical username and password suffer from significant disadvantages. The graphical password-based authentication system has recently been introduced as an effective alternative. Although the graphical schemes effectively generate the passwords with better flexibility and enhanced security, the most common problem with this is the shoulder surfing attack. This paper proposes an effective 3D graphical password authentication system to overcome such drawbacks. The system is based on the selection of click points for generating passwords. The proposed work involved a training phase for evaluating the model in terms of the success rate. The overall evaluations of the model in terms of password entropy, password space, login success rates, and prediction probability in the shoulder surfing and guessing attacks proved that the model is more confidential and maintains a higher range of integrity than the other existing models.
Effective Moving Object Tracking Using Adaptive Background Subtraction with Advanced Probability Evolutionary Algorithm
Authors: Ekkurthi Adinarayana , Sujatha V. , Kumar K. Vijay
Published: September 2023

Tracking moving objects is a very difficult task for many computers. The task of tracking is a moving object. There are many virtual applications such as video surveillance and object recognition. Many ideas have been suggested for moving objects tracking and detection. One of the main problems solving tracking is finding identical objects in different frames. In this article, this application mainly focuses on the detection and tracking of moving objects using adaptive Background subtraction with adaptive probability evolutionary algorithm (AREA) method. Normal probability evolutionary algorithm will track only human motion but our algorithm tracks all the moving objects very accurately. Our proposed system shows the tracked objects with the red mark border.
An Optimized Node Level Lightweight Security Algorithm for Cloud Assisted-IoT
Authors: Sumahasan S. , Rajyalakshmi D.
Published: September 2023

The fastest-evolving technology, the Internet of Things (IoT), will advance the fields of agriculture, defense, and medical electronics. IoT is focused on giving every object a purpose. IoT with cloud assistance offers a potential remedy for the issue of data expansion for individual objects with restricted capabilities. With the increasing use of cloud technology, the Internet of Things (IoT) has encountered additional security hurdles when it comes to exchanging data between two parties. To address this issue, a thorough investigation was conducted into a secure cloud-assisted strategy for managing IoT data, which ensures the safety of data during its collection, storage, and retrieval via the cloud, while also considering the growing number of users. To achieve this, a lightweight security mechanism that is optimized at the node level is implemented in the proposed system. By utilizing our technology, a secure IoT infrastructure can be established to prevent the majority of data confidentiality threats posed by both insiders and outsiders. Using a heartbeat sensor and a node MCU, we create a heartbeat monitoring system. At the node MCU level, giving security to the patient's health data and preventing unauthorized users from attacking it. Smaller key sizes and lightweight security techniques for IoT devices with minimal power, lower power and memory consumption and Execution time, transmission capacity reserve is used to achieve security. In order to achieve this. The performance of the RSA and ECC algorithms in terms of execution time, power consumption, and memory use have been tabulated for this experimental arrangement. The ECC method occurs to produce the best results in tiny devices.
Security Issues in Service Model of Fog Computing Environment
Authors: Bommala Harikrishna , Vikkurty Sireesha , Babu K. Ramesh , Reddy Bhargavi Peddi
Published: September 2023

Fog computing is an innovative way to expand the cloud platform by providing computing resources. The platform is a cloud that has the same data, management, storage and application features, but their origins are different because they are deployed to different locations. The platform system can retrieve a large amount, work in the field, be fully loaded, and mount on a variety of hardware devices. With this utility, Fog Framework is perfect for applications and critical moments. Fog computing is similar to cloud computing, but because of its variability, creates new security and privacy challenges that go beyond what is common for fog nodes. This paper aims to understand the impact of security problems and how to overcome them, and to provide future safety guidance for those responsible for building, upgrading and maintaining fog systems.
Impact of Fuzzy Logic in Object-Oriented Database Through Blockchain
Authors: Mishra Eesha , Kumar Santosh , Awasthi Shivam
Published: September 2023

In this article, we show that applying fuzzy reasoning to an object-arranged data set produces noticeably better results than applying it to a social data set by applying it to both social and object-situated data sets. A Relational Data Base Management System (RDBMS) product structure offers a practical and efficient way to locate, store, and retrieve accurate data included inside a data collection. In any case, clients typically have to make vague, ambiguous, or fanciful requests. Our work allows clients the freedom to utilise FRDB to examine the database in everyday language, enabling us to provide a range of solutions that would benefit clients in a variety of ways. Given that the degree of attributes in a fuzzy knowledge base goes from 0 to 1, the term "fuzzy" was coined. This is due to the base's fictitious formalization's reliance on fuzzy reasoning. In order to lessen the fuzziness of the fuzzy social data set as a result of the abundance of uncertainty and vulnerabilities in clinical medical services information, a fuzzy article located information base is designed here for the Health-Care space. In order to validate the presentation and sufficiency of the fuzzy logic on both data sets, certain fuzzy questions are thus posed of the fuzzy social data set and the fuzzy item-situated information base..
Improving Data Transmission Rate with Self Healing Activation Model for Intrusion Detection with Enhanced Quality of Service
Authors: Madhuri Simhadri , Lakshmi S. Venkata
Published: September 2023

Several types of attacks can easily compromise a Wireless Sensor Network (WSN). Although not all intrusions can be predicted, they may cause significant damage to the network and its nodes before being discovered. Due to its explosive growth and the infinite scope in terms of applications and processing brought about by 5G, WSN is becoming more and more deeply embedded in daily life. Security breaches, downed services, faulty hardware, and buggy software can all cripple these enormous systems. As a result, the platform becomes unmaintainable when there are a million or more interconnected devices. When it comes to network security, intrusion detection technology plays a crucial role, with its primary function being to constantly monitor the health of a network and, if any aberrant behavior is detected, to issue a timely warning to network administrators. The current network's availability and dependability are directly tied to the efficacy and timeliness of the Intrusion Detection System (IDS). An Intrusion-Tolerant system would incorporate self-healing mechanisms to restore compromised data. System attributes such as readiness for accurate service, supply identical and correct data, confidentiality, and availability are necessary for a system to merit trust. In this research, self-healing methods are considered that can detect intrusions and can remove with intellectual strategies that can make a system fully autonomous and fix any problems it encounters. In this study, a new architecture for an Intrusion Tolerant Self Healing Activation Model for Improved Data Transmission Rate (ITSHAM-IDTR) is proposed for accurate detection of intrusions and self repairing the network for better performance, which boosts the server's performance quality and enables it to mend itself without any intervention from the administrator. When compared to the existing paradigm, the proposed model performs in both self-healing and increased data transmission rates..
Real-Time IoV Task Offloading through Dynamic Assignment of SDN Controllers: Algorithmic Approaches and Performance Evaluation
Authors: Lanka Divya , Kandasamy Selvaradjou
Published: September 2023

Task offloading in Internet of Vehicles (IoV) is very crucial. The widespread use of IoT applications frequently interacts with the cloud, thereby increasing the load on centralized cloud controllers. Centralized network management in cloud infrastructure is not feasible for the latest IoT trends. Decentralized and decoupled network management in Software Defined Networks (SDN) can enhance IoV services. SDN and IoV coupling can better handle task offloading in ubiquitous and dynamic IoV environments. However, appropriate SDN controller assignment and allotment strategies play a prominent role in IoV communication. In this study, we developed algorithms for SDN controller assignment and allotment namely 1) Next Fit Allotment and Assignment of SDN Controller in IoV (NFAAC), 2) Dynamic Bin Packing Allotment and Assignment of SDN Controller in IoV (DBPAAC), and 3) Dynamic Focused and Bidding Allotment and Assignment algorithm of SDN Controller in IoV (DFBAAC). These algorithms were simulated using open-flow switch controllers. The controllers were modeled as Road Side Units (RSU) that can allocate bandwidth and resource requirements to vehicles on the road. Our results show that our proposed algorithm works efficiently for SDN controller assignment and allocation, outperforming the existing work by a significant improvement of 13.5%. The working of the proposed algorithms are verified, tested, and analytically presented in this study.
Age-Adaptive Multimodal Biometric Authentication System with Blockchain-based Re-Enrollment
Authors: Sawant Geetanjali , Bharadi Vinayak , Prasad Kaushal , Jangid Pravin
Published: September 2023

In the long run, a significant time gap between enrollment and probe image challenges the model's prediction ability when it has been trained on variant biometric traits. Since variant biometric traits change over time, it is sensible to construct a multimodal biometric authentication system that must include at least one invariant trait, such as the iris. The emergence of Deep learning has enabled developers to build classifiers on synthesized age-progressive images, particularly face images, to search for individuals who have been missing for many years, to avail a comprehensive portrayal of their appearance. However, in sensitive areas such as the military and banks, where security and confidentiality are of utmost importance, models should be built using real samples, and any variations in biometric traits should trigger an alert for the system and notify the subject about re-enrollment. This paper proposes an algorithm for age adaptation of biometric classifiers using multimodal channels which securely update the biometric traits while logging the transactions on the blockchain. It emphasizes confidence-score-based re-enrolment of individual subjects when the authenticator module becomes less effective with a particular subject's probe image. This reduces the time, cost, and memory involved in periodic re-enrolment of all subjects. The classifier deployed on the blockchain invokes appropriate smart contracts and completes this process securely.
An Implementation of Machine Learning Algorithm for Fake News Detection
Authors: Bussa Sudhir , Bodhankar Aniruddha , Patil Vinod H. , Pal Hemant , Bunkar Satyendra Kumar , Khan Qureshi Abdul Razzak
Published: September 2023

Fake news is a growing concern in the age of social media, as it can spread rapidly and have serious consequences. To combat this issue, machine learning techniques have been used for fake news detection. In this study, we propose two models, LSTM and SVM, for fake news detection. The LSTM model is a deep learning algorithm that is particularly suited to sequential data such as text. It can capture long-term dependencies in the text and has shown promising results in natural language processing tasks. The SVM model, on the other hand, is a classical machine learning algorithm that has been widely used for classification tasks. To evaluate the performance of the proposed models, we conducted experiments on a dataset of news articles. Our results show that both models achieve high accuracy in detecting fake news. However, the LSTM model outperforms the SVM model with an accuracy of 94% compared to 89%. Furthermore, we conducted a feature importance analysis to determine the most important features for detecting fake news. The results show that the presence of certain words and phrases, such as "unverified" and "anonymous sources", are strong indicators of fake news. In conclusion, our study demonstrates the effectiveness of using machine learning techniques, particularly LSTM and SVM, for detecting fake news. This research can be applied to assist individuals and organizations in identifying and combating fake news in the digital age.
An Innovative Approach for Predicting Software Defects by Handling Class Imbalance Problem
Authors: Suryawanshi Ranjeetsingh , Kadam Amol , Anekar Devata , Patil Vinayak
Published: September 2023

From last decade unbalanced data has gained attention as a major challenge for enhancing software quality and reliability. Due to evolution in advanced software development tools and processes, today’s developed software product is much larger and complicated in nature. The software business faces a major issue in maintaining software performance and efficiency as well as cost of handling software issues after deployment of software product. The effectiveness of defect prediction model has been hampered by unbalanced data in terms of data analysis, biased result, model accuracy and decision making. Predicting defects before they affect your software product is one way to cut costs required to maintain software quality. In this study we are proposing model using two level approach for class imbalance problem which will enhance accuracy of prediction model. In the first level, model will balance predictive class at data level by applying sampling method. Second level we will use Random Forest machine learning approach which will create strong classifier for software defect. Hence, we can enhance software defect prediction model accuracy by handling class imbalance issue at data and algorithm level.
Exploring Path Computation Techniques in Software-Defined Networking: A Review and Performance Evaluation of Centralized, Distributed, and Hybrid Approaches
Authors: Saxena Mohit Chandra , Sabharwal Munish , Bajaj Preeti
Published: September 2023

Software-Defined Networking (SDN) is a networking paradigm that allows network administrators to dynamically manage network traffic flows and optimize network performance. One of the key benefits of SDN is the ability to compute and direct traffic along efficient paths through the network. In recent years, researchers have proposed various SDN-based path computation techniques to improve network performance and reduce congestion.

This review paper provides a comprehensive overview of SDN-based path computation techniques, including both centralized and distributed approaches. We discuss the advantages and limitations of each approach and provide a critical analysis of the existing literature. In particular, we focus on recent advances in SDN-based path computation techniques, including Dynamic Shortest Path (DSP), Distributed Flow-Aware Path Computation (DFAPC), and Hybrid Path Computation (HPC).

We evaluate three SDN-based path computation algorithms: centralized, distributed, and hybrid, focusing on optimal path determination for network nodes. Test scenarios with random graph simulations are used to compare their performance. The centralized algorithm employs global network knowledge, the distributed algorithm relies on local information, and the hybrid approach combines both. Experimental results demonstrate the hybrid algorithm's superiority in minimizing path costs, striking a balance between optimization and efficiency. The centralized algorithm ranks second, while the distributed algorithm incurs higher costs due to limited local knowledge. This research offers insights into efficient path computation and informs future SDN advancements.

We also discuss the challenges associated with implementing SDN-based path computation techniques, including scalability, security, and interoperability. Furthermore, we highlight the potential applications of SDN-based path computation techniques in various domains, including data center networks, wireless networks, and the Internet of Things (IoT).

Finally, we conclude that SDN-based path computation techniques have the potential to significantly improvement in-order to improve network performance and reduce congestion. However, further research is needed to evaluate the effectiveness of these techniques under different network conditions and traffic patterns. With the rapid growth of SDN technology, we expect to see continued development and refinement of SDN-based path computation techniques in the future.
Image Classification for Breast Cancer Using a Modified Convolution Neural Network Architecture
Authors: Porwal Priya , Singh Ajay Shankar , K. Thirunavukkarasu
Published: September 2023

The most common type of cancer that results in death is breast cancer. In the world, millions of people struggle with this disease. Breast cancer can affect men and women but women are more affected. For awareness, it is necessary to understand the sign and symptoms of breast cancer. The most common sign is an abnormal lump in the breast. But there may be many reasons of develop abnormal lumps. Computer-Aided Diagnosis (CAD) is extensively used in pathological image analysis to help pathologists enhance diagnosis efficiency, accuracy, and consistency. Recent studies have looked into deep learning methodologies to improve the effectiveness of pathological CAD.
Feature Selection and Energy Management in Wireless Sensor Networks using Deep Learning
Authors: Dora Sidhartha Sankar , Swain Prasanta Kumar
Published: September 2023

In wireless sensor networks, when the available energy sources and battery capacity are extremely constrained, energy efficiency is a major issue to be adressed. One of the main goals in the design of wireless sensor networks (WSNs) is to maximize longevity of battery life. Designers can benefit from the use of intelligent power utilization models to accomplish this goal. These models seek to decrease the number of chosen sensors used to record environmental measures in order to minimize power utilization while retaining the acceptable level of measurement accuracy. In order to simulate wireless sensor networks, we looked at real world datasets. Our simulation findings demonstrate that the suggested strategy can be used to accomplish significant goals by using the right number of sensors using deep learning, extend the lifespan of the wireless sensor networks.
A Hybrid Grey Wolf Optimization and Constriction Factor based PSO Algorithm for Workflow Scheduling in Cloud
Authors: Sriperambuduri Vinay Kumar , M Nagaratna
Published: September 2023

Due to its flexibility, scalability, and cost-effectiveness of cloud computing, it has emerged as a popular platform for hosting various applications. However, optimizing workflow scheduling in the cloud is still a challenging problem because of the dynamic nature of cloud resources and the diversity of user requirements. In this context, Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms have been proposed as effective techniques for improving workflow scheduling in cloud environments. The primary objective of this work is to propose a workflow scheduling algorithm that optimizes the makespan, service cost, and load balance in the cloud. The proposed HGWOCPSO hybrid algorithm employs GWO and Constriction factor based PSO (CPSO) for the workflow optimization. The algorithm is simulated on Workflowsim, where a set of scientific workflows with varying task sizes and inter-task communication requirements are executed on a cloud platform. The simulation results show that the proposed algorithm outperforms existing algorithms in terms of makespan, service cost, and load balance. The employed GWO algorithm mitigates the problem of local optima that is inherent in PSO algorithm.
A High-Performance Reconfigurable Four Element Multiband MIMO Antenna for UWB Communications
Authors: Santhikiran B. , Kavitha T.
Published: September 2023

For 5G applications, a 4-element orthogonal multi-input, multi-output antenna with an H-slot is advised. This antenna's CPW power source has four elements with better isolation. For this proposed antenna, the dimensions are reduced to , and the band obstruction with a parameter less than -10dB that is from 2.2GHz to 20GHz and it notches vary from 3.4GHz to 4.3GHz in the frequency range of 8.2GHz to 8.7GHz. In order to support WiMAX (3.3GHz–3.7GHz) and band ranges from 8.2GHz–8.7GHz in military/radar applications, notched filters will be used. The proposed antenna proved successful in achieving mutual coupling at less than -19 dB. The antenna's ECC level is 0.019 and its directivity gain is almost 10 db, making it a very good antenna. Except at the notches, the peak gain and radiation efficacy are 5.8 dB and 82%, respectively. This suggested antenna can be used for UWB applications such location monitoring, communications, military/radar applications, and biomedical systems as a prepared option or recommendation.
Early Detection of Cattle Hoof Disease using Internet of Things(IoT) based Sensory Data
Authors: K Durairaj. , Kumar.V Dhilip , G.R Kanagachidambaresan.
Published: September 2023

The monitoring of the health of dairy cattle is an extremely important component in the process of growing the supply of dairy products around the globe. Farmers these days are displaying less interest in the dairy industry since animals are suffering from a wide variety of debilitating health difficulties, unpredictability in the form of fatal illnesses, and advanced breeding expenses. The idea of "Smart Dairy Farming" is no longer only a notion for the distant future; rather, it has begun to materialise as numerous areas, such as machine learning, have found practical applications in this sector. In the dairy business, the timely diagnosis of lameness is a significant challenge that farmers are not yet able to tackle in an effective manner. Lameness may be brought on by a wide variety of foot and limb disorders, each of which can be brought on by a different illness, management practise, or environmental element. The importance of lameness prevention, early identification, and treatment in dairy cows cannot be overstated in light of the many detrimental impacts that may result from lameness. The early discovery of illness provides farmers with the opportunity to take preventative measures sooner, which may result in the reduction or elimination of the use of antibiotics, an increase in milk production, and cost savings on veterinary care for their herd. This finding reveals the possibility of using classification algorithms to differentiate between the behaviours.
“Do You Know How to Ask for an Incomplete?” Reconceptualizing Low-Income, First-Generation Student Success Through a Resource Acquisition Lens
Authors: BASSETT BECCA SPINDEL
Published: September 2023

In this ethnographic study, Becca Spindel Bassett investigates why low-income and first-generation students access fewer resources and gain fewer benefits from their university campuses than do their higher-income, continuing-generation peers. Building on sociological theories that emphasize the relational and political dynamics of resource acquisition, the article explores the disadvantages that these students face in making persuasive claims on university resources and the role that faculty and staff can play in mitigating these disadvantages. Drawing on a year-long ethnographic study of two universities that serve and graduate large numbers of low-income, first-generation students, Bassett finds that faculty and staff drew on three common, proactive strategies to empower students to make effective claims on university resources, which directed them toward valuable resources and elevated their local social status. These findings challenge foundational theories in higher education that attribute equity gaps to individual-level differences, as well as reveal the importance of claims-making processes in determining who succeeds and who struggles on campus and underscore the critical role that faculty and staff can play in fostering more structurally and culturally supportive campuses.
Personality, Interests, «Rules instead of Laws»: Standpoint of a Young Professor of Jurisprudence (To the 200th Anniversary of the Great Russian Educator K.D. Ushinsky)
Authors: Malinova A. G.
Published: September 2023

The author analyzes K.D. Ushinsky’s views on personality, its numerous and multifaceted interests, cameralism and civil law from the little-known legal works written by K.D. Ushinsky in the first years after graduating from the Faculty of Law of Moscow University, namely three years of his serving as a professor of the Demidovsky Law Lyceum in Yaroslavl. In this regard, the general conditions of teaching at that higher educational institution and the range of disciplines taught, the few surviving sources of Ushinsky’s lecture activity of this period and his speech «On cameral education» at the solemn meeting of the Demidovsky Lyceum are briefly considered. Special attention is given to Ushinsky’s astute conclusion that convenient rules took the place of laws in German cameralistics of that time. This opinion is especially relevant today, because it shows that the desire of Western countries to start living not according to the norms of international law, but according to the rules established by them, did not arise today. The modern political, diplomatic, legal, and economic reality is literally oversaturated with facts of illegal sanctions, asset freezes, property seizures, and other illegal actions of the United States and a number of other Western countries against Russia and other independent states, as well as citizens of these states. Ushinsky’s analysis of German cameralism clearly shows that the use by Western countries of «rules convenient for them» instead of laws is by no means a modern «invention», but a constantly developing process, the foundation of which was laid by Western political and legal thought several centuries ago.
THE INDOCTRINATION DIMENSION OF REPRESSION: TELEVISED CONFESSIONS IN CHINA*
Authors: Fu Diana
Published: September 2023

Public confessions are repression spectacles that have appeared in different regimes and historical periods. This study theorizes an indoctrination dimension of repression—the turning of punishment into a control tool that displays the regime’s apparent transformative power. Whereas repression is often defined by its inhibiting dimension, this study argues that authorities stage repressive spectacles not only to deter further dissent but also as an instrument of “thought reform,” a governance tradition in China. The study finds two indoctrinating narratives based on an interpretive analysis of a dataset of televised confessions in China. First, confessions show a doctrine of political dissident as traitorous and immoral. Second, they convey a doctrine of rectification that displays the seemingly transformative power of the regime, one in which transgressors perform law-abiding citizens. These findings advance the conceptual refinement of repression by analyzing its indoctrination dimension.
Irrigation Depths Adjustment Methods on Yield Components, Grain Yield and Water Use Efficiency of Dry Beans Crop
Authors: Almeida Patricia Mara de , Marco Nataly Katia de , Kuhn Luan Junior , Biazatti Renan Marré , Fernandes Franciele Fátima , Pazzini Eduarda Posser , Piazzetta Hugo Von Linsingen
Published: September 2023

O feijão é uma das principais culturas na dieta humana brasileira, apresentando baixa produtividade nacional, o que pode ser explicado pela ocorrência de períodos de deficiência hídrica para a cultura. Desta maneira, objetivou-se avaliar os componentes de rendimento da cultura, a produtividade de grãos e a eficiência do uso da água, em diferentes manejos da lâmina de irrigação e validar a planilha Lâmina para recomendação de irrigação da cultura. O delineamento experimental foi de blocos casualizados com quatro repetições. Os tratamentos foram: não utilização da irrigação; manutenção da umidade do solo na capacidade real de água no solo em 45% da capacidade total de água do solo; manutenção da umidade do solo equivalente em 100% da umidade da capacidade de campo; e ajuste da lâmina de irrigação utilizando a planilha “Lâmina”. Determinou-se a quantidade total de água recebida pela cultura, a produtividade de grãos, a relação litros por quilograma de grãos, o ciclo e os componentes do rendimento. O tratamento Lâmina apresentou os melhores resultados para a produtividade, e maior eficiência no uso da água, no entanto não se obteve diferenças significativas para as componentes do rendimento exceto para a componente espessura das vagens. Assim, a planilha Lâmina apresentou grande potencial, tendo alta produtividade e colaborando para a racionalização do uso de água na cultura do feijão.
 
Palavras-chave: Phaeseolus vulgaris L. Déficit de Água. Precipitação.
 
Abstract
Dry beans are one of the most important crops for Brazilians people diet, features a low national yield, which can be explained by the occurrence of water deficit events. Thus, this study aimed to evaluate the yield components, the grain yield and the water use efficiency of dry beans submitted to different ways to adjust the irrigation depth, and validate the Lâmina spreadsheet to recommend irrigation to the culture crop. The experimental design was a randomized block with four replications. Four treatments: non-use of irrigation; soil moisture equivalent to actual capacity of water in the soil at 45% of the total capacity of the ground water; soil moisture equivalent to 100% of the field capacity; and irrigation depth adjustment provided by Lâmina spreadsheet. The Lâmina treatment showed the best results for grain yield and water use efficiency, however the yield components were not significant, except for pods thickness. Thus, the Lâmina spreadsheet showed great potential, with high yield and contributing to the rational water use in dry beans crop.
 
Keywords: Phaeseolus vulgaris L. Water Deficit. Rainfall.