bluuebunny/crossref_metadata_2025 huggingface dataset:
Sembolik ve Sözel Biçimi Kullanma Öz Yeterlik Ölçeğinin Psikometrik Özelliklerinin Rasch Modeli ile İncelenmesi
Authors: ÇEKMEZ Erdem , CEVAHİR BOLAT Rümeysa
Published: December 2023

: Bu çalışmada, matematik öğretmeni adaylarının matematiksel ifadelerin farklı biçimleri arasında tercüme yapma hususunda öz yeterliklerini ölçmek için Klasik Test Teorisi çerçevesinde geliştirilmiş bir ölçeğin psikometrik özelliklerinin Rasch Teorisi perspektifinden incelenmesi amaçlanmıştır. Bu amaçla ölçek 252 matematik öğretmeni adayının oluşturduğu bir örneklem üzerinde uygulanarak elde edilen veriler ile maddelerin uyum istatistikleri, kişiler ile maddelere ilişkin güvenirlik ve ayırt edicilik indeksleri, maddelere yanıt verme hususunda benimsenen kategori yapısının etkinliği, test maddelerinin cinsiyet, coğrafi bölge ve sınıf düzeyi değişkenlerinin oluşturduğu örneklemin alt kümelerinde yanlılık gösterme durumu, testin örneklemi hedefleme yeterliği ve testin boyut yapısı incelenmiştir. Elde edilen bulgular örneklemin farklı alt kümelerinde ölçek maddelerinin yanlılık göstermediğini, uyum istatistiklerinin kabul edilebilir sınırlar içerisinde olduğunu, ölçeğin Rasch boyutundan başka boyut içermediğini, test maddelerinin öz yeterlik değişkeninin geniş bir aralığını ve kişileri ilgili niteliğe sahiplik açısından kabul edilebilir hassaslıkta ölçebildiğini, testin örneklemi hedeflemede yeterli olduğunu göstermiştir. Ayrıca ölçekte benimsenen kategori yapısının uygun olmadığı ve olumsuz ve olumlu maddelerin zıtlık oluşturduğu ortaya çıkmıştır. Elde edilen bulgulardan hareketle test maddelerine ve kategori yapısına yönelik önerilerde bulunulmuştur.
Pre-service Teachers’ Metaphorical Perceptions of “Pedagogical Formation Certificate Program Student” Concept
Authors: TUNCEL Meriç , KARAKIŞ Özlem
Published: December 2023

TThis study aimed to determine the metaphorical perceptions of pre-service teachers participating in the pedagogical formation certificate program about the concept of pedagogical formation certificate program student. Phenomenology design was used in the data collection. The study group of the research consisted of 417 pre-service teachers participating in the pedagogical formation training certificate program at the faculty of education of a state university located in the Western Black Sea Region in Türkiye in the 2022-2023 academic year. The data of the study were collected through a form prepared by the researchers asking "pedagogical formation program student is like (1)............/similar because (2)................" to an object, a shape, an animal or a living creature (metaphor/mental image) and to state the reason (justification/logical basis) for their similes. The documents were analyzed by content analysis method. As a result of the research, 52 metaphors developed by pre-service teachers taking part in pedagogical formation certificate program about the concept of pedagogical formation program student were collected in 16 different categories named as “dough, new born baby, storage space, warrior, turtle, guided person, lucky fish, mother, light, miserables, unprocessed mine, precious metal, dreamer, intermediary person, unconscious person, lego”.
Öğretmen Görüşlerine Göre İş Yükü ile Bireysel ve Örgütsel Performans Arasındaki İlişki
Authors: TAŞKIN Sevgi , SEZGİN NARTGÜN Şenay
Published: December 2023

Bu araştırma lise öğretmenlerinin görüşlerine göre aşırı iş yükünün, bireysel ve örgütsel performans üzerine etkisini belirlemek ve bu görüşler arasındaki ilişkiyi incelemek amacıyla yapılmıştır. İlişkisel tarama modelinde olan araştırmanın çalışma evreni 2020–2021 eğitim öğretim yılında Bolu ili merkez ilçe liselerinde görev yapan 250 öğretmen oluşturmaktadır. Araştırmada veri toplama aracı olarak iş yükü, bireysel performans ve örgütsel performans ölçekleri kullanılmıştır. Verilerin analizinde ortalama, standart sapma, Mann Whitney U ve Pearson Korelasyon analizi yapılmıştır. Araştırma sonuçlarına göre; öğretmenlerin iş yükü, bireysel performans ve örgütsel performansa yönelik görüşleri “Katılıyorum” düzeyindedir. Öğretmenlerin görüşlerinde aşırı iş yükü, bireysel performans ve örgütsel performans ölçeklerinde medeni durum değişkenine göre anlamlı fark bulunmamıştır. Öğretmenlerin iş yükü ölçeği ile bireysel performans ölçeği ve örgütsel performans ölçeği arasında düşük düzeyde, bireysel performans ölçeği ile örgütsel performans ölçeği arasında ise orta düzeyde anlamlı ilişkiler tespit edilmiştir. Araştırma sonuçları değerlendirildiğinde, öğretmenlere aşırı iş yükü algısı oluşturacak düzeyde sorumlulukların verilmesi ile okulların belirlenen eğitim hedeflerine ulaşması beklenemez. Ayrıca ölçüsüz verilen işler, çalışma zamanlarının uzaması ve işlerin üstesinden gelinememesi öğretmenlerin performansını düşürmektedir. Artan rekabet koşulları ve değişen eğitim sistemi düşünüldüğünde, öğretmenlerin performansının düşmesi, doğrudan okulun eğitsel performansını olumsuz yönde etkileyeceği belirtilmektedir. Bu bakımdan okullarda eğitsel politikalar uygulanırken ve görev dağılımı yapılırken, öğretmenlere dengeli bir iş yükü verilmesi, mesleki yeterliliklerini ve performanslarını sergileyebilecekleri ortamların oluşturulması önerilebilir. Aynı zamanda hizmet içi eğitimlerde politika üreten yöneticilere, öğretmenlerin çalışma performansını etkileyen iş yükünün, okula olan olumlu ve olumsuz etkileri gözler önüne serilerek seminer ve toplantılarda yer verilmesi önerilebilir.
Exploring Microbial Diversity with QIIME2 v1
Authors: Hu Jialin
Published: December 2023

This tutorial introduces Qiime2 for non-phylogenetic diversityanalysis, covering core metrics calculation, emphasizing standardized samplingdepth, and using pre-processed feature tables and metadata. It then exploresbeta group significance analysis via qiime diversity beta-group-significance,detailing parameters and introducing pairwise testing. The tutorial concludeswith a concise demonstration of PCoA plot creation using qiime emperor plot,highlighting input requirements and insights into result interpretation. Usersare encouraged to explore the interactive Qiime2 visualization for valuableinsights into microbial community relationships and factors influencingdiversity. Overall, the tutorial serves as a brief yet comprehensiveintroduction to Qiime2 for enhanced capabilities in microbiome research.
Maintenance of the recording chamber for non-human primate (Chamber cleaning and dura scraping) v1
Authors: R Harsch Devin , Kase Daisuke , J Lipski Witold , S Turner Robert
Published: December 2023

Once a craniotomy has been done in the chamber, the inside of the chamber must be cleaned regularly (2-3 times per week) to prevent infection. This protocol explains two procedures to maintain the recording chamber in a healthy condition:(1) chamber cleaning and (2) dura scraping.
BIT 577 QIIME2 Tutorial v1
Authors: Livingston Isabella
Published: December 2023

Command line scripts for processing and analyzing 16S amplicon data using QIIME2.
Annonaceae DNA extraction protocol from silicagel dried and herbarium preserved leaves v1
Authors: Soulé Vincent
Published: December 2023

This protocol is used for DNA extraction of samples from the tropical plant family Annonaceae for leaves dried using silicagel or sampled from herbarium sheets. This protocol is made for generation of small to long fragments (depending quality of sample) to prepare NGS libraries. This protocol is designed to extract DNA in batches of 48 samples, but this can also be undertaken in 2 times 48 (96) samples.
Primary hippocampal and cortical neuronal culture and transfection v1
Authors: J. Farrell Ryan , C Kokotos Alexandros , A. Ryan Timothy
Published: December 2023

Neurons consist some of the most complex cells. The culture of primary neurons from murine brains has advanced the understanding of neuronal function at the cellular and molecular level. This protocol presents an established method to isolate and culture hippocampal and cortical neurons from rat and mouse brains along with a method to sparsely transfect these neurons.
Lifeplan Camera Trapping Protocol v3
Authors: M.K. Rogers Hanna , Giedre Banelyte Gaia , M Farrell Arielle , Hardwick Bess , Mononen Tommi , Kerdraon Deirdre
Published: December 2023

Lifeplan is a global biodiversity monitoring project with the aim of assessing the current state of biodiversity worldwide, and using this knowledge to generate predictions of how biodiversity might look in the future. In this protocol we describe the method used within the Lifeplan project for collecting camera trap data on an international scale in a wide variety of environmental conditions and habitats. The aim is to use this data to identify species from images and create species lists for the different locations across the globe. This protocol contains a detailed description from setup of the cameras in the field to weekly data collection, as well as steps to configure cameras and collect necessary information to help in species identification. We identify the equipment used in Lifeplan, but also give technical specifications of that equipment so that other users of this protocol can find equivalent alternative equipment. We also specify what metadata should be collected with the camera trap data. The technical solution we use to collect metadata in the Lifeplan project is described in detail in the full Lifeplan protocol. The expected outcome of this method is camera trap images with associated metadata. The species identification process is outside the scope of this protocol and will be described elsewhere.
A NEEDS ANALYSIS OF TECHNOLOGY-INTEGRATED BASIC ENGLISH GRAMMAR COURSE IN THE INDONESIAN CONTEXT
Authors: Suharti Dwi Sloria , Musthafa Bachrudin , Yusuf Fazri Nur
Published: December 2023

Needs analysis is essential for developing a curriculum and teaching materials. Integration of technology in courses provides a supportive environment for learners across a wide range of courses. However, the needs analysis on the technology-integrated Basic English Grammar course learning is insufficient. This report aims to determine students' needs for studying Basic English Grammar courses through technology integration. For this purpose, the researchers used a descriptive qualitative design by distributing questionnaires. Twenty-four students completed the questionnaire, and the researchers recruited several students for interviews. The results of this study reveal students' expectations for technology-integrated assignments, such as submitting writing caption chores to Instagram, with a 48 per cent success rate. The technology-integrated Basic English Grammar course learning presences depict the most commonly used technologies such as YouTube, Instagram, the Quiz app, Google Classroom, Webbing, and e-Books. The researchers identified the lack of internet connection and an overabundance of information that perplexes students—this suggests the necessity to provide training to cope with the technological challenges of learning Basic English Grammar. The study proposes a teaching-learning situation in the Basic English Grammar course with technology integration as learning activities materials in the syllabus so that the teachers as the curriculum makers can develop the curriculum according to the needs of students. This report highlights that lecturers and universities perform periodic assessments of their students' needs as a basis for developing a curriculum with these reflective needs. In addition, the pedagogical implications for technology-integrated tools in the Basic English Grammar course are discussed.
EXPLORING THE PROFILE OF SCIENTIFIC ARTICLE WRITING ABILITY AMONG TERTIARY STUDENTS: INSIGHTS FROM FIVE INSTITUTIONS IN INDONESIA
Authors: Hernawan Hernawan , Anshari Dadang , Syihabuddin Syihabuddin , Mulyati Yeti
Published: December 2023

The motivation for this study stems from the observation that the culture and ability of tertiary students to write scientific articles are at a low level. This study aims to describe the profile of students’ scientific writing abilities. A descriptive method was used to gather data from five tertiary institutions in West Java. This study used a qualitative approach. The samples included 5 lecturers and 211 students. Data were collected using student needs questionnaires, interviews with lecturers, and analysis of student scientific articles. Data collection techniques included questionnaires and interviews and were analyzed using descriptive data analysis. The findings indicate that students’ ability to write scientific articles falls within the sufficient category. Despite this reality, the questionnaire results reveal that students still encounter difficulties in writing scientific articles and require a learning model that makes the process easier and helps to overcome problems in writing. The analysis of interviews with lecturers of scientific writing courses suggests that students’ ability to write scientific papers is adequate. Lecturers have implemented various learning methods during lectures because not all students like writing scientific articles. Moreover, most students are still confused about writing scientific articles correctly, and their motivation to read is low.
Digital Feminist Placemaking: The Case of the “Woman, Life, Freedom” Movement
Authors: Mehan Asma
Published: December 2023

Throughout Iran and various countries, the recent calls of the “Zan, Zendegi, Azadi” (in Persian), “Jin, Jiyan, Azadi” (in Kurdish), or “Woman, Life, Freedom” (in English) movement call for change to acknowledge the importance of women. While these feminist protests and demonstrations have been met with brutality, systematic oppression, and internet blackouts within Iran, they have captured significant social media attention and coverage outside the country, especially among the Iranian diaspora and various international organizations. This article, grounded in feminist urban theories of the Global South, analyzes the digital feminist placemaking movement in Iran. As the first counter-revolution led by women, the movement utilizes digital art, graffiti, and protest movements to embody women’s solidarity groups and sympathy rallies. Our analysis employs various digital research methods, including social media scrutiny and the study of protest illustrations. Analyzing the digital feminist placemaking in Iran will enable us to compare the commonalities, differences, challenges, and opportunities between the minorities and majorities in the world’s countries. The outcomes of this research can help international organizations such as Amnesty International and the United Nations Agency for Gender Equality and Women’s Empowerment (UN Women), as well as policymakers, institutions, academics, and NGOs, to highlight the various ways in which broader public participation could be encouraged in the process of digital feminist insurgent placemaking.
Optimal Deep Convolutional Neural Network based Fusion Model for Soil Nutrient Analysis
Authors: Sharmila.G Et al.
Published: December 2023

The vast majority of people in India, agriculture is their main line of work, and it has a large economic impact.. Soil is important for supplying vital nutrients to crops for better yield. Determining soil nutrients is certainly essential for selecting appropriate crops and monitoring growth. Common methods used by agriculturalists are inadequate to satisfy increasing demands and have to obstruct cultivating soil. For a better crop yield, agriculturalists must have an awareness regarding the soil nutrients for a specific crop. There comes a need for using Deep learning methods in soil analysis that would help farmers in the domain. This study introduces an Optimal Deep Convolutional Neural Network Fusion Model for Soil nutrient Type Classification (ODCNNF-STC) technique. The presented ODCNNF-STC technique examines the input soil images to classify them into different nutrients present in the soil. In this approach, the noise present in the soil images are initially filtered using a bilateral filter (BF) followed by contrast enhancement. The preprocessed soil images are fed to the model formed by the fusion of DenseNet201 and InceptionResNetV2 models extracting the soil images that can successfully differentiate soil nutrients. Finally, classification of soil nutrients were performed by three classifiers namely extreme learning machine (ELM), RMSProp optimizer-based 1DCNN, and RMSProp optimizer-based Stacked Auto Encoder (SAE). The experimental validation of ODCNNF-STC method is examined on real-time dataset of soil images with a maximum accuracy of 99.39% over recent methods.
The Analysis of Data Tampering and Forensics in a Cloud Environment
Authors: Vibha Upadhya Et al.
Published: December 2023

In cloud systems, where sensitive data is stored and processed remotely, data manipulation is a serious security problem. The issues of data tampering in cloud environments are explored in this research, along with the importance of forensic investigation in reducing its effects. We explore several data tampering techniques and illustrate the need for strong security measures to guard against unlawful behavior. This paper also covers forensic analysis methods, tools, and techniques that are crucial in locating, analyzing, and minimizing data tampering instances in cloud systems. The security posture associated with cloud infrastructures can be greatly improved by integrating cutting-edge forensic procedures, ensuring data integrity, confidentiality, and overall system dependability.
Deep Learning Perspectives on Efficient Image Matching in Natural Image Databases
Authors: Mirzanur Rahman Et al.
Published: December 2023

With the proliferation of digital content, efficient image matching in natural image databases has become paramount. Traditional image matching techniques, while effective to a certain extent, face challenges in dealing with the high variability inherent in natural images. This research delves into the application of deep learning models, particularly Convolutional Neural Networks (CNNs), Siamese Networks, and Triplet Networks, to address these challenges. We introduce various techniques to enhance efficiency, such as data augmentation, transfer learning, dimensionality reduction, efficient sampling, and the amalgamation of traditional computer vision strategies with deep learning. Our experimental results, garnered from specific dataset, demonstrate significant improvements in image matching efficiency, as quantified by metrics like precision, recall, F1-Score, and matching time. The findings underscore the potential of deep learning as a transformative tool for natural image database matching, setting the stage for further research and optimization in this domain.
Formulation and Comparison of Experimental based Mathematical Model with Artificial Neural Network Simulation on Surface Roughness with Burnished Spherical Surface Tool on Aluminum Alloy6351
Authors: Dnyaneshwar M. Mate Et al.
Published: December 2023

This paper deals with the effect of burnishing process on the Aluminum Alloy material 6351(HE 15) using Lathe. Surface roughness generated after the turning operation was used to ball burnishing. The surface roughness pattern which was further used to simulate ball burnishing process using ANN. Improvement in the surface roughness values achieved for tool steel after ball burnishing process was 98.2429%.
A Thermal Image based Fault Detection in Electric Vehicle Battery Cells Utilizing CNN U-Net Model
Authors: Senthilraj Et al.
Published: December 2023

It entails the formation of thermal images from battery cells under different conditions, capturing crucial thermal patterns such as hotspots, insulation degradation, and overheating. For robust model training, data preprocessing and augmentation techniques are applied. The U-Net model, known for its expertise in semantic segmentation tasks, is applied to evaluate thermal images and to detect fault-related features. The results demonstrate the U-Net's unique precision, sensitivity, and specificity in detecting thermal anomalies. This research adds to the improvement of the safety and dependability of EV battery systems, with applications in the electric mobility and automotive industries.
An AHP-Based Framework for Effective Requirement Management in Agile Software Development (ASD)
Authors: Kashif Asad Et al.
Published: December 2023

In Agile changes may be made at any time throughout the project lifetime in agile projects. These changes, however, often lead to longer turnaround times and higher costs while having a substantial influence on the activities and quality of project management. The suggested framework's main goal is to increase the effectiveness and flexibility of the requirement engineering process by successfully managing requirements changes, particularly in contexts where Agile Software Development (ASD) is practiced. The Agile methodology has gained popularity as a strategy for developing software because it can adjust to changing requirements and deliver software gradually. To make sure that the software being produced satisfies stakeholder expectations and adds value to the firm, good requirement management is essential. Using the Analytic Hierarchy Process (AHP) to prioritize requirements based on their relative relevance and urgency, this article introduces a framework for requirement management in Agile Projects. The most important requirements are taken care of first thanks to this strategy, which enables a more organized and informed decision-making process. We demonstrate the actual use of our framework in real-world contexts and highlight its efficacy in solving the issues faced by Agile projects by including a case study and an accompanying table. The suggested framework also supports the three core tenets of the Agile approach—transparency, cooperation, and continuous improvement—to foster an environment of excellence and ongoing learning within the Agile team. By developing these fundamental ideas, our framework not only supports Agile teams' continual growth and development but also helps them manage and prioritize requirements more efficiently, which ultimately improves project results and increases organizational value.
Development of efficient techniques for ASR System for Speech Detection and Recognization system using Gaussian Mixture Model- Universal Background Model
Authors: Veera V Rama Rao M Et al.
Published: December 2023

Some practical uses of ASR have been implemented, including the transcription of meetings and the usage of smart speakers. It is the process by which speech waves are transformed into text that allows computers to interpret and act upon human speech. Scalable strategies for developing ASR systems in languages where no voice transcriptions or pronunciation dictionaries exist are the primary focus of this work. We first show that the necessity for voice transcription into the target language can be greatly reduced through cross-lingual acoustic model transfer when phonemic pronunciation lexicons exist in the new language. Afterwards, we investigate three approaches to dealing with languages that lack a pronunciation lexicon. Secondly, we have a look at the efficiency of graphemic acoustic model transfer, which makes it easy to build pronunciation dictionaries. Thesis problems can be solved, in part, by investigating optimization strategies for training on huge corpora (such as GA+HMM and DE+HMM). In the training phase of acoustic modelling, the suggested method is applied to traditional methods. Read speech and HMI voice experiments indicated that while each data augmentation strategy alone did not always increase recognition performance, using all three techniques together did. Power normalised cepstral coefficient (PNCC) features are tweaked somewhat in this work to enhance verification accuracy. To increase speaker verification accuracy, we suggest employing multiple “Gaussian Mixture Model-Universal Background Model (GMM-UBM) and SVM classifiers”. Importantly, pitch shift data augmentation and multi-task training reduced bias by more than 18% absolute compared to the baseline system for read speech, and applying all three data augmentation techniques during fine tuning reduced bias by more than 7% for HMI speech, while increasing recognition accuracy of both native and non-native Dutch speech.
Robust Facial Emotion Recognition using Marine Predators Algorithm with Deep Convolutional Neural Network
Authors: K. Fathi Mary Et al.
Published: December 2023

Facial emotion recognition (FER) is a technology that includes the automatic identification and categorization of human sentiments depending on facial emotions. It leverages deep learning (DL), computer vision (CV), and machine learning (ML) methods to analyze the features of an individual's face, like the place of the mouth, eyes, eyebrows, and complete facial movements for determining their emotional conditions. Popular emotions that FER can recognize comprise surprise, sadness, happiness, fear, disgust, and anger. FER employing DL has an advanced application of artificial intelligence (AI) and deep neural networks (DNNs) that contain training methods to automatically recognize and categorize human expressions based on facial expressions. This approach normally comprises convolutional neural networks (CNNs) or highly complex models namely recurrent neural networks (RNNs) and convolutional RNNs (CRNNs) for analyzing and interpreting complex facial features and dynamics. This study introduces a new Robust Facial Emotion Recognition employing the Marine Predators Algorithm with Deep Learning (RFER-MPADL) approach. The main aim of the RFER-MPADL technique is to detect and categorize different kinds of emotions expressed in facial images. To accomplish this, the RFER-MPADL technique initially applies a bilateral filtering (BF) approach for the preprocessing step. Additionally, the RFER-MPADL technique uses the EfficientNet-B0 method for feature extraction. Moreover, the tuning process of the EfficientNet-B0 method was implemented using the MPA. Finally, the classification of facial emotions can be performed by the use of a deep autoencoder (DAE), in turn augments the overall performance of the RFER-MPADL method. The experimental analysis of the RFER-MPADL methodology is assessed on a standard facial expression dataset. The extensive outcomes exhibited the effectual performance of the RFER-MPADL methodology over other methods.
Automatic Sorting Machine Using PLC and HMI Technique
Authors: Ketki P. Kshirsagar Et al.
Published: December 2023

The automatic sorter machine using PLC is a system that sorts productsbasedonspecificcriteriasuchas size, weight, or color. The system consists of a programmable logic controller (PLC), sensors, actuators, motors, conveyor system, power supply, programming software, HMI development software, and communication protocols. The sorting process is automated, with the PLC controlling them ovement of products to the sorting stations based on the selected sensors'feedback. The HMI allows the operator to monitor and control the sorting process and displays information such as the status of the machine, the number of products sorted, and any errors or alarms. The system's design and implementation require careful consideration of the sorting requirements, selection of appropriate sensors and actuators, design of the control algorithm, programming of the PLC, development of the HMI, testing and commissioning, and maintenance and optimization. The automatic sorter machine using PLC is a reliable and efficient solution for automated sorting processes in various industries such as manufacturing, packaging, and logistics.
Deception/Truthful Prediction Based on Facial Feature and Machine Learning Analysis
Authors: Twisha Patel Et al.
Published: December 2023

The Automatic Deception detection refers to the investigative practices used to determine whether person is telling you Truth or lie. Automatic deception detection has been studied extensively as it can be useful in many real-life scenarios in health, justice, and security systems. Many psychological studies have been reported for deception detection.  Polygraph testing is a current trending technique to detect deception, but it requires human intervention and training.  In recent times, many machine learning based approaches have been applied to detect deceptions. Various modalities like Thermal Imaging, Brain Activity Mapping, Acoustic analysis, eye tracking. Facial Micro expression processing and linguistic analyses are used to detect deception. Machine learning techniques based on facial feature analysis look like a promising path for automatic deception detection. It also works without human intervention. So, it may give better results because it does not affect race or ethnicity. Moreover, one can do covert operation to find deceit using facial video recording. Covert Operation may capture the real personality of deceptive persons. By making combination of various facial features like Facial Emotion, Facial Micro Expressions and Eye blink rate, pupil size, Facial Action Units we can get better accuracy in Deception Detection.
Performance Evaluation of Routing Protocols for Vehicle Re-Routing in ITS-based Vehicular Networks
Authors: Pallavi Singh Et al.
Published: December 2023

This study aims to assess the performance of routing protocols in Intelligent Transportation System (ITS)-based vehicular networks, specifically in accident and highway scenarios. The effective management of traffic flow in these situations is crucial for ensuring the safety and smooth operation of vehicular networks. Therefore, it is imperative to evaluate routing protocols to identify the most suitable one for these scenarios. The evaluation considers various commonly used routing protocols in vehicular networks, including Ad hoc On-Demand Distance Vector (AODV), Ad hoc On-Demand Multipath Distance Vector (AOMDV), and Destination-Sequenced Distance Vector (DSDV). The evaluation is based on several performance metrics, such as packet delivery ratio, end-to-end delay, network throughput, normalized routing load, and routing overhead. These metrics provide insights into the effectiveness and efficiency of the routing protocols in handling re-routing in accident and highway scenarios. The research is divided into two modules, Module I and Module II, to evaluate the effectiveness of routing protocols in these distinct scenarios using the NS2 simulation tool. The simulation results are analyzed and compared to determine the performance of the routing protocols in each module. The findings indicate that AODV consistently achieves the highest throughput, packet delivery ratio, and lowest end-to-end delay, routing overhead, and normalized routing load, followed by AOMDV and then DSDV. The findings of this study contribute to the understanding of the strengths and weaknesses of different routing protocols in accident and highway scenarios. This knowledge can assist in the development of more efficient and reliable routing protocols for vehicular networks.
Mitigating Hotspot Problem Using Chaotic Salp Swarm Algorithm for Energy Efficient IoT Assisted Wireless Sensor Networks
Authors: A. Gayathri Et al.
Published: December 2023

Wireless Sensor Networks (WSN) and Internet of Things (IoT) continued to be pro-active study due to their far reaching applications and also a crucial technology for ubiquitous living. In WSN, energy awareness becomes a significant design problem. Clustering can be defined as a renowned energy-efficient method and renders a lot of benefits like energy competence, less delay, scalability, and lifetime; but it resulted in hot spot problems. To sort out this problem a method called unequal clustering is designed. In unequal clustering, the cluster size differs with the Base Station (BS) distance. In this study, a new Chaotic Salp Swarm Algorithm Based Unequal Clustering Approach (CSSA-UCA) methodology to resolve hot spot issues in IoT-assisted WSN is proposed. The major objective of the CSSA-UCA methodology lies in the effectual identification of CHs and unequal cluster sizes. To accomplish this, the CSSA-UCA technique initially derives the CSSA by the incorporation of chaotic notions into the conventional SSA. At the same time, a fitness function incorporating multiple input parameters was considered for unequal cluster construction. A wide range of experimental result analyses is performed to exhibit the supremacy of the CSSA-UCA technique. The experimental results stated that the CSSA-UCA technique proficiently balances energy accretion and improves the network lifetime.
Load Balancer using Whale-Earthworm Optimization for Efficient Resource Scheduling in the IoT-Fog-Cloud Framework
Authors: Gaurav Goel Et al.
Published: December 2023

Cloud-Fog environment is useful in offering optimized services to customers in their daily routine tasks. With the exponential usage of IoT devices, a huge scale of data is generated. Different service providers use optimization scheduling approaches to optimally allocate the scarce resources in the Fog computing environment to meet job deadlines. This study introduces the Whale-EarthWorm Optimization method (WEOA), a powerful hybrid optimization method for improving resource management in the Cloud-Fog environment. Striking a balance between exploration and exploitation of these approaches is difficult, if only Earthworm or Whale optimization methods are used. Earthworm technique can result in inefficiency due to its investigations and additional overhead, whereas Whale algorithm, may leave scope for improvement in finding the optimal solutions using its exploitation.  This research introduces an efficient task allocation method as a novel load balancer. It leverages an enhanced exploration phase inspired by the Earthworm algorithm and an improved exploitation phase inspired by the Whale algorithm to manage the optimization process. It shows a notable performance enhancement, with a 6% reduction in response time, a 2% decrease in cost, and a 2% improvement in makespan over EEOA. Furthermore, when compared to other approaches like h-DEWOA, CSDEO, CSPSO, and BLEMO, the proposed method achieves remarkable results, with response time reductions of up to 82%, cost reductions of up to 75%, and makespan improvements of up to 80%.
Simulation Modelling of Cloud Mini and Mega Data Centers Using Cloud Analyst
Authors: Mohit Mathur Et al.
Published: December 2023

Cloud Computing has now become a base technology for various other technologies including Internet of Things, Big Data Technologies and many other technologies, the responsibility of Cloud become critical in case of real time applications where the cloud services are required in real time. Delay in the response from Cloud may lead to serious consequences even loss of lives where the processes data from cloud must reach within predefined time interval. The performance of Cloud has experienced delays with the current infrastructure due to multiple issues in Traditional Cloud Network Model. The Paper suggests a proposed architecture Cloud Mini Data Centers simulated using Cloud Analyst to minimize the delays of Cloud Service delivery. The paper also simulate traditional cloud Network model using Cloud Analyst and provides a comparative study of both models.
Importance of Machine Vision Framework with Nondestructive Approach for Fruit Classification and Grading: A Review
Authors: D. Baswaraj Et al.
Published: December 2023

Machine vision technology has gained significant importance in the agricultural industry, particularly in the non-destructive classification and grading of fruits. This paper presents a comprehensive review of the existing literature, highlighting the crucial role of machine vision in automating the fruit quality assessment process. The study encompasses various aspects, including image acquisition techniques, feature extraction methods, and classification algorithms. The analysis reveals the substantial progress made in the field, such as developing sophisticated hardware and software solutions, which have improved accuracy and efficiency in fruit grading. Furthermore, it discusses the challenges and limitations, such as dealing with variability in fruit appearance, handling different fruit types, and real-time processing. The identification of future research needs emphasizes the potential for enhancing machine vision frameworks through the integration of advanced technologies like deep learning and artificial intelligence.Additionally, it underscores the importance of addressing the specific needs of different fruit varieties and exploring the applicability of machine vision in real-world scenarios, such as fruit packaging and logistics. This review underscores the critical role of machine vision in non-destructive fruit classification and grading, with numerous opportunities for further research and innovation. As the agricultural industry continues to evolve, integrating machine vision technologies will be instrumental in improving fruit quality assessment, reducing food waste, and enhancing the overall efficiency of fruit processing and distribution.
Ensemble-Based Machine Learning Approach for Real-Time Person Counting in an Instant Attendance System
Authors: Kumar Janardan Patra Et al.
Published: December 2023

Real-time attendance systems have become indispensable in various domains, including educational institutions and workplaces, as they automate attendance tracking and improve efficiency. This paper introduces a robust real-time attendance system that combines OpenCV and the You Only Look Once (YOLO) model. By integrating computer vision and deep learning techniques, the system achieves accurate and rapid face detection and recognition. Our proposed system utilizes OpenCV, a powerful computer vision library, to capture video streams from cameras. The YOLO model, a cutting-edge real-time object detection algorithm, is employed to identify and localize faces within the video frames. Thanks to YOLO's efficiency, the system ensures real-time processing, enabling seamless attendance recording. To enhance accuracy, the system employs a two-step approach consisting of face detection and face recognition. During the face detection phase, the YOLO model detects bounding boxes around faces. Subsequently, the system matches these detected faces against a pre-existing database of enrolled individuals using face recognition techniques. To improve performance, transfer learning techniques are applied to fine-tune the YOLO model on a diverse dataset containingvarious face images. This adaptation process ensures high precision and recall rates, even in challenging conditions such as varying lighting and occlusion. Experimental results demonstrate the effectiveness of the proposed real-time attendance system, achieving a high accuracy rate suitable for practical applications. Its real-time performance allows for seamless integration into existing attendance management workflows, resulting in time savings and improved administrative processes. The core intention of this paperwork is to develop a GUI page using Python programming, which will display the number of students who are present and number of students who are absent along with the number of students when section details are entered manually. In addition to this, pictures of the students who are present are displayed on the framework. It is called the real-time face detection which is very beneficial for managingacademic activity.
A Survey and Implementation of Machine Learning Algorithms for Customer Churn Prediction
Authors: Snehal Rathi Et al.
Published: December 2023

Estimating customer traffic is an important task for businesses because it helps them identify customers who are most likely to leave and take preventative measures to retain them by improving customer satisfaction and further increasing their own revenue. In this article, we focus on developing a machine-learning model for predicting customer churn using historical customer data We performed engineering operations on the data, addressed the missing digits, coded the categorical variables, and preprocessed the data before evaluating it using a variety of performance indicators, including accuracy, precision, recall, f1 score, and ROC AUC_Score. Our feature significance analysis revealed that monthly fees, customer tenure, contract type, and payment method are the factors that have the most impact on forecasting customer churn. Finally, we conclude the best-performing model, the Soft Voting Classifier, implemented on the four best-performing classifiers with a good accuracy of 0.78 and a relatively better ROC AUC_Score of 0.82.
An Analysis of Malicious URL Detection Using Deep Learning
Authors: Maruti Patil Et al.
Published: December 2023

Considerable progress has been achieved in the digital domain, particularly in the online realm where a multitude of activities are being conducted. Cyberattacks, particularly malicious URLs, have emerged as a serious security risk, deceiving users into compromising their systems and resulting in annual losses of billions of dollars. Website security is essential. It is critical to quickly identify dangerous or bad URLs. Blacklists and shallow learning are two techniques that are being investigated in response to the threat posed by malicious URLs and phishing efforts. Historically, blacklists have been used to accomplish this. Techniques based on blacklists have limitations because they can't detect malicious URLs that have newly generated. In order to overcome these challenges, recent research has focused on applying machine learning and deep learning techniques. By automatically discovering complex patterns and representations from unstructured data, deep learning has become a potent tool for recognizing and reducing these risks. The goal of this paper is to present a thorough analysis and structural comprehension of Deep Learning based malware detection systems. The literature review that covers different facets of this subject, like feature representation and algorithm design, is found and examined. Moreover, a precise explanation of the role of deep learning in detecting dangerous URLs is provided.
-Memory Computing Based Reliable and High Speed Schmitt trigger 10T SRAM cell design
Authors: Mucherla Usha Rani Et al.
Published: December 2023

Static random access memories (SRAM) are useful building blocks in various applications, including cache memories, integrated data storage systems, and microprocessors. The von Neumann bottleneck difficulties are solved by in-memory computing. It eliminates unnecessary frequent data transfer between memory and processing units simultaneously. In this research, the replica-based 10T SRAM design for in-memory computing (IMC) is designed by adapting the word line control scheme in 14nm CMOS technology. In order to achieve high reading and writing capability, the Schmitt trigger inverter was used for energy-saving and stable use. To speed up the writing process of the design, a single transistor is inserted between the cross-coupled inverters. In addition, to increase the node capacity, the voltage boosting circuitry is emphasized. The adaptive word line control scheme was utilized by integrating the replica column based circuit. The Replica approach regulates signal flow through the core by using a dummy column and a dummy row in RAM. To demonstrate the viability of the suggested design, the simulated outcomes are contrasted with those of existing designs. The various performance metrics examined are Read Static Noise Margin (RSNM), Write (WSNM), Hold (HSNM), Read Access Delay (RAD), Write Access Delay (WAD), Read performance and Write performance the varying supply voltage is evaluated.
Comparative Analysis Association and Prediction of Various Phenotypic Traits of Oryza Sativa
Authors: B. Kiranmai Et al.
Published: December 2023

Understanding the genotype-phenotype relationship and accurately predicting breeding values are crucial aspects of crop improvement programs. This paper investigates the genetic basis ,association of phenotypic trait height and yield and predicts the phenotypic traits of Oryza Sativa (rice) through a comprehensive approach encompassing genome-wide association studies (GWAS), phylogenetic analysis, machine learning algorithms, and the development of a graphical user interface (GUI) application. Genotypic and phenotypic data were collected from the RiceVarMap database. The genotypic information consisted of gene variation IDs, while the phenotype data included plant height. Data preprocessing involved the creation of a sequence. fasta file and multiple sequence alignment using the ClustalW tool. A phylogenetic tree was then constructed to analyse the subpopulations of Oryza Sativa. Clustering techniques were applied to further explore the genetic relationships among the samples. A GWAS file was generated to identify associations between genotype and phenotype. Subsequently, machine learning algorithms were employed for the classification and prediction of genomic estimated breeding values (GEBV) for height and yield traits. Random Forest emerged as the most accurate algorithm with 85% accuracy. To facilitate user interaction and data exploration, a GUI application was developed using Flask, allowing users to access the phylogenetic tree, height, and yield information, GWAS results, and make predictions.  We explored there is a strong positive association between phenotypic trait height and yield.
Bidirectional Encoder Representations Transformers for Improving CNN-LSTM Covid-19 Disease Detection Classifier
Authors: Jinu Paulson Siluvai Rathinam Et al.
Published: December 2023

Early identification of COVID-19 diseased persons is crucial to avoid and prevent the transmission of the SARS-CoV-2 virus. To achieve this, lung Computed Tomography (CT) scan segmentation and categorization models have been broadly developed for COVID-19 diagnosis. Amongst, Multi-Scale function learning with an Attention-based UNet and Marginal Space Deep Ambiguity-attentive Transfer Learning (MS-AUNet-MSDATL) framework is developed to concurrently segment the COVID-19 infected regions and classify their risk levels from the CT/Chest X-Ray (CXR) scans. This model utilizes Convolutional Neural Network–Long Short Term Memory (CNN-LSTM) as a classifier for proper recognition. Although CNN-LSTM efficiently learns the spatial and temporal data, but it highly ignores the pixels and their adjacent information which results in lower classification rate. So, in this paper, Bidirectional Encoder Representations from Transformers (BERT) is introduced along with CNN-LSTM in MS-AUNet-MSDATL model to resolve the above mentioned issues for efficient COVID-19 risk level classification. Initially, the segmented CT and CXR images from MS-AUNet is given as input to the BERT model. BERT structure consist of stack of transformer encoder layers which extracts fixed features from a pre-trained models to obtain the numerical representation of the given image.  Then, the numerical expressions from the BERT are transformed to pre-learned CNN model for selecting the important features from the given representations.  The LSTM model receives the CNN output and generates a new representation based on the data order. In addition, Fully Connected Layer (FCL) maps the CNN-LSTM results into categorization classes to learn pixels and their adjacent information for COVID-19 risk level detection and diagnosis.  The complete work is termed as MS-AUNet-EMSDATL.  Finally, the test findings shows that the MS-AUNet-MS-B-DATL achieves accuracy of 98.67% and 98.55% on CT and CXR images compared to the other existing frameworks.