bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Improved Prediction of Software Defects Using Machine Learning-based Boosting Techniques with NASA Dataset
Authors: Goyal Jayanti , Sinha Ripu Ranjan
Published: October 2023

Predicting when and where bugs will appear in software may assist improve quality and save on software testing expenses. Predicting bugs in individual modules of software by utilizing machine learning methods. There are, however, two major problems with the software defect prediction dataset: Social stratification (there are many fewer faulty modules than non-defective ones), and noisy characteristics (a result of irrelevant features) that make accurate predictions difficult. The performance of the machine learning model will suffer greatly if these two issues arise. Overfitting will occur, and biassed classification findings will be the end consequence. In this research, we suggest using machine learning approaches to enhance the usefulness of the CatBoost and Gradient Boost classifiers while predicting software flaws. Both the Random Over Sampler and Mutual info classification methods address the class imbalance and feature selection issues inherent in software fault prediction. Eleven datasets from NASA's data repository, "Promise," were utilised in this study. Using 10-fold cross-validation, we classified these 11 datasets and found that our suggested technique outperformed the baseline by a significant margin. The proposed methods have been evaluated based on their abilities to anticipate software defects using the most important indices available: Accuracy, Precision, Recall, F1 score, ROC values, RMSE, MSE, and MAE parameters. For all 11 datasets evaluated, the suggested methods outperform baseline classifiers by a significant margin. We tested our model to other methods of flaw identification and found that it outperformed them all. The computational detection rate of the suggested model is higher than that of conventional models, as shown by the experiments..
Diagnosis and Stage Determination of CT Scanned Images of Lung Cancer using Hybrid model
Authors: Waghere Sandhya , Saptasagar Akshata , Phursule Rajesh , Badgujar Rahul , Misal Atharva , Raskar Omkar
Published: October 2023

Varied Machine learning models are developed and are being developed for detecting and diagnosing diseases present globally. One of the unique machine learning models is proposed by the paper where the authors concentrate on combining and providing a single-stop solution as the model will be helping the medical facilities in diagnosing lung Cancer as well as determining the stage severity of the same. An amalgamation of pertinent algorithms will boost the medical processes and will help generate highly accurate results with greater precision.
PCO-IB: Churn Analysis P2P Networks Using A Peer Co-Operative Intensive Based Schema
Authors: Yaga Reddemma , Rao Ch. D. V. Subba
Published: October 2023

The Peer-to-Peer networks used technology of distributed computing.  The P2P network is essential for network communication. P2P networks are utilized in many applications due to these benefits. For example, record sharing, broadcast communications, and media streaming. There are a lot of nodes connected to the P2P network. Peers of network frequently join and leave the network at the same time. In the P2P network, this kind of paradigm is called churn. Numerous new examination works uncovered that stir is the primary issue looked by the present P2P organization. Content availability, data accuracy, and overhead were significantly reduced by the churn process. An Incentive-Based (IB) schema was proposed in this paper in order to circumvent the limitations of the P2P network for multimedia transmission. The IB schema that has been proposed encourages fair communication and cooperation among the nodes. Multimedia transmission efficiency in real-time P2P networks is maximized by the IB schema. In this paper, IB outline for the most part centered around the upgrade of the P2P organizations. The proposed construction is carried out utilizing Organization Test system. In P2P networks, the proposed IB schema improved multimedia transmission performance.
Fuzzy TOPSIS-based Secure Neighbor Discovery Mechanism for Improving Reliable Data Dissemination in Wireless Sensor Networks
Authors: Kiranmayi E. Jyothi , Rao N. V. , Nayanatara K. S.
Published: October 2023

Wireless Sensor Networks (WSNs) being an indispensable entity of the Internet of Things (IoT) are found to be more and more widely utilized for the rapid advent of IoT environment. The reliability of data dissemination in the IoT environment completely depends on the secure neighbor discovery mechanism that are utilized for effective and efficient communication among the sensor nodes. Secure neighbor discovery mechanisms that significantly determine trustworthy sensor nodes are essential for maintaining potential connectivity and sustaining reliable data delivery in the energy-constrained self organizing WSN. In this paper, Fuzzy Technique of Order Preference Similarity to the Ideal Solution (TOPSIS)-based Secure Neighbor Discovery Mechanism (FTOPSIS-SNDM) is proposed for estimating the trust of each sensor node in the established routing path for the objective of enhancing reliable data delivery in WSNs. This proposed FTOPSIS-SNDM is proposed as an attempt to integrate the merits of Fuzzy Set Theory (FST) and TOPSIS-based Multi-criteria Decision Making (MCDM) approach, since the discovery of secure neighbors involves the exchange of imprecise data and uncertain behavior of sensor nodes. This secure neighbor is also influenced by the factors of packet forwarding potential, delay, distance from the Base Station (BS) and residual energy, which in turn depends on multiple constraints that could be possibly included into the process of secure neighbor discovery. The simulation investigations of the proposed FTOPSIS-SNDM confirmed its predominance over the benchmarked approaches in terms of throughput, energy consumption, network latency, communication overhead for varying number of genuine and malicious neighboring sensor nodes in network.
Revelation of Significant Fake Rhetorical in Wrapping Bygone Utilizing Significant Learning Procedures
Authors: Pughazendi N. , Suganya T. S. , S. Soorya , Chitra D.
Published: October 2023

The developing computation control has made the profound learning calculations so powerful that making an unclear human synthesized video famously called a profound fake has got to be exceptionally straightforward. Scenarios where these practical confront swapped profound fakes are utilized to form political trouble, fake psychological warfare occasions, vindicate porn, and shakedown people groups are effortlessly imagined. In this work, we depict a modern profound learning-based strategy that can viably recognize AI-generated fake recordings from genuine videos. Our strategy can naturally be recognizing the substitution and reenactment of deep fakes. We are attempting to utilize Manufactured Intelligence (AI) to battle Fake Intelligence(AI). Our framework uses a res-next neural convolution system to extract frame-level highlights and promote the use of these highlights to prepare the long-term memory (LSTM)-based repetitive neural network (RNN) to classify whether the video is subject to art. control or not , i.e whether the video is profoundly fake or genuine. To imitate the genuine time scenarios and make the show perform way better on genuine time information, we assess our strategy on an expansive sum of adjusted and blended data-set arranged by blending the different accessible data-set like Face-Forensic, Deep Fake location challenge, and Celeb-DF. We moreover focus on  how our framework can accomplish competitive results utilizing exceptionally straightforward and strong approaches.
Framework for Virtualized Network Functions (VNFs) in Cloud of Things Based on Network Traffic Services
Authors: Yedukondalu Gangolu , Yasmeen Yasmeen , Reddy G. Vinoda , Changala Ravindra , Kotha Mahesh , Gopi Adapa , Gummadi Annapurna
Published: October 2023

The cloud of things (CoT), which combines the Internet of Things (IoT) and cloud computing, may offer Virtualized Network Functions (VNFs) for IoT devices on a dynamic basis based on service-specific requirements. Although the provisioning of VNFs in CoT is described as an online decision-making problem, most widely used techniques primarily focus on defining the environment using simple models in order to discover the optimum solution. This leads to inefficient and coarse-grained provisioning since the Quality of Service (QoS) requirements for different types of CoT services are not considered, and important historical experience on how to provide for the best long-term benefits is disregarded. This paper suggests a methodology for providing VNFs intelligently in order to schedule adaptive CoT resources in line with the detection of traffic from diverse network services. The system makes decisions based on Deep Reinforcement Learning (DRL) based models that take into account the complexity of network configurations and traffic changes. To obtain stable performance in this model, a special surrogate objective function and a policy gradient DRL method known as Policy Optimisation using Kronecker-Factored Trust Region (POKTR) are utilised. The assertion that our strategy improves CoT QoS through real-time VNF provisioning is supported by experimental results. The POKTR algorithm-based DRL-based model maximises throughput while minimising network congestion compared to earlier DRL algorithms.
LSGDM Two Stage Consensus Reaching Process for Autocratic Decision Making using Group Recommendations
Authors: Bhukya Raghuram , Suma Vanga , Rao V. Chandra Shekhar
Published: October 2023

The decision making is a general and significant action in day-to-day life. In some cases, experts cannot express their preferences using precise value due to inherent unreliability. The utilization of linguistic labels creates expert judgement more informative and consistent for decision making. The group recommendation is considered as a significant factors of e-commerce domain due to their direct impact on profit. The personalized experiments improve the engagement and the count of purchases of the customer when the recommended products are matched to the current interest.In this paper, the Large-Scale Group Decision Making (LSGDM) two stage consensus reaching process is proposed by using three various Amazon real world dataset.This proposed method permits an autocratic decision maker to utilize a different group recommendation for a sequence of decisions at highest level of consensus. The performance of the model is estimated by applying parameters like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Precision and Recall. The obtained result shows that proposed methodology provides better result while comparing various other methods.
AI Enabled Next-Generation Traffic Control System
Authors: M. Bharathwaj , J. Arunkumar , J. Ashwinsanjay , C. Kamesh , R. Salini
Published: October 2023

Traffic is one of the superior problems in modern metropolis. Fresh and advanced technology related infusions are required to supervise themselves and direct traffic signals in order to decrease the snarl-upping of traffic. Major problem is when it comes to a predicament or an emergency circumstance which affects the servicing facilities like ambulances, fire trucks, police vans etc. In this paper, we capture data from the surveillance camera and using it we will train the machine using Machine Learning and Deep Learning. So, the process goes where we use a collective number of images which can be enormous in numbers which can be used to train the model. Subsequently, the vehicles are identified, and are categorized into various classes and this classification is done by itself, as it is edified to precision. We procured 88% accuracy using YOLOv5 for vehicle recognition. Further it contributes to the future, so that road design and scrutiny can be developed and secondly the fuel usage can be controlled, and the standby time is also saved effectively. Within some period, we will be able to harmonize most of the signals, by imparting a flexible traffic management system, thus resulting in declination of traffic congestion.
Implementation of Dynamic Virtual Cloud Architecture for Privacy Data Storage
Authors: Anantharam Banoth , Sharma Neeraj , Rani B.Kavitha
Published: October 2023

Nowadays rapidly developing technologies, cloud computing offers versatile services. However, cloud computing presents a challenge to secure information sharing. Customers can securely share their data with others and remotely store it in the cloud using cloud storage services. In recent times, cloud storage typically represents as the primary method of external data storage. The primary challenge is safeguarding the cloud-based data against attacks. Over the information network, the growth of private or semi-private information has increased. The search techniques have not been addressed by privacy safeguards. As there is no suitable audit system, the validity of the stored data has become in question. In addition, user authentication presents additional difficulties. Hence in order to solve these issues, Design and implementation of dynamic virtual cloud architecture for privacy data storage is presented. In this approach, third-party audits are presented accompanied a new, regenerative public audit methodology. A distributed KDC (Key Distribution Center) is employed to encrypt the data. Documents can be stored on a private server in plain word form, which compromise the protection of privacy. As a result, system security can be improved to make the documents safer and more effective. The main objective of this Virtual Cloud Architecture is to achieve data confidentiality, as well as authenticity.
A Comprehensive Survey of Convolutional Neural Networks for Skin Cancer Classification and Prediction
Authors: Rapaka Anuj , Mallela Ramesh Babu , Rao M. V. V. Rama , Pokkuluri Kiran Sree , Rao P. B. V. Raja , Thammuluri Rajesh
Published: October 2023

Skin cancer, a prevalent and potentially fatal condition, requires early detection and precise classification to ensure effective treatment. In recent years, there has been a significant rise in the popularity of Convolutional Neural Networks (CNNs) prominence as a robust solution for image processing and analysis, significantly surpassing conventional techniques in skin cancer prediction and classification. This survey paper offers a thorough examination of CNNs and their diverse applications in diagnosing skin cancer, emphasizing their benefits, existing obstacles, and potential avenues for future research.
DDoS Attack Detection in WSN using Modified Invasive Weed Optimization with Extreme Learning Machine
Authors: Murugesh C. , Murugan S.
Published: October 2023

Wireless sensor networks (WSN) are the wide-spread methodology for its distribution of the vast amount of devoted sensor nodes (SNs) that is employed for sensing the atmosphere and gather information. The gathered information was transmitted to the sink nodes via intermediate nodes. Meanwhile, the SN data are prone to the internet, and they are vulnerable to diverse security risks, involving distributed denial of service (DDoS) outbreaks that might interrupt network operation and compromises data integrity. In recent times, developed machine learning (ML) approaches can be applied for the discovery of DDoS attacks and accomplish security in WSN. To achieve this, this study presents a modified invasive weed optimization with extreme learning machine (MIWO-ELM) model for DDoS outbreak recognition in the WSN atmosphere. In the presented MIWO-ELM technique, an initial stage of data pre-processing is conducted. The ELM model can be applied for precise DDoS attack detection and classification process. At last, the MIWO method can be exploited for the parameter tuning of the ELM model which leads to improved performance of the classification. The experimental analysis of the MIWO-ELM method takes place using WSN dataset. The comprehensive simulation outputs show the remarkable performance of the MIWO-ELM method compared to other recent approaches.
Efficient Disease Identification Method for Crop Leaf using Deep Learning Techniques
Authors: Kumar Punith , Champa H. N.
Published: October 2023

Many prime grain-producing nations have implemented steps to limit export of grains as COVID-19 has expanded over the globe; food security has sparked significant worry from a number of stakeholders. One of the most crucial concerns facing all nations is how to increase grain output. However, the diseases occur in crops remain a challenge for countless farmers, therefore it is critical to understand their severity promptly and precisely to guide the them in taking additional measures to lessen the chances of plants being affected furthermore.

This paper describes a deep learning model for the identification of crop diseases that can achieve high accuracy with low processing power. The model, called the inception v3 network, has been tested on a tomato leaf dataset and has obtained a average identification accuracy of 98.00% and further the ensemble of two inception v3 models with slight diversity achieved an accuracy of 98.11%. The results suggest that this model could be useful in improving food security by helping farmers quickly and accurately identify crop diseases and take appropriate measures to prevent further spread.
Enhancing Rice Plant Disease Recognition and Classification Using Modified Sand Cat Swarm Optimization with Deep Learning
Authors: Anandhi D. Felicia Rose , Sathiamoorthy S.
Published: October 2023

Rice plant diseases play a critical challenge to agricultural productivity and food safety. Timely and accurate recognition and classification of these ailments are vital for efficient management of the disease. Classifying and recognizing rice plant disease by implementing Deep Learning (DL) has emerged as a powerful approach to tackle the challenges associated with automated disease diagnosis in rice crops. DL, a subfield of artificial intelligence, concentrates to train neural networks with several layers for automated learning of the complex patterns and illustrations from data. In the context of rice plant diseases, DL methods can effectually extract meaningful features from images and accurately classify them into different disease categories.  Therefore, this study introduces a new Modified Sand Cat Swarm Optimization with Deep Learning based Rice Plant Disease Detection and Classification (MSCSO-DLRPDC) technique. The main objective of the MSCSO-DLRPDC technique focalize on the automated classification and recognition of rice plant ailments. To achieve this, the MSCSO-DLRPDC methodology involves two levels of pre-processing such as median filter-based noise removal and CLAHE-based contrast enhancement. Besides, Multi-Layer ShuffleNet with Depthwise Separable Convolution (MLS-DSC) methodology is utilized for feature extraction purposes. Moreover, the Multi-Head Attention-based Long Short-Term Memory (MHA-LSTM) methodology is utilized for the process of rice plant disease detection. At last, the MSCSO method is utilized for the tuning process of the MHA-LSTM approach. The MSCSO approach inspired by the collective behaviour of sand cats and the mutation operator, is implemented for optimizing the parameters of the MHA-LSTM network. To demonstrate the enhanced accomplishment of the MSCSO-DLRPDC method, a broad set of simulations were carried out. The extensive outputs show the greater accomplishment of the MSCSO-DLRPDC method over other methods. The proposed approach has the capability in assisting farmers and agricultural stakeholders in effectively managing rice plant diseases, contributing to improved crop yield and sustainable agricultural practices.
A Review: Effort Estimation Model for Scrum Projects using Supervised Learning
Authors: Sunda Neelam , Sinha Ripu Ranjan
Published: October 2023

Effort estimation practice in Agile is a critical component of the methodology to help cross-functional teams to plan and prioritize their work. Agile approaches have emerged in recent years as a more adaptable means of creating software projects because they consistently produce a workable end product that is developed progressively, preventing projects from failing entirely. Agile software development enables teams to collaborate directly with clients and swiftly adjust to changing requirements. This produces a result that is distinct, gradual, and targeted. It has been noted that the present Scrum estimate approach heavily relies on historical data from previous projects and expert opinion, while existing agile estimation methods like analogy and planning poker become unpredictable in the absence of historical data and experts. User Stories are used to estimate effort in the Agile approach, which has been adopted by 60–70% of the software businesses. This study's goal is to review a variety of strategies and techniques that will be used to gauge and forecast effort. Additionally, the supervised machine learning method most suited for predictive analysis is reviewed in this paper.
Automated Heart Syndrome Forecast Model Exploiting Machine Learning Approaches
Authors: Parisha Parisha , Srivastava Gaurav Kumar , Kumar Santosh
Published: October 2023

Heart disease is a frequent condition that appears as a result of a poor diet and an irregular lifestyle. It is one of the most frequent diseases worldwide, with numerous reasons that damage the heart and have claimed countless lives in recent years. Due to the enormous number of risk factors for heart disease, it is critical to adopt a precise and dependable approach to provide an early diagnosis and correct prognosis. As a result, there is a broad potential for implementing various types of machine learning approaches for retrieving such critical data from the database. This study evaluates numerous machine learning algorithms for correctly predicting cardiac sickness and offers analytical findings, with an emphasis on various methodologies.
An Improved Chaotic Grey Wolf Optimization Algorithm (CGWO)
Authors: Ramana Ramana , Kavitha K. , Sahu Smita Rani , Manideep B. , Kumar T. Ravi , Panda Nibedan
Published: October 2023

Grey Wolf Optimization (GWO) is a new type of swarm-based technique for dealing with realistic engineering design constraints and unconstrained problems in the field of metaheuristic research. Swarm-based techniques are a type of population-based algorithm inspired by nature that can produce low-cost, quick, and dependable solutions to a wider variety of complications. It is the best choice when it can achieve faster convergence by avoiding local optima trapping. This work incorporates chaos theory with the standard GWO to improve the algorithm's performance due to the ergodicity of chaos. The proposed methodology is referred to as Chaos-GWO (CGWO). The CGWO improves the search space's exploration and exploitation abilities while avoiding local optima trapping. Using different benchmark functions, five distinct chaotic map functions are examined, and the best chaotic map is considered to have great mobility and ergodicity characteristics. The results demonstrated that the best performance comes from using the suitable chaotic map function, and that CGWO can clearly outperform standard GWO.
Revolutionizing Agriculture: Machine Learning-Driven Crop Recommendations and Disease Detection in Fertilizer Management
Authors: Mohite Sagar , Mohite Snehal , Jadhav Swati , Jadhav Pramod A. , Singh Amanjot , Surma Paras , Namdeo Kabir
Published: October 2023

Modern agriculture faces a multitude of challenges, including crop failures, disease outbreaks, and suboptimal yields, primarily stemming from the underutilization of advanced farming technologies and a lack of expert guidance. This research proposes a comprehensive solution consisting of three key components: a Crop Disease Detection System, a Fertilizer Recommendation System, and a Crop Suggestion System. The Crop Disease Detection System employs state-of-the-art technology to evaluate crop health by analyzing the condition of plant leaves, enabling early and accurate identification of agricultural diseases. Simultaneously, the Fertilizer Recommendation System leverages soil quality data and environmental factors to provide personalized fertilizer recommendations, optimizing nutrient application. An essential element of this system is a robust soil testing module, recognizing the critical importance of assessing soil quality. Soil fertility evaluation, guided by soil pH measurements, enables precise crop predictions. The proposed system utilizes Machine Learning classification algorithms to predict suitable crops based on essential soil parameters—Phosphorus, Potassium, and Nitrogen levels. It also offers tailored fertilizer recommendations to enhance soil fertility. By implementing these interconnected solutions, this research aims to significantly improve crop yields while reducing crop damage. This holistic approach empowers farmers with the tools and knowledge needed to enhance agricultural productivity and food security. Anticipated outcomes include higher crop yields and a reduced vulnerability of crops to diseases, contributing to a more sustainable and prosperous agricultural sector.
An Ensemble Learning Approach for Fast Disaster Response using Social Media Analytics
Authors: Ayyadurai M. , Sujatha K. , Guru R. Pavithra , Sasirekha D. , Umamageswari A. , Deepa S.
Published: October 2023

Natural disaster happens, as a result of natural hazards that cause financial, environmental or human losses. Natural disasters strike unexpectedly, affecting the lives of tens of thousands of people. During the flood, social media sites were also heavily used to disseminate information about flooded areas, rescue agencies, food and relief centres. This work proposes an ensemble learning strategy for combining and analysing social media data in order to close the gap and progress in catastrophic situation. To enable scalability and broad accessibility of the dynamic streaming of multimodal data namely text, image, audio and video, this work is designed around social media data. A fusion technique was employed at the decision level, based on a database of 15 characteristics for more than 300 disasters around the world (Trained with MNIST dataset 60000 training images and 10000 testing images).  This work allows the collected multimodal social media data to share a common semantic space, making individual variable prediction easier. Each  merged numerical vector(tensors) of text and audio  is sent into the K-CNN algorithm, which is an  unsupervised learning algorithm (K-CNN), and the  image and video data is given to a deep learning  based Progressive Neural Artificial Search (PNAS).  The trained data acts as a predictor for future  incidents, allowing for the estimation of total  deaths, total individuals impacted, and total  damage, as well as specific suggestions for food,  shelter and housing inspections. To make such a prediction, the trained model is presented a satellite image from before the accident as well as the geographic and demographic conditions, which is expected to result in a prediction accuracy of more than 85%.
Supply Chain Tracing and Anti-Counterfeiting with Distributed Ledger Technology
Authors: Patil Swati Bula , Rathi Snehal Rahul , Shelke Ganesh C. , Wankhede Shalini Vaibhav , Kodmelwar Manohar K. , Pathak Kishor Renukadas , Birare Komal M.
Published: October 2023

In recent times, there has been a rampant proliferation of counterfeit products that has left a trail of devastation in the manufacturing sectors. The repercussions of this extend to companies, impacting their brand reputation, revenue streams and overall profitability. Industries like agriculture, banking, electronics, and high-value deliveries uses the emergence of blockchain technology as a powerful tool to discern between authentic and counterfeit items. Its potential as a means to curtail the influx of fake products in the market is substantial. Blockchain technology, at its core, operates as a decentralized and distributed digital ledger system, meticulously recording transactions within interconnected blocks across multiple databases. The inherent security of this technology ensures the immutability of these blocks, rendering them invulnerable to alteration or hacking. By leveraging blockchain technology, consumers can independently verify the authenticity of a product, eliminating the need for reliance on third-party intermediaries. Incorporating recent technological advancements, the utilization of Quick Response (QR) codes offers a robust approach to combat the proliferation of counterfeit goods. The integration of blockchain technology with QR codes serves as a means to uphold the integrity of products. This innovative system securely stores product details and unique codes in the form of blocks,  where QR codes play a pivotal role in collecting and matching these unique codes with entries in the blockchain database. If the QR code matches with entries in the database, the user receives a confirmation of the product's authenticity; otherwise, an alert is triggered, signaling the presence of a counterfeit product.
Intelligent Destination Recommender and Community Builder
Authors: Jeswani Ashish , Shinde Radhika , Panaskar Roma , Kanade Varad , Kolapkar Vrushali
Published: October 2023

Recommendation engines make use of machine learning techniques and generally deal with ranking and rating of products/users. With the help of this system we aim to suggest different destinations to users based on their interest and previous visits. Along with recommendations we also aspire to enable users to build travel communities for people sharing similar interests .This shall help travelers with planning ,meeting like-minded people,safety and enthralling experience.

As per the analysis done on pre-existing systems we discerned that enabling users to build a community of travelers visiting the same destination is an eccentric attribute proposed.

This distinctive attribute of building communities shall be implemented using the basics of clustering algorithms in Machine learning.
Comprehensive Analysis of Surface Morphology and Elemental Composition Changes in Bioadsorbents Before and After Cocoon Adsorption
Authors: Sahu Hemlal , Mitra Jayati Chatterjee
Published: October 2023

Adsorption processes have a wide range of applications in environmental science and materials research. Understanding the surface morphology and elemental composition of bioadsorbents is fundamental for assessing their effectiveness in these processes. In this study, we utilized advanced analytical techniques, including Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Fourier Transform Infrared Spectroscopy (FTIR), to investigate the structural and chemical changes that occur in bioadsorbents before and after cocoon adsorption. Raily cocoon, Daba BV, and Daba TV were selected as model bioadsorbents for this comprehensive analysis. This research paper investigates the surface morphology and chemical composition changes in tasar cocoons, specifically Raily cocoon, Daba BV, and Daba TV, before and after the adsorption process. The analysis was conducted using Scanning Electron Microscopy (SEM) to observe the morphological changes, and Energy Dispersive X-ray Spectroscopy (EDS) to determine the elemental composition. Furthermore, Fourier Transform Infrared Spectroscopy (FTIR) was employed to identify functional groups in the tasar cocoon components. The results reveal significant surface modifications and elemental interactions, shedding light on the adsorption capacity of tasar cocoons.
Tourism Industry: Strengthening India’s Influence
Authors: Ritesh Sharma
Published: October 2023

It is obvious that the travel sector is the most important and effective part of any nation's economy. It may be the industry with the fastest growth. It is reliable to bring a million-dollar business to any nation while also providing joy and leisure. Due to the tourism industry's recent rapid growth, a number of locations have emerged as key tourist attractions for both local and international travelers. India is a nation renowned for its extensive social history. The country is strong in terms of its legalism, otherworldliness, and everyday splendor because of the people who reside there, who come from many cases, religions, and ethnic backgrounds. By examining the expansion of the travel industry areas in India, this investigation seeks to contribute to the existing work on the travel business. This essay seeks to understand how the travel industry helps to make India a sensitive power and creates key areas of strength for it. Exploratory in character, the evaluation adopts a subjective methodology combining member perception at various Indian tourism industry locations and optional information investigation. The findings show that India is widely regarded as a potential market for the travel sector.
Fen Bilgisi Öğretmen Adaylarının Sanal Müze Uygulamalarına Yönelik Görüşleri
Authors: ÖNER ARMAĞAN Fulya , EZBERCİ ÇEVİK Ebru , KÖKSAL Ela
Published: October 2023

Araştırmanın amacı fen bilgisi öğretmen adaylarının fen eğitiminde sanal müze uygulamalarına yönelik görüşlerini incelemektir. Çalışmada nitel araştırma desenlerinden durum çalışması kullanılmıştır. Çalışmanın katılımcıları, fen bilgisi öğretmenliği 3. ve 4. sınıfında öğrenim gören 29 öğretmen adayıdır. Çalışmada, sanal gezinti öncesi ve sonrasında öğretmen adaylarının sanal müze hakkındaki görüşlerini belirlemek amacıyla açık uçlu sorulardan oluşan soru formu kullanılmıştır. Elde edilen nitel verilerin çözümlenmesinde betimsel analizden yararlanılmıştır. Araştırma sonunda, öğretmen adaylarının sanal müzeyi uygulama öncesi bir erişim şekli, uygulama sonrasında bir teknoloji olarak tanımladıkları belirlenmiştir. Bunun yanı sıra, öğretmen adaylarının uygulama öncesinde sanal müzenin etkili kullanılabilmesi için sahip olması gereken özellik olarak en fazla ‘teknolojiyi kullanabilme’ görüşünü belirttikleri, uygulama sonrasında ise en fazla ‘teknolojik yeterlik’ yanıtını verdikleri belirlenmiştir. Elde edilen sonuçlar doğrultusunda üniversitelerde müze eğitimi dersinin seçmeli/zorunlu ders olacak şekilde düzenlemeler yapılması, sanal müze kullanımına yönelik etkinlikler düzenlenmesi, öğretmen adaylarının sanal müzeleri bir dijital ders materyali olarak daha fazla kullanmaları yönünde teşvik edilmesine yönelik öneriler sunulmuştur.
Yeşil Kimya Farkındalık Ölçeğinin Geliştirilmesi: Geçerlik ve Güvenirlik Çalışması
Authors: TOPALOĞLU Sema , GENEL Yaşar , BAKIRCI Hasan
Published: October 2023

Bu araştırmanın amacı, biyoloji, fizik ve kimya öğretmenlerinin yeşil kimyaya yönelik farkındalıklarını ölçen geçerli ve güvenilir bir ölçme aracı geliştirmektedir. Bu bağlamda araştırma, ölçek geliştirme çalışması olarak tasarlanmıştır. Ölçme aracının geliştirildiği süreçte Açımlayıcı Faktör Analizi (AFA) ve Basit Doğrusal Regresyon Analizi uygulamaları için araştırmaya dört farklı ilden (Balıkesir, Elazığ, İstanbul ve Van) toplam 382 fizik, kimya, biyoloji öğretmeni çevrimiçi olarak katılım sağlamıştır. Yapılan analizler sonucunda ölçeğin üç faktörlü bir yapıda olduğu ve ölçek maddelerine ait faktör yük değerlerinin 0.507 ile 0.854 arasında değerler aldığı belirlenmiştir. Araştırma sonucunda öğretmenlerin yeşil kimyaya yönelik farkındalıklarını ölçmeye yönelik 23 maddeden oluşan üç faktörlü bir yapıda geçerli ve güvenilir bir ölçme aracı geliştirilmiştir.
Ortaokul Öğrencilerinin Grafik Algıları ve Grafik Oluşturma Yeterlikleri: Bir Durum Çalışması
Authors: TOSUN Tuğba , ÖZEN ÜNAL Deniz , AKSU Gökhan
Published: October 2023

Bu araştırmada sekizinci sınıf öğrencilerinin grafik algılarının ve grafik oluşturma yeterliklerinin incelenmesi amaçlanmaktadır. Araştırmanın yöntemi durum çalışması olup veri toplama aracı olarak klinik görüşme görevleri kullanılmıştır. Araştırmanın çalışma grubunu akademik başarıları yüksek beş öğrenci oluşturmaktadır. Elde edilen veriler nitel veri analizi yöntemlerine göre analiz edilmiştir. Araştırmanın sonucunda, grafik algısı teorik çatısı bağlamında sekizinci sınıf öğrencilerinin veri okuma düzeyinde herhangi bir sorun yaşamadıkları fakat üst düzey beceriler gerektiren veriler arası okuma ve veri ötesi okuma düzeyindeki beceriler noktasında zorluklar yaşadıkları tespit edilmiştir. Ayrıca öğrencilerin grafik eksenlerinin ölçeklendirilmesi, eksenlerin adlandırılması, verilen noktalardan geçen doğrusal grafiği çizme ve uygun grafik türüne karar verme gibi bir takım zorluklarla karşılaştıkları ve bu noktada yanılgılara sahip oldukları görülmüştür.
Examining the Effectiveness of School-Based Interventions Developed to cope with and Prevent Substance Use: A Systematic Literature Review
Authors: ÇELİK Feride , ULUSOY Yağmur
Published: October 2023

In this research, between January 2012 and December 2022, four databases, Eric, Science Direct, Web of Science and Scopus, were searched for studies written in English and 7 studies were found to meet the necessary conditions for review. The findings show that the most frequently used substances by adolescents are cigarettes, alcohol, opium, nass, cannabis, and methamphetamine. Also, it was concluded that group intervention shave an effect on perceived parent involvement in adolescents, prevent and decrease adolescent substance use, resist peer pressure, help in being able to say no, and increaseparents’ relationship satisfaction with their adolescents. These findings suggest that school-based group interventions that involved adolescents and parents together are functional in preventing and reducing adolescent substance use. However, the fact that half of the risk of bias assessment is ambiguous suggests that research that integrates experimental studies with low risk of bias is needed.
Ondalık Gösterim Öğretiminde Ele Alınan Ters Yüz Öğrenme Modeline Dayalı Etkinliklerin Altıncı Sınıf Öğrencilerine Uygulanmasına Bir Bakış
Authors: KORKMAZ Faruk , HACISALİHOĞLU KARADENİZ Mihriban
Published: October 2023

Bu araştırmanın amacı, ondalık gösterim öğretiminde ele alınan Ters Yüz Öğrenme Modeli’ne (TYÖM) dayalı etkinliklerin altıncı sınıf öğrencilerine uygulanma sürecinin nasıl gerçekleştiğini ortaya koymaktır. Nitel araştırma yöntemlerinden durum çalışması ile yürütülen araştırmanın çalışma grubunu, Trabzon ilinin Beşikdüzü ilçesinde bir devlet okulunun altıncı sınıfında öğrenim gören, kolay ulaşılabilir durum örneklemesi ile seçilen 20 öğrenci oluşturmaktadır. Çalışmada, Ters Yüz Öğrenme Etkinliklerinden elde edilen verilerin analizinde Dört Dereceli Rubrik ile Puan Verme Ölçeği kullanılmıştır. Çalışma sonucunda TYÖM ile planlanan öğretim uygulamalarının, öğrenmeye katkı sağladığı ve konunun anlaşılmasını kolaylaştırdığı açığa çıkmıştır. Bu doğrultuda TYÖM’nin, matematiğin diğer konularının öğretim süreçlerine de entegre edilmesi önerilmektedir.
Altıncı Sınıf Öğrencilerinin Yeni Nesil Matematik Problemlerine İlişkin Metaforik Algıları
Authors: ŞEKER Fadime , BAĞDAT Osman
Published: October 2023

Bu araştırmanın amacı ortaokul 6. sınıf öğrencilerinin yeni nesil matematik problemlerine yönelik sahip olduğu algıları metaforlar aracılığı ile belirlemektir. Çalışmada nitel araştırma yaklaşımı benimsenmiştir. Araştırmanın katılımcıları bir devlet okulunda öğrenim gören 67 adet 6. sınıf öğrencisinden oluşmaktadır. Araştırmada veriler “Yeni nesil matematik problemleri …gibidir. Çünkü…” sorusunu içeren bir açık uçlu anket aracılığıyla toplanmıştır. Veriler içerik analizi tekniğine göre çözümlenmiştir. Araştırma sonucunda altıncı sınıf öğrencilerinin yeni nesil matematik problemlerine yönelik algılarına ilişkin 75 metafor ve 11 adet kategori belirlenmiştir. En çok karşılaşılan metaforlar “zor” ve “karmaşık” olmuştur. Araştırma sonucunda yeni nesil matematik problemlerine yönelik algılarına ilişkin öğrencilerin olumsuz algılarının daha yüksek olduğu belirlenmiştir. Araştırmadan elde edilen bulgular ışığında öğretmen, program yapıcı ve ders kaynağı yazarlarına birtakım önerilerde bulunulmuştur.
New Data on the Scarabaeinae (Coleoptera: Scarabaeidae) Fauna of Bozdağ at Eskişehir in Central Anatolia Region
Authors: Gülmez Mehmet , Şenyüz Yakup
Published: October 2023

Insects are generally dominant in terrestrial ecosystems and play a role in ecologically important functions and ecological processes. Dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) are essential for the first decomposition, especially in forest/pasture ecosystems. This study aimed to determine the faunistic data of the subfamily Scarabaeinae (Coleoptera: Scarabaeidae) depending on three different altitudes in the Bozdağlar region of Eskişehir. In this study, the pitfall trap method collected totally of 40 species (34 species and 6 subspecies) belonging to 10 genera belonging to the Scarabaeinae subfamily of the Scarabaeidae family. Cheironitis haroldi Ballion, 1871, Caccobius. mundus Ménétriés, 1839, Onthophagus. (A.) falzonii Goidanich, 1926, Onthophagus (P.) carpanetoi Pittino, 1982, Onthophagus (P.) similis Scriba, 1790, Onthophagus (P). t. truchmenus Kolenati, 1846 and Ateuchetus armeniacus Ménétriés, 1832, are the new data for Eskişehir province.
Accounting Ethics: From Professionals Views
Authors: Septiari Dovi , Helmayunita Nayang , Serly Vanica
Published: October 2023

This research investigated the professional accounting perception about ethics. We used a semi-structured interview with 11 professional accountants that work as an auditor, tax consultant, financial accountant, and banker. The results show that accountants define ethics as rules and value. Several ethical problems like manipulation, profit management exploitation, and pressures leading to unethical actions were found in practice. The ethical value, senior's role, and corporate policy also play an essential role in helping accountants resolve ethical issues. This research has contributed to our knowledge about ethics perception from the professional perspective. Also, this finding gives important suggestions for the corporate, academics environment, and policymakers.
Association of serum pregnancy associated plasma protein: a with gestational diabetes mellitus
Authors: Begum Khadiza , Saha Mousumi , Tamanna Jesika Rizvi , Ara Banu Ferdous , Ferdous Sharmin , Rahman A. K. M. Mizanur , Akhtar Nahreen
Published: October 2023

Background: Gestational diabetes mellitus is defined as diabetes diagnosed during pregnancy that is not clearly overt diabetes. GDM has many adverse consequences on the health of mother and fetus.
Methods: This cohort study was carried out involving 77 women of 11 to14-week pregnant attending in the Gynecology & Obstetrics and Fetomaternal Medicine OPD, BSMMU from September 2020 to August 2021.
Results: Respondents were divided into two groups. Low PAPP-A group (<0.5MoM) and normal PAPP-A group (>0.5MoM). In low PAPP-A group, out of 16 respondents, 8 (42.1%) developed GDM and remaining 8(13.8%) were euglycemic. Whereas, in normal PAPP-A group, out of 61 respondents, majority 50 (86.2%) were euglycemic and only 11 (57.9%) women developed GDM. A total 19 (24.68%) respondents developed GDM from both low and normal PAPP-A group. ROC analysis of PAPP-A level for detection of GDM, a AUC value of 0.889 (95% CI 0.818-0.960) found which was statistically significant. A cut off value of ≤16.80 showed the highest Youden index (0.652) with 89.4% sensitivity and 81% specificity, the accuracy was 83.35. Moreover, a cut off value ≤16.80 showed PPV and NPV of 62.1% and 95.9%, respectively. PAPP-A level of GDM detected (10.32±5.56) µg/ml was significantly lower from non GDM mothers (25.08 ±9.85) µg/ml, where p<0. 001.
Conclusions: Study finding revealed that maternal serum PAPP-A level was lower in 11-14 weeks of pregnancy who subsequently developed GDM. So, a low PAPP-A level (<0.5 MoM) in 11-14 weeks of pregnancy is associated for development of GDM.
Study of community awareness regarding contraception and its practices
Authors: Pathak Pallavi , Parihar Akansha , Rohini P.
Published: October 2023

Background: Family planning (FP) programmes impact women’s health by providing universal access to sexual and reproductive healthcare services and counselling information. The ability to decide freely the number, spacing and timing of one’s children is a basic human right, endorsed at the International Conference on Population and Development in 1994. Family planning programmes are associated with lower fertility and lower maternal mortality. Through family planning programmes, women gain access to contraceptives, increasing the likelihood that they can achieve their desired family size.
Methods: Married women 18-49 years of age were recognised and randomly selected from different areas Indore. Preformed questionnaire containing demographic information and relevant questions was used for data collection.
Results: About 80.6% women were aware about PNC contraception. Majority undergone female sterilization (40.0%), 24.8% were using condoms, followed by IUCDs (6.5%) and contraceptive pills (4.7%).
Conclusions: In this cross sectional study, awareness regarding contraceptive practices and taboos was not found to be at par with the requirement and lower in concurrence with low education, income, rural residence. Specific intervention programs need to be planned to improve their health practices and thereby improving the health status of the mother and child.
A clinical study of ectopic pregnancy at a tertiary care hospital of North Delhi
Authors: Sweta  , Anjum Shazia , Mendiratta Suman Lata
Published: October 2023

Background: aim was to study the risk factors, clinical presentation and management of ectopic pregnancy in a tertiary care centre of North Delhi
Methods: This was a prospective observational study conducted in the department of obstetrics and gynaecology Hindu Rao hospital and NDMC medical college, Delhi. A total of 65 cases of ectopic pregnancy were analyzed, over a period of 1 year that is from September 2020 to August 2021
Results: The incidence of ectopic pregnancy was 12.8/1000 deliveries. Majority of cases were multigravidas and of gestation age between 6 to 10 weeks. The common risk factors were PID, contraception failure (IUCD), tubal ligation, infertility, ART procedures, tubal surgery, and previous ectopic and previous abortion. Ultrasound, urine pregnancy test, serum β-hCG titre were done. Laparotomy was done in all cases. Surgery in form of salpingectomy, salpingo-oophorectomy, and uterine reconstruction was done. No maternal mortality occurred.
Conclusions: Diagnosis of ectopic pregnancy requires clinical suspicion and supportive investigations like UPT, ultrasonography, β-HCG and laparoscopy. It is an important cause of admission to Hindu Rao hospital as maternal near miss cases.