bluuebunny/crossref_metadata_2025 huggingface dataset:
Forecasting the Missing Links in Heterogeneous Social Networks
Authors: Gupta Atika , Matta Priya , Pant Bhasker
Published: October 2023

Social network analysis has gained attention from several researchers in the past time because of its wide application in capturing social interactions. One of the aims of social network analysis is to recover missing links between the users which may exist in the future but have not yet appeared due to incomplete data. The prediction of hidden or missing links in criminal networks is also a significant problem. The collection of criminal data from these networks appears to be incomplete and inconsistent which is reflected in the structure in the form of missing nodes and links. Many machine learning algorithms are applied for this detection using supervised techniques. But, supervised machine learning algorithms require large datasets for training the link prediction model for achieving optimum results. In this research, we have used a Facebook dataset to solve the problem of link prediction in a network. The two machine learning classifiers applied are LogisticRegression and K-Nearest Neighbour where KNN has higher accuracy than LR. In this article, we have proposed an algorithm Graph Sample Aggregator with Low Reciprocity, (GraphSALR), for the generation of node embeddings in larger graphs which use node feature information.
A Comprehensive Study of Different Skin Cancer Detection Models Using Deep Learning Techniques
Authors: Kumar Gottapu Santosh , Prashanti Gottapu , Jagadeesh Gurugubelli
Published: October 2023

Skin cancer is a highly prevalent disease that exhibits rapid growth worldwide. The timely identification and accurate diagnosis of skin cancer are of paramount importance in the context of preventive measures. The identification of skin cancer in its early stages poses a significant challenge for dermatologists. In recent years, machine learning techniques have been widely employed in both supervised and unsupervised learning tasks to address this issue. In this study, different existing techniques to detect different types of skin cancers and the evaluation metrics to assess their performance are dealt with.
Congestion Aware WSN-IoT-Application Layer Protocols for Healthcare Services
Authors: Anuradha Anuradha , Gulati Tarun , Narang Pravin , Sharma Anupam Kumar , Ghate Shubhangi N. , Bindal Amit Kumar
Published: October 2023

In the healthcare industry, WSN-IoT networks can be used to gather patient data for statistical purposes. IoT-based application-level protocols do not take into account these facts while forwarding the data to the gateway or server, which may degrade the network performance if the data was collected from a patient with ordinary/critical health issues and the route was busy or congested. In this paper, we'll look at the performance of two application layer protocols (i.e. CoAP and MQTT) within the constraints of a scalable network by integrating a congestion-aware scheme with them.
A Comprehensive Investigation of Beam Management Through Conventional and Deep Learning Approach
Authors: Pawar Sheetal , Venkatesan Mithra
Published: October 2023

5G spectrum uses cutting-edge technology which delivers high data rates, low latency, increased capacity, and high spectrum utilization. To cater to these requirements various technologies are available such as Multiple Access Technology (MAT), Multiple Input Multiple Output technology (MIMO), Millimetre (mm) wave technology, Non-Orthogonal Multiple Access Technology (NOMA), Simultaneous Wireless Information and Power Transfer (SWIPT). Of all available technologies, mmWave is prominent as it provides favorable opportunities for 5G. Millimeter-wave is capable of providing a high data rate i.e., 10 Gbit/sec. Also, a tremendous amount of raw bandwidth is available i.e., around 250 GHz, which is an attractive characteristic of the mmWave band to relieve mobile data traffic congestion in the low frequency band. It has a high frequency i.e., 30 – 300 GHz, giving very high speed. It has a very short wavelength i.e., 1-10mm, because of this it provides the compact size of the component. It will provide a throughput of up to 20 Gbps. It has narrow beams and will increase security and reduce interference. When the main beam of the transmitter and receiver are not aligned properly there is a problem in ideal communication. To solve this problem beam management is one of the solutions to form a strong communication link between transmitter and receiver. This paper aims to address challenges in beam management and proposes a framework for realization. Towards the same, the paper initially introduces various challenges in beam management. Towards building an effective beam management system when a user is moving, various steps are present like beam selection, beam tracking, beam alignment, and beam forming. Hence the subsequent sections of the paper illustrate various beam management procedures in mmWave using conventional methods as well as using deep learning techniques. The paper also presents a case study on the framework's implementation using the above-mentioned techniques in mmWave communication. Also glimpses on future research directions are detailed in the final sections. Such beam management techniques when used for mmWave technology will enable build fast, efficient, and capable 5G networks.
Deep Learning Frameworks for Cardiovascular Arrhythmia Classification
Authors: Malleswari Pinjala N. , Padala Srinivas , Pullarao Matta Venkata , Sankar M. Ravi , Mounika Y.
Published: October 2023

Arrhythmia classification is a prominent research problem due to the computational complexities of learning the morphology of various ECG patterns and its wide prevalence in the medical field, particularly during the COVID-19 pandemic. In this article, we used Empirical Mode Decomposition and Discrete Wavelet Transform for preprocessing and then the modified signal is classified using various classifiers such as Decision Tree, Logistic Regression, Gaussian Naïve Bayes, Random Forest, Linear  SVM, Polynomial SVM, RBF SVM, Sigmoid SVM and Convolutional Neural Networks. The proposed method classify the data into five classes N (Normal), S (Supraventricular premature) beat, (V) Premature ventricular contraction, F (Fusion of ventricular and normal), and Q, (Unclassifiable Beat) using softmax regressor at the end of the network. The proposed approach performs well in terms of classification accuracy when tested using ECG signals acquired from the MIT-BIH database. In comparison to existing classifiers, the Accuracy, Precision, Recall, and F1 score values of the proposed technique are 98.5%, 96.9%, 94.3%, and 91.32%, respectively.
An Open-Source Platform for Real-Time Preliminary Diagnosis amongst Adults using Data Analytics
Authors: Sharma Kalpana , Sharma Sital , Dhimal Sunil , Sen Biswaraj , Pradhan Ashis , Singh Vikash Kumar
Published: October 2023

Depression can be defined as a mental health disorder characterized by persistently depressed mood, loss of interest in activities, causing significant impairment in daily life.  Technical intervention to screen depression in non-clinical population which records, classify depression on the basis of severity and provide features or predictors that discriminate the classification of depression among non-clinical population comprising of college students is the main area of the study. Beck Depression Inventory – II (BDI-II), as per Diagnostic and Statistical manual of Mental disorder (DSM IV) is used to screen depression and its severity. Indicators are determined on the basis of how well the features or predictors can discriminate the classes of depression severity.  Providing quality indicators which help in supporting the process can be considered as symptoms for screening depression.  Descriptive analytics is used in order to find the underlying pattern of the responses captured, factor analysis groups variables on the basis of correlation between patterns of the responses to reduce dimension.  The approach for supervised descriptive analysis method that takes BDI-II questions as features and refine the features using information gain and linear discriminant analysis as feature selection algorithm. The classification of severity of depression is done using Support vector machine (SVM)..
Arabic Text Summarization Challenges using Deep Learning Techniques: A Review
Authors: Souri Adnan , Achhab Mohammed Al , Mohajir Badr Eddine El , Naoum Mohamed , Hichami Outman El , Zbakh Abdelali
Published: October 2023

Text summarization is a challenging field in Natural Language Processing due to language modelisation and used techniques to give concise summaries.  Dealing with Arabic language does increase the challenge while taking into consideration the many features of the Arabic language, the lack of tools and resources for Arabic, and the Algorithms adaptation and modelisation. In this paper, we present several researches dealing with Arabic Text summarization applying different Algorithms on several Datasets. We then compare all these researches and we give a conclusion to guide researchers on their further work.
Enhanced Spam Detection System for Twitter Social Networking Platform
Authors: Yadav Partibha , Kumar Ajay , Shivani Shivani , Hooda Reena , Sudhir Sudhir , Pooja Pooja
Published: October 2023

Twitter social site is one of the most popular Online Social Networking Site (OSN) used by popular people such as Ministers, businessman, large companies, actors to share their information. In this site, around 500 million of tweets are posted monthly by the total 313 million Twitter active users. The widespread of Twitter has drawn the interest of spammers. These malicious actors exploit the platform for various nefarious purposes, including monitoring authentic users, disseminating harmful software, and promoting their agendas through URLs embedded in tweets. They engage in tactics like secret following and unfollowing legitimate users, all with the intent of gathering sensitive information.To resolve this problem, a secure spam detection based on machine learning approach is designed. The designed used stop word removal, word to vector model to refined and dimensionally reduced the data. To enhance the quality of the data Cosine similarity is also been applied to measure the similarity score among the tweets and based upon that Artificial Neural Network (ANN) is trained. Later on, it is used to test the efficiency by examining the performance parameters in terms of precision, recall and F-measure. Also, the comparative analysis has been performed to present the efficiency of the work. The average precision, recall and F measure of proposed spam detection model of 0.9252, 0.6107 and 0.734 are obtained.
Cultivating Insight: Detecting Autism Spectrum Disorder through Residual Attention Network in Facial Image Analysis
Authors: C Gnanaprakasam , Rajagopal Manoj Kumar , Swaminathan A. , Livingston L. M. Merlin , Ramkumar G.
Published: October 2023

Revolutionizing Autism Spectrum Disorder Identification through Deep Learning: Unveiling Facial Activation Patterns. In this study, our primary objective is to harness the power of deep learning algorithms for the precise identification of individuals with autism spectrum disorder (ASD) solely from facial image datasets. Our investigation centers around the utilization of face activation patterns, aiming to uncover novel insights into the distinctive facial features of ASD patients. To accomplish this, we meticulously examined facial imaging data from a global and multidisciplinary repository known as the Autism Face Imaging Data Exchange. Autism spectrum disorder is characterized by inherent social deficits and manifests in a spectrum of diverse symptomatic scenarios. Recent data from the Centers for Disease Control (CDC) underscores the significance of this disorder, indicating that approximately 1 in 54 children are impacted by ASD, according to estimations from the CDC's Autism and Developmental Disabilities Monitoring Network (ADDM). Our research delved into the intricate functional connectivity patterns that objectively distinguish ASD participants, focusing on their facial imaging data. Through this investigation, we aimed to uncover the latent facial patterns that play a pivotal role in the classification of ASD cases. Our approach introduces a novel module that enhances the discriminative potential of standard convolutional neural networks (CNNs), such as ResNet-50, thus significantly advancing the state-of-the-art. Our model achieved an impressive accuracy rate of 99% in distinguishing between ASD patients and control subjects within the dataset. Our findings illuminate the specific facial expression domains that contribute most significantly to the differentiation of ASD cases from typically developing individuals, as inferred from our deep learning methodology. To validate our approach, we conducted real-time video testing on diverse children, achieving an outstanding accuracy score of 99.90% and an F1 score of 99.67%. Through this pioneering work, we not only offer a cutting-edge approach to ASD identification but also contribute to the understanding of the underlying facial activation patterns that hold potential for transforming the diagnostic landscape of autism spectrum disorder.
Phishing Detection using Base Classifier and Ensemble Technique
Authors: Pandey Mithilesh Kumar , Pal Rekha , Pal Saurabh , Shukla Arvind Kumar , Pandey Manish Ranjan , Shahi Shantanu
Published: October 2023

Phishing attacks continue to pose a significant threat in today's digital landscape, with both individuals and organizations falling victim to these attacks on a regular basis. One of the primary methods used to carry out phishing attacks is through the use of phishing websites, which are designed to look like legitimate sites in order to trick users into giving away their personal information, including sensitive data such as credit card details and passwords. This research paper proposes a model that utilizes several benchmark classifiers, including LR, Bagging, RF, K-NN, DT, SVM, and Adaboost, to accurately identify and classify phishing websites based on accuracy, precision, recall, f1-score, and confusion matrix. Additionally, a meta-learner and stacking model were combined to identify phishing websites in existing systems. The proposed ensemble learning approach using stack-based meta-learners proved to be highly effective in identifying both legitimate and phishing websites, achieving an accuracy rate of up to 97.19%, with precision, recall, and f1 scores of 97%, 98%, and 98%, respectively. Thus, it is recommended that ensemble learning, particularly with stacking and its meta-learner variations, be implemented to detect and prevent phishing attacks and other digital cyber threats.
Survey of Deep Fake Creation Technologies and Deep Fake Detection using LSTM
Authors: Pant Shilpa , Gosavi Chhaya
Published: October 2023

Artificial intelligence known as Deep Fake is one of many techniques that have been successfully developed in recent years for altering faces in images and videos. It can produce convincingly faked images, audio, and video. Deep Fake can create problem, especially when there is a media component involved. Even if it is helpful, when it is used maliciously, such as for disseminating fake news or cyber bullying, it can pose a threat to society. It is necessary to develop a complete fake detection method to handle such issues. Too far, numerous methods have been developed to distinguish between authentic and fraudulent videos.

The objective of this work is to give a summary of different approaches for Deep Fake creation and to provide an overview of LSTM algorithms for deep fake video detection.
A Research to Improve Contiguous Memory Allocation in Linux Kernel
Authors: Suryavanshi Anmol , Sharma Sanjeevkumar
Published: October 2023

The demand for Contiguous Memory Allocation (CMA) has witnessed significant growth in both low-end and high-end devices in recent years. However, the existing practices for utilizing CMA prove insufficient, particularly when catering to the needs of low-end (32-bit) devices. CMA, a Linux program used for memory reservation and allocation, faces limitations in its current implementations. Presently, techniques such as Scatter-Gather Direct Memory Access (DMA), Input Output Memory Management Unit (IOMMU), and Memory Reservation are commonly employed for contiguous memory allocation. Unfortunately, these methods are financially impractical for low-end devices and struggle to efficiently allocate substantial memory chunks, leading to latency concerns. In this paper, we introduce an improved CMA approach that intelligently allocates virtual memory for data mapping as needed. Alternatively, it directly allocates and deallocates physical memory without the necessity of virtual memory mapping, employing the DMA_KERNEL_NO_MAPPING attribute within the DMA Application Programming Interface (API). By adopting this method, latency is reduced, and the facilitation of larger memory allocations is promoted, addressing the limitations of the current techniques.
Customer Segmentation and Business Sales Forecasting using Machine Learning for Business Development
Authors: Malviya Pravin , Bhandari Vijay , Sisodiya Pankaj Singh , Suman Saurabh
Published: October 2023

This study explores the application of machine learning techniques for business development, focusing on sales prediction and customer segmentation, using a Walmart dataset. Performance metrics include Mean Absolute Error (MAE) and R2 scores. Our hybrid approach combines the BIRCH algorithm with time-lagged machine learning (TL-ML). The results reveal that customer segmentation significantly improves model performance across all metrics. Among the techniques tested, models incorporating customer segmentation (CS-RFR and CS-TL-ML) outperform standard Random Forest Regressor models. Specifically, CS-TL-ML shows a slight advantage in terms of both lower MAE and higher R2 scores, confirming its efficacy for sales prediction and customer segmentation tasks.
A Novel Hybrid Optimization With Ensemble Constraint Handling Approach for the Optimal Materialized Views
Authors: Vullam Nagagopiraj , Murali Krishna Chinta Venkata , Davuluri Suneetha , Rao Muvva Venkateswara , Boppana Venugopal , Gorintla Shobana , Srinivasarao Popuri
Published: October 2023

The datawarehouse is extremely challenging to work with, as doing so necessitates a significant investment of both time and space. As a result, it is essential to enable rapid data processing in order to cut down on the amount of time needed to respond to queries that are sent to the warehouse. To effectively solve this problem, one of the significant approaches that should be taken is to take the view of materialization. It is extremely unlikely that all of the views that can be derived from the data will ever be materialized. As a result, view subsets need to be selected intelligently in order to enable rapid data processing for queries coming from a variety of locations. The Materialized view selection problem is addressed by the model that has been proposed. The model is based on the ensemble constraint handling techniques (ECHT). In order to optimize the problem, we must take into account the constraints, which include the self-adaptive penalty, the Epsilon ()-parameter, and the stochastic ranking. For the purpose of making a quicker and more accurate selection of queries from the data warehouse, the proposed model includes the implementation of an innovative algorithm known as the constrained hybrid Ebola with COATI optimization (CHECO) algorithm. For the purpose of computing the best possible fitness, the goals of "processing cost of the query," "response cost," and "maintenance cost" are each defined. The top views are selected by the CHECO algorithm based on whether or not the defined fitness requirements are met. In the final step of the process, the proposed model is compared to the models already in use in order to validate the performance improvement in terms of a variety of performance metrics.
A Method for Malicious Network Packet Detection based on Anomalous TTL Values
Authors: Sharma Geetika , Makwana Rajni Ranjan Singh
Published: October 2023

In the current digital age, a pervasive shift towards digitalization is evident in all aspects of life, encompassing entertainment, education, business, and more. Consequently, the demand for internet access has surged, paralleled therefore unfortunate escalation in cybercrimes. This study undertakes an exploration into the intrinsic nature of network packets, aiming to discern their potential for malice or legitimacy. In the internet, 32 intermediate nodes are encountered by a Network packet before it reaches its final host. Our findings suggest that the time-to-live (TTL) parameter in certain IP packets diverges from the initial TTL by more than 32 intermediary hops. It's likely that these packets are generated by specialized software. We anticipate that malicious IP packets exhibit unconventional TTL values, influenced by factors such as the source machine's operating system and protocols like TCP/ICMP/UDP, etc. To gauge the effectiveness and value of the proposed method, an experiment was conducted utilizing the SNORT NIDS system. Filtering rules based on signatures were formulated to thoroughly analyze the traffic. Real network data, along with DARPA and MACCDC 2012 datasets, were employed as inputs for the SNORT NIDS, and it has been observed that the suggested approach successfully detects the anomalous network packets.
Exploring Unconventional Sources in Big Data: A Data Lifecycle Approach for Social and Economic Analysis with Machine Learning
Authors: Namose Mahadevi , Hiwarkar Tryambak
Published: October 2023

This study delves into the realm of leveraging unconventional sources within the domain of Big Data for conducting insightful social and economic analyses. Employing a Data Lifecycle Approach, the research focuses on harnessing the potential of linear regression, random forest, and XGBoost techniques to extract meaningful insights from unconventional data sources. The study encompasses a structured methodology involving data collection, preprocessing, feature engineering, model selection, and iterative refinement. By applying these techniques to diverse datasets, encompassing sources like social media content, sensor data, and satellite imagery, the study aims to provide a comprehensive understanding of social and economic trends. The results obtained through these methods contribute to an enhanced comprehension of the intricate relationships within societal and economic systems, further highlighting the importance of unconventional data sources in driving valuable insights for decision-makers and researchers alike.
Cross-Layer Optimization on Different Data Rates for Efficient Performance in Wireless Sensor Network
Authors: Anju Anju , Bindal Amit Kumar , Bhatia Sukhwinder Kaur , Narang Pravin , Kumar Ajay , Saini Mehak
Published: October 2023

The traditional protocols used in wireless sensor networks adhere to stringent layering approaches, which decreases the performance of the quality of service (Quality of Service) metrics. As per specifications 802.15.4, wireless sensor networks are inexpensive and energy efficient. It is essential for evaluating the performance of WSNs. Researchers have looked into the fundamental aspects of a single physical layer and the medium access control (MAC) layer protocol using methodologies calculated using several mathematical models or experimental approaches, respectively. In this research, we offer an improved cross-layer analytical model that utilises a thorough combining and interacting of a Markov chain model of the MAC layer's propagation with a model of the PHY layer's propagation. This combination and interaction are described in detail. Various Quality of Service (quality of service) statistics are presented and evaluated, and a cross-layer effectiveness degradation study is conducted under different inputs of multi-parameter vectors. Other parameters, such as Average Wait Time, Reliability, Failure Probability, and Throughput, have been estimated from the simulation results and contrasted with standardised models. The cross-layer model provides a more thorough performance study with various cross-layer parameter sets, some of which comprise distance, power transmission, and offered loads, among other things.
Hybrid Algorithm for the Detection of Lung Cancer Using CNN and Image Segmentation
Authors: Singh Drishti , Singh Jaspreet
Published: October 2023

When it comes to cancer-related fatalities, lung cancer is by far the most common cause. Early detection is the key to a successful diagnosis and treatment plan for lung cancer, just as it is for other types of cancer. Automatic CAD systems for lung cancer screening using computed tomography scans primarily involve two steps: the first step is to detect all potentially malignant pulmonary nodules, and the second step is to determine whether or not the nodules are malignant. There have been a lot of books published recently about the first phase, but not many about the second stage. Screening for lung cancer requires a careful investigation on each suspicious nodule and the integration of information from all nodules. This is because the presence of pulmonary nodules does not always indicate cancer, and the morphology of nodules, including their shape, size, and contextual information, has a complex relationship with cancer. In order to overcome this problem, we suggest a deep CNN architecture that is different from the architectures that are commonly utilised in computer vision. After the suspicious nodules have been formed with the modified version of U-Net, they are used as an input data for our model. First, the suspicious nodules are generated with U-Net. To automatically diagnose lung cancer, the suggested model is a multi-path CNN that concurrently makes use of local characteristics as well as more general contextual characteristics from a wider geographical area. In order to accomplish this, the model consisted of three separate pathways, each of which used a different receptive field size, which contributed to the modelling of distant dependencies (short and long-range dependencies of the neighbouring pixels). After that, we concatenate characteristics from the three different pathways in order to further improve our model's performance. In conclusion, one of the contributions that we have made is the development of a retraining phase system. This system enables us to address issues that are caused by an uneven distribution of picture labels. The experimental findings from the KDSB 2017 challenge demonstrate that our model is more adaptable to the described inconsistency among the nodules' sizes and shapes. Furthermore, our model obtained better results in comparison to other researches.
Machine Learning-Enhanced Advancements in Quantum Cryptography: A Comprehensive Review and Future Prospects
Authors: Chandre Pankaj R , Shendkar Bhagyashree D , Deshmukh Sayalee , Kakade Sameer , Potdukhe Suvarna
Published: October 2023

Quantum cryptography has emerged as a promising paradigm for secure communication, leveraging the fundamental principles of quantum mechanics to guarantee information confidentiality and integrity. In recent years, the field of quantum cryptography has witnessed remarkable advancements, and the integration of machine learning techniques has further accelerated its progress. This research paper presents a comprehensive review of the latest developments in quantum cryptography, with a specific focus on the utilization of machine learning algorithms to enhance its capabilities. The paper begins by providing an overview of the principles underlying quantum cryptography, such as quantum key distribution (QKD) and quantum secure direct communication (QSDC). Subsequently, it highlights the limitations of traditional quantum cryptographic schemes and introduces how machine learning approaches address these challenges, leading to improved performance and security. To illustrate the synergy between quantum cryptography and machine learning, several case studies are presented, showcasing successful applications of machine learning in optimizing key aspects of quantum cryptographic protocols. These applicatiocns encompass various tasks, including error correction, key rate optimization, protocol efficiency enhancement, and adaptive protocol selection. Furthermore, the paper delves into the potential risks and vulnerabilities introduced by integrating machine learning with quantum cryptography. The discussion revolves around adversarial attacks, model vulnerabilities, and potential countermeasures to bolster the robustness of machine learning-based quantum cryptographic systems. The future prospects of this combined field are also examined, highlighting potential avenues for further research and development. These include exploring novel machine learning architectures tailored for quantum cryptographic applications, investigating the interplay between quantum computing and machine learning in cryptographic protocols, and devising hybrid approaches that synergistically harness the strengths of both fields. In conclusion, this research paper emphasizes the significance of machine learning-enhanced advancements in quantum cryptography as a transformative force in securing future communication systems. The paper serves as a valuable resource for researchers, practitioners, and policymakers interested in understanding the state-of-the-art in this multidisciplinary domain and charting the course for its future advancements.
An Analysis of Those Factors Influencing Customer’s Online Buying Behaviours
Authors: Kant Tushar , Chaturvedi Umesh Kumar
Published: October 2023

Today, the internet has changed the lifestyle of every person in the world who works in any sector. So, this advancement of technology has also impacted consumers buying behaviour as they move from traditional stores to online stores. Consumers can buy any item, durable or non-durable, at any time or any place without spending extra money or time. In this paper, we identify the factors that motivate customers to shift from traditional to online stores. These factors may vary from person to person, and their buying behaviour is not the same. So, the main motive of this study is to identify those factors that influence consumers' online shopping behaviour using exploratory factor analysis. A survey was conducted to accomplish our objective, in which 341 respondents participated, and the data were analysed using IBM SPSS.
Investigating Flavonoid Extracts from Medicinal Plants: Evaluating their Anti-Cancer Potential, Mechanisms, and Synergistic Impact on Colon Cancer
Authors: Mishra Saurabh , Dubey Sharda , Chitranshi Rishabh , Kumar Arvind , Sharma Suraj , Saini Monika , Pandey Abhishek , Kumar Ranjit , Ravindra Nath Ravindra Nath , Katiyar Shashwat , Varma Garima
Published: October 2023

Colon cancer, the leading cause of global cancer-related mortality, demands innovative therapeutic approaches to combat its formidable impact. This empirical study embarks on a quest to unlock novel avenues for colon cancer treatment by investigating the anti-cancer potential of flavonoid extracts sourced from medicinal plants. Our research journey commences with an in-depth examination of the staggering global burden imposed by colon cancer and the inherent limitations of current therapeutic regimens. In response to this pressing challenge, we spotlight the emerging enthusiasm for natural compounds, specifically flavonoids, as transformative agents within the realm of cancer research and therapy. In our pursuit of innovative solutions, we meticulously select medicinal plants celebrated for their flavonoid-rich content and extract these bioactive compounds with precision. Rigorous phytochemical analyses unveil the specific flavonoids at play. In a series of in vitro experiments employing colon cancer cell lines, we uncover a compelling narrative of concentration-dependent cytotoxicity, underscoring the remarkable anti-proliferative attributes of these extracts. Moreover, our investigations reveal that flavonoid extracts possess the remarkable capability to induce apoptosis, substantiated through Annexin V/PI staining and caspase activation assays. As we delve deeper into mechanistic insights, a rich tapestry unfolds, elucidating the intricate modulation of pivotal apoptosis-related pathways by these natural compounds. This study not only furnishes compelling evidence of flavonoid extracts' anti-cancer potential against colon cancer but also underscores the pivotal role of natural compounds in the ever-evolving landscape of cancer research, offering a beacon of hope for pioneering therapeutic strategies. The journey has only begun, and further investigations, alongside rigorous clinical trials, are warranted to harness the full therapeutic potential of flavonoid-based interventions in colon cancer management, potentially redefining the paradigm of cancer treatment.
Phytochemicals & Pharmacological activity of Xanthium strumarium: An Updated Review
Authors: Trivedi Swati , Gupta Vishal , Tiwari Gaurav , Manigaunha Ashish
Published: October 2023

Therapeutic plants are of extraordinary worth in the field of treatment and fix of infections. Throughout the long term, logical examination has extended our insight into the compound constituents of the therapeutic plants, which decide the restorative properties. The purpose of this paper is to summarize the progress of modern research, and provide a systematic review on the traditional usages, phytochemistry, pharmacology of the X. strumarium. Plant parts like leaves, organic product, bark and seed have been accounted for having antidiabetic, antiulcer, calming, invulnerable modulator and pain- relieving movement. This survey tends to the tentatively validated realities and furthermore proposes the requirement for research on substance and pharmacological properties of Xanthium strumarium.
Eco-Friendly Stability Indicating HPLC Method for Estimation of Elbasvir and Grazoprevir by Quality by Design Approach
Authors: Bhangale Charushila , Somwanshi Sachin , Kotade Kiran , Gaikar Mayur
Published: October 2023

Introduction: The pharmaceutical industry faces a significant problem as a result of the worldwide requirement to modify processes in order to comply with the green analytical chemistry (GAC) requirements. An enormous amount of organic hazardous waste is produced by high-performance liquid chromatography (HPLC), one of the methods employed the most frequently at different stages in the pharmaceutical sector. Aim: To develop analytical quality by design-aided stability indicating green high-performance liquid chromatography (HPLC) method for estimation of Elbasvir and Grazoprevir in a dosage form. Material and Methods: The critical chromatographic factors were the % of ethanol in the mobile phase, flow rate, and their overall effect on the responses like asymmetry, theoretical plates, and resolution were studied to optimize the method. Green analytical chemistry (GAC) has mainly focused on developing analytical methods that are safe for the environment. Therefore, it is imperative that the GAC principles be applied in pharmacological analysis. A rotatable central composite design was employed, and the optimized conditions for chromatographic separation were made with a run time of 5 minutes using Zorbax C18 column (4.6× 150 mm, 5 µm) with 0.1% Trifluoroacetic acid and ethanol (40:60 v/v) as components of a mobile phase, flowing at a rate of 1.0 ml/minute. Photodiode array detection was carried out at 253 nm. Results: The retention time was 1.8 min for EBS and 2.84 min for GZP. According to the ICH guidelines, the proposed method was validated and stress studies revealed that Elbasvir and Grazoprevir are prone to acidic, basic, and oxidation stress conditions. An analytical eco-scale score evaluated the greenness profile and a software-based evaluation. Conclusion: The developed HPLC method is eco-friendly and shall be adopted   in the routine quality control of Elbasvir and Grazoprevir in a tablet dosage form.
A Study of Crop Yield Prediction Using Machine Learning Approaches
Authors: Satish Kumar Kalhotra Satish Kumar Satish Kumar Kalhotra , Prakash K.C. , Mishra Manoj Kumar , Kumar M S Annapurna Kishore
Published: October 2023

Agriculture plays a pivotal role in our society by providing food, fiber, and raw materials for various industries. The world's population is steadily growing, and there is increasing pressure on agriculture to meet the rising global food demand. In this context, the use of machine learning approaches to predict crop yields has gained significant importance. This paper aim is to study the significance of crop yield prediction through machine learning, its methods, applications, and its potential to revolutionize the agricultural sector.
Psychological and Linguistic Aspects of Teaching English to Paramedicine Students of Medical Institutes
Authors: kizi Yakhshiboyeva Gulbakhor Oybek , Isroilova Dildora Muxtorovna  , Alla Olegovna Chijova  , Odiljonovna Toshboyeva Barnokhon
Published: October 2023

The practical purpose of teaching a foreign language in this field is to master the types of speech activities and to form the skills and abilities of oral (speaking and listening comprehension) and written (reading and writing) communication in a foreign language. The purpose of receiving or giving information creates the need for communication. Thus, in reading, a content plan (the meaning of the text) and a procedural plan (how to read the text and make it sound) are distinguished.
Fieldfare (Turdus Pilaris L.) Nesting Ecology in the City of Stepanakert of the Republic of Artsakh and its Surrounding Territories
Authors: Aydinyan Lusine Garnik , Hayrapetyan Vahram Torik
Published: October 2023

The present study delves into the nesting ecology of fieldfares within the avian population of Stepanakert, located in the Republic of Artsakh, alongside its contiguous regions. The research material encompasses observations and investigations carried out between 2017 and 2022 within the aforementioned geographic domains. In our specific conditions, the initiation of the fieldfare nesting process transpires under outdoor air temperatures exceeding +8°C to +10°C, while the first appearance of an egg within the nest during spring corresponds to temperatures of +15°C to +16°C. The nesting substrates encompass an array of broad-leaved and coniferous trees, in addition to various man-made structures. Nest placement exhibits diversity, situated at elevations of 0.5-1.2 meters above the ground within open areas proximate to the city, escalating to 3-4.5 meters within the central zones of Stepanakert, and subsiding to 1.5-2.5 meters in woodland environs adjacent to the city. The construction of nests unfolds over a median period of 5-7 days. On average, nests accommodate a clutch of 3-5 eggs. The morphological attributes of eggs reveal considerable variability. In our contextual setting, fieldfares engender two generations annually, with an incubation duration spanning 12-13 days. Hatchlings emerge from eggs within a single day, occasionally within two days, eventually taking their inaugural flights after 15-17 days. The percentages of losses occurring between the embryonic stage and the attainment of flying capability among chicks display distinct variations.
A mixture of carbon dioxide and dimethyl ether as a refrigerant for ground air conditioning system
Authors: Zharov Anton A. , Garanov Sergey A. , Talyzin Maksim S. , Kovalchuk Nikita A.
Published: October 2023

BACKGROUND: The selection of refrigerants for modern air conditioning systems (ACS) in ground facilities is a multidisciplinary task. Particularly, meeting the required energy efficiency of the refrigeration cycle as well as ensuring ecological safety of production, operation, and utilization of the refrigeration system. Herein, the working pressure levels of the refrigeration cycle considerably affect the availability, cost, and safety of the refrigeration equipment. The fire safety of the working substance is also important.
AIM: To investigate the feasibility of a mixture of dimethyl ether and carbon dioxide as refrigerant for energy efficient and safe application of ACS in ground facilities.
METHODS: Comparative analysis of a simple one-stage vapor–compression cycle using traditional working substances (R22 and R410A) and the proposed working substance, which is in the form of a mixture of dimethyl ether and carbon dioxide, using packages, such as Mathcad, HYSYS, CoolPack, and REFPROP, was performed.
Results: An ecofriendly mixture of dimethyl ether and carbon dioxide with low global warming potential and zero ozone depletion potential was proposed as refrigerant. Increasing the percentage of dimethyl ether in the blend reduces the temperature glide in the gas cooler, a property of CO2, and pressures at which the blend operates. The mixture has limited operational properties due to the flammability of dimethyl ether, but its environmental performance makes the material of some practical interest.
CONCLUSION: Fire safety of the proposed working substance was calculated. The concentration of dimethyl ether in the mixture at which it becomes flammable and unsafe for ACS was determined to be 8.3%.
With an increase in the dimethyl ether content in the mixture with CO2 from 4% to 8%, the refrigeration coefficient of the cycle increases from 2.53 to 2.88, but it is 1.57 times less than that of R410A.
The difference in operating pressures between the used non-ecological refrigerants and proposed mixture was determined. Results indicate that the mixture of dimethyl ether and carbon dioxide is currently inapplicable to mass production compressors, which use R410A as refrigerant. The condensation pressure of the most effective and nonflammable mixture of dimethyl ether and CO2 (with dimethyl ether concentration of 8%) is 101 bar against 30 bar for R410A.
Therefore, we intend to evaluate test mixtures of dimethyl ether with other substances in the future.
Artırılmış Gerçeklik Uygulamalarının Eğitimde Kullanımının Başarı Üzerindeki Etkisinin İncelenmesi: 2010-2020 Yılları arasında Yapılan Çalışmaların Meta-Analizi
Authors: ÜRÜN ARICI Nesrin , YILDIZ Emre
Published: October 2023

Bu araştırmanın amacı, eğitimde kullanılan artırılmış gerçeklik uygulamalarının öğrencilerin başarıları üzerindeki etkililiğine yönelik yapılan birbirinden farklı çalışmaların sonuçlarının bir araya getirilmesidir. Verilerin toplanması için çalışmaya dâhil edilme ölçütleri belirlenerek 28 çalışma araştırmaya dâhil edilmiştir. Bu araştırmada meta-analize alınan her çalışmaya ait etki büyüklükleri ve varyansları, grupların birleştirilmiş etki büyüklükleri ve grupların karşılaştırılmaları comprehensive meta-analysis istatistik programı ile yapılmıştır. Bulgulara göre, artırılmış gerçekliğin, akademik başarıyı artırmada geleneksel uygulamalara göre daha etkili olduğu söylenebilir. Etki büyüklüğünün en büyük olduğu ülkenin Türkiye, alanın ise fen eğitimi ve örneklem düzeyinin okul öncesi olduğu tespit edilmiştir.
Examination of Primary School Teachers' Opinions on Cultural Heritage Education in Life Science Lesson
Authors: BATMAZ Onur
Published: October 2023

The case study was used in this study, which was conducted to examine the opinions of primary school teachers on cultural heritage education in the Life Science lesson. The participants the study consisted of 15 primary school teachers working primary schools in Yozgat. Convenience sampling method was used while determining the participants. A semi-structured interview form improved by the researcher and expert opinions were used to collect the data of the study. Descriptive analysis technique was used analysis of the obtained data. At the end of the data, it was concluded that teachers' opinions on cultural heritage are mostly in the form of cultural heritage elements. The majority of the teachers stated that they did not receive any education on cultural heritage or did not participate in the activity. In addition, a significant part of the teachers expressed that they found the cultural heritage education the Life Science curriculum insufficient.
The Effect of Mind and Intelligence Games on University Students’ Perceived Stress and Psychological Well-Being Level
Authors: AKÇA Coşkun , ÖZEL Yasemin
Published: October 2023

This study aims to examine whether mind and intelligence games that support cognitive, social, and emotional skills have an effect on university students' perceived stress and psychological well-being levels. The population of the study consists of students actively studying at Kastamonu University Tosya Vocational School in the 2021-2022 academic year. Two groups of students were included in the study. While one of the groups had mind and intelligence game activities, the other group did not have these activities. The obtained data were analyzed using the statistical package program. As a result of the analysis, it was concluded that the psychological well-being levels of those who participated in the mind and intelligence game workshops were significantly higher in the positive direction and the perceived stress level was significantly lower in the negative direction compared to those who did not participate. According to the results of the research, it can be said that mind and intelligence games are an important educational tool used to support the psychosocial development of students and individuals and reduce stress.
İlköğretim Matematik Öğretmen Adaylarının ve Matematik Öğretmenlerinin Yanlışlar için Kullandıkları Dönütlerin İncelenmesi
Authors: TÜRKDOĞAN Ali , YILDIZ Ahmet , KORKUSUZ Emine , DEMİRKIRAN Ayşe Pelin , IŞIK Betül , ÖZCAN Canan , GÜÇARSLAN Feyza
Published: October 2023

Eğitimde nitelikli bireyler yetiştirmek için öğretmenlerin sahip olması gereken yeterliklerden biri de dönüt verebilmedir Davranışçı teorilerde yanlış göz ardı edilirken, bilişsel teorilerde ise sadece kavram yanılgıları önemsenmektedir. Ancak yapılandırmacı teoride: yanlışa öğrenme için bir fırsat ve öğrenme ortamının kaçınılmaz bir parçası olarak bakılmaktadır. Bu çalışma kapsamında öğretmen adaylarına ve öğretmenlere (167 kişi) 12 açık uçlu sorudan oluşan bir anket uygulanmıştır. Böylece hem yanlış türleri ile dönüt türleri arasındaki ilişki hem de dönüt verme becerisinin öğretmen adaylarının sınıflarına ve öğretmenlere göre nasıl değiştiği irdelenmiştir. Çalışma betimleyici bir çalışmadır. Mevcut durumun ortaya çıkarılması amaçlanmaktadır. Çalışma sonucunda en fazla dönütün 4. sınıftaki öğretmen adaylarından, en az dönütün ise 1. sınıftaki öğretmen adaylarından geldiği görülmüştür. Öğretmenlerin dönütleri 4. Sınıftaki öğretmen adaylarının dönütlerine göre daha azdır. Bu anlamda öğretmenlere dönüt konusunda hizmet içi eğitim verilebilir.
Üstbilişsel Okuma Konusunda Yapılmış Araştırmaların İncelenmesi
Authors: ÇİFTÇİ Yunus Emre , BULUT İlhami
Published: October 2023

Bu araştırmanın amacı 2022 yılına kadar Türkiye’de üstbilişsel okuma konusunda yayınlanmış olan tezlerin ve makalelerin incelenmesidir. Bu amaç doğrultusunda araştırmanın türü, yılı, tezin hazırlandığı üniversite, bilim dalı, araştırmanın gerçekleştirildiği düzey, araştırmanın alanı, yöntem, çalışma grubu, örneklem tekniği, örneklem büyüklüğü, veri toplama araçları, veri analiz yöntemleri ve araştırmaların amaçları başlıkları altında incelenmiştir. Araştırma doküman analizi araştırma yöntemi ile yapılmıştır. Araştırmalar en çok üniversite düzeyinde yapılmış ve öğretmen adayları ile yürütülmüştür. Araştırmalarda en çok nicel araştırma yöntemi kullanılmıştır. Araştırmalarda veri toplama aracı olarak en çok ölçek kullanılmıştır. Araştırmaların amaçları incelendiğinde öğrencilerin üstbilişsel okuma stratejileri düzeyini belirleme ve üstbilişsel okuma stratejilerini çeşitli değişkenler açısından inceleme en çok belirlenen amaçlar olmuştur.