Reviewerly/exHarmony huggingface dataset:
FOHC: Firefly Optimizer Enabled Hybrid approach for Cancer Classification

Early detection and prediction of cancer, a group of chronic diseases responsible for a large number of deaths each year and a serious public health hazard, can lead to more effective treatment at an earlier stage in the disease's progression. In the current era, machine learning (ML) has widely been used to develop predictive models for incurable diseases such as cancer, heart disease, and diabetes, among others, taking into account both existing datasets and personally collected datasets, more research is still being conducted in this area. Using recursive feature elimination (RFE), principal component analysis (PCA), the Firefly Algorithm (FA), and a support vector machine (SVM) classifier, this study proposed a Firefly Optimizer-enabled Hybrid approach for Cancer classification (FOHC). This study considers feature selection and dimensionality reduction techniques RFE and PCA, and FA is used as the optimization algorithm. In the last stage, the SVM is applied to the pre-processed dataset as the classifier. To evaluate the proposed model, empirical analysis has been carried out on three different kinds of cancer disease datasets including Brain, Breast, and Lung cancer obtained from the UCI-ML warehouse. Based on the various performance parameters like accuracy, error rate, precision, recall, f-measure, etc., some experiments are carried out on the Jupyter platform using Python codes. This proposed model, FOHC, surpasses previous methods and other considered state-of-the-art works, with 98.94% accuracy for Breast cancer, 95.58% accuracy for Lung cancer, and 96.34% accuracy for Brain cancer. The outcomes of these experiments represent the effectiveness of the proposed work.
A Behaviour Study on Cloud Eco-System: Data Security Perspective

Cloud eco-system is a revolution now, which modifies the way in which the IT-based services are being delivered to end customers. It is increasing or we can also say grown-up technology that delivers multiple benefits whether in terms of economics or in terms of cost-effective resource utilization. The ability to install and improve their services on that platform is made possible by the advent of cloud computing, which opens up new options for long-term solutions. Cloud computing's environmental and economic impacts must be considered while assessing its long-term viability. A growing number of organizations, businesses, and personal users are depending on services supplied by the cloud and keeping crucial information in the cloud because of its easy-to-use characteristics. The cloud, despite its widespread use, nevertheless has a number of drawbacks when it comes to data security. Customers are concerned about how their personal information is transported to and from the cloud. Research articles in this topic have been thoroughly analyzed and examined in this report.
Enhancing Handwriting in the Peruvian Educational Context: Design of an Interactive Web Platform

The main objective of this research work is to design a web platform so that through its use they can learn to write, it is aimed at a general public regardless of their age. A Design Thinking methodology was used since it was found to be adequate to explain each stage of the research in their respective phases of the methodology. On the other hand, a survey was conducted with 15 people from an Alternative Basic Educational Center (CEBA) to find out if they were willing to use a web platform to learn to write.&#x0D; Finally, it was concluded that it is necessary to take advantage of technological advances for problems that may occur in the field of education and have solutions through its use.
A novel cost function for nuclei segmentation and classification in imbalanced histopathology data-sets

Cancer is a major global health problem, causing millions of deaths yearly. Histopathological analysis plays a crucial role in detecting and diagnosing various types of cancer, enabling an accurate diagnosis to inform targeted treatment planning, allowing for better cancer staging, and ultimately improving prognosis. We aim to detect cancer earlier, which can ultimately help reduce mortality rates and enhance patients’ quality of life. However, detecting and classifying rare cells is a key challenge for pathologists and researchers. Many histopathological data-sets contain imbalanced data, with only a few instances of rare cells whose unique morphological structures can impede early diagnosis efforts. Our model, SPNet, a spatially aware convolutional neural network, addresses this problem by employing a spatial data balancing technique, enhancing the classification of rare nuclei by 21.8 %. Since nuclei often cluster and exhibit patterns of the same class, SPNet’s novel cost function targets spatial regions, resulting in a 1.9 % increase in the F1 classification of rare class types within the CoNSeP dataset. When integrated with a ResNet50-SE encoder, SPNet increases the mean F1 score for classifying all nuclei in the CoNSeP dataset by 4.3 %, compared to the benchmark set by the state-of-the-art HoVer-Net model. The potential integration of SPNet into existing medical devices could allow us to streamline diagnostic processes and minimise false negatives.
Topic: Esophagus Benign-GERD, Achalasia, MotilityAbstract ID: 79Reconstructing the Omega Valve With Posterior Hill Stitch and Partial Anterior Fundoplication

Restricted accessAbstractFirst published online September 22, 2023Topic: Esophagus Benign-GERD, Achalasia, MotilityAbstract ID: 79Reconstructing the Omega Valve With Posterior Hill Stitch and Partial Anterior FundoplicationGabriel Reep, Alexis McQuitty, […], Eloise Catrett, and Gal Levy+1-1View all authors and affiliationsOnlineFirsthttps://doi.org/10.1177/26345161231196000
Toxic Comment Classification using Deep Learning

Online Conversation media serves as a means for individuals to engage, cooperate, and exchange ideas; however, it is also considered a platform that facilitates the spread of hateful and offensive comments, which could significantly impact one's emotional and mental health. The rapid growth of online communication makes it impractical to manually identify and filter out hateful tweets. Consequently, there is a pressing need for a method or strategy to eliminate toxic and abusive comments and ensure the safety and cleanliness of social media platforms. Utilizing LSTM, Character-level CNN, Word-level CNN, and Hybrid model (LSTM + CNN) in this toxicity analysis is to classify comments and identify the different types of toxic classes by means of a comparative analysis of various models. The neural network models utilized for this analysis take in comments extracted from online platforms, including both toxic and non-toxic comments. The results of this study can contribute towards the development of a web interface that enables the identification of toxic and hateful comments within a given sentence or phrase, and categorizes them into their respective toxicity classes.
Experimental Investigation for Detecting Mitotic Cells in Medical Image using an Automated Algorithm

Cancer of the breast is a malignant tumour that originates in the cells of the breast tissue. It is by far the most common kind of cancer found in females around the world, with a projected 2.3 million new cases will be discovered in the year 2020 alone. It is projected that one in eight women will be diagnosed with breast cancer at some point in their life, despite the fact that breast cancer can also occur in men. Breast cancer is a complex condition that can arise from a diverse set of factors, express itself in a variety of ways, and can be treated in a variety of ways. Ductal carcinoma in situ, invasive ductal carcinoma, and invasive lobular carcinoma are all different subtypes. Both the available treatment options and the expected outcome of breast cancer are very variable depending on the particular subtype of the illness. Breast cancer risk factors include drinking alcohol and not getting enough exercise, as well as getting older, having a family history of the disease, having genetic mutations, being exposed to estrogens, and having a family history of the disease. There is not always a connection between having risk factors and developing breast cancer, despite the fact that there can be a link between the two. The prognosis and treatment options for breast cancer are highly dependent on the stage of the disease at the time of diagnosis. During staging, the extent to which the cancer has spread throughout the body and how far it has progressed are both measured. The TNM system, the IAFCM system, the ACM system, and the MPIG system are just few of the staging systems that are used to classify breast cancer. These staging systems consider not only the size of the tumor but also whether or not lymph nodes are involved and whether or not distant metastases are present. The severity of breast cancer symptoms can vary widely, depending not only on the subtype of the disease but also on how far along it has progressed. Alterations in the size or shape of the breast, discharge from the nipple, and alterations in the skin of the breast (such as redness or dimpling) are all common indications. On the other hand, not all cases of breast cancer present themselves in a visible manner, and mammography and other forms of routine screening may be able to detect some of these cases. Options for treating breast cancer vary depending on the patient's condition and the stage of the disease, as well as the patient's overall health and their preferences towards therapy. Common examples of medical interventions include surgery, radiotherapy, chemotherapy, hormone therapy, and targeted therapy. Other examples include. In certain cases, it may be appropriate to participate in more than one form of treatment.
SENTIMENT ANALYSIS OF MYPERTAMINA APPLICATION USING SUPPORT VECTOR MACHINE AND NAÏVE BAYES ALGORITHMS

In line with the needs of the community and the progress of the times in the advanced field of fintech, cash payments are currently considered insecure as well as ineffective and efficient. To run a non-cash or cashless transaction program currently run by the government, PT. Pertamina invites the public to use E-Payment from the My Pertamina application in collaboration with LinkAja. In this study, the sentiments of MyPertamina application users will be analyzed based on reviews on the Google Play Store. Review data will be analyzed to determine whether the review has positive, negative, or neutral sentiments. The data analysis stage is text preprocessing to change uppercase to lowercase, clearing text, separating text, taking important words, changing essential words, and labeling data into positive, negative, and neutral classes. As well as the classification and evaluation of results. This study used the Support Vector Machine (SVM) and Naïve Bayes classification methods. To evaluate the results, the confusion matrix was used to test the accuracy, precision, recall, and F1 score value. The classification results obtained the highest accuracy value for the Support Vector Machine (SVM) method, which had accuracy (68.50%), precision (70.00%), recall (69.70%), and F1 score (68.46%). Meanwhile, the Naïve Bayes method has performance with accuracy (63.00%), precision (63.90%), recall (61.34%), and F1 score (59.55%).
Boosting English Proficiency in Peruvian Children from 6 to 12 Years Old: Development of an Innovative Hybrid Application

The present research work tries to design a hybrid application to learn English in children from 6 to 12 years old where it has 5 modules that are . Home, materials, evaluations, cards and notes (score table). A Design Thinking methodology was implemented since it was considered to be the most appropriate for the development of the research since in each phase it would be detailed what the research would consist of and the necessary requirements to be able to design it. Likewise, a survey was conducted with 30 people to know their opinion on the use of technologies in learning, resulting in a positive way about the implementation of technologies when teaching and learning.&#x0D; It was concluded that the implementation of a hybrid application would facilitate student learning.
Newton’s Law of Gravitational Force (NLGF) based Machine Learning Technique for Uneven Illuminated Face Detection

A photo gallery is crucial for organizing your photos, presenting them in beautiful categories, and doing sophisticated memory searches. The photo gallery is portrayed in a vocabulary of nonlinear similarities to the prototype face image collection. One of the difficult research ideas for machine learning technologies is the maintenance of a photo gallery using facial recognition. Based on changes in the faces' appearance, faces are identified. This research proposes novel machine learning algorithms to recognize faces by characterizing the majority of discriminating local characteristics, which maximizes the dissimilarity between face photos of different persons and reduces the dissimilarity between features between face images of the same person. This method relies on Newton's third law of gravitational force to determine the relationship between pixels to extract the features of noisy accurately and efficiently, unevenly illuminated, and rotationally invariant face images.
Performance Analysis of Reputation based Proof of Credibility Consensus Mechanism for Blockchain based Applications

Blockchain is a decentralized transaction and data management technology first developed for the Bitcoin cryptocurrency. Blockchain technology is gaining popularity due to its core attributes which provides security, anonymity and data integrity without any involvement of third party. Consensus mechanism is a procedure by which all peers in the blockchain network agrees to a common agreement on the current state of the distributed ledger. It plays vital role in increasing efficiency of any blockchain environment. Though we have many consensus mechanisms working currently in different areas but they still lack in parameters like status of validators, latency, node failure etc. In Our proposed algorithm Proof of credibility, we have tried to incorporate all above factors in it. We have also implemented two or more factors of proposed algorithm and have evaluated and compared with existing consensus algorithm. In future research we aim to implement RPoC in any blockchain network and then we will evaluate it in terms of different evaluation parameters such as performance, security, scalability.
The Dynamic Selection of Combination Methods in Classifier Ensembles by Region of Competence

Over the years, research has advanced in the field of classifier ensembles. Several ways to improve its efficiency have emerged, for homogeneous and heterogeneous ensembles. One challenge when using classifier ensembles is the definition of its structure. Basically, the ensemble structure selection can be done in two different ways, static and dynamic selection. Different static selection, dynamic selection defines the ensemble structure is selected for each testing instance (dynamic selection). Dynamic selection methods have been proposed in the literature, mainly for ensemble members and features, but very little effort has been done to propose dynamic selection methods for combination methods. In this paper, a dynamic combination selection is proposed in which the combination method is selected to each testing instance. The main aim of the proposed dynamic combination selection is to adapt the ensemble structure to the characteristics of each testing instance. In order to assess the feasibility of the proposed method, an empirical analysis is conducted. In this analysis, the proposed method is used along with a dynamic ensemble member selection (KNORA-Eliminate and META-DES) in order to promote more dynamicity in the ensemble structure. In this analysis, the proposed methods are compared to classifier ensemble with static combination methods and it improved the performance of all analyzed methods, for almost all analyzed scenarios.
Google Workspace Tools and Their Relationship with Self-Regulatory Competence

AbstractThe objective of this work was to relate the use of Google Workspace tools with self-regulation competence in virtual environments during COVID-19. To begin with, it was explained that since the pandemic, the courses changed from face-to-face to virtual, so it was necessary to train teachers and students in the use of technologies especially in Google Workspace tools to strengthen distance education university processes. Also, it was considered that the failure of students in virtual mode may be due to the lack of development of self-regulation competence. The methodology used was a nonprobabilistic, descriptive, and correlational investigation. An analysis was carried out using a sample of eighty-five students from three undergraduate degrees at a public university in Sonora, Mexico. The data were collected from February to March 2022 using two questionnaires: use of Google Workspace and CMEA (Motivation and Learning Strategies Questionnaire [Spanish acronym]). Data were analyzed using descriptive statistics and correlation analysis. The results highlight that the students always use the tools Gmail, Drive, Forms, Meet, and rarely use Calendar. Also, they evaluated both the motivation scale and the learning strategies scale with high values. Correlations show relationships between Google Workspace with self-regulatory competencies. It is concluded that the use of Google Workspace tools favors self-regulation.
Immunohistochemical Survey of Invasive Ductal Carcinoma of the Breast, using ER, PR, HER2 and KI-67 biomarkers, in Uyo, Nigeria

Background: Breast’s Invasive Ductal Carcinoma (IDC), which is the commonest type of malignancy in females worldwide, can be characterized using immunohistochemistry in view of personalized cancer therapy. In this study, we aimed to determine the pattern of immunohistochemical profiles of IDC using oestrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 receptor (HER2) and proliferative index (Ki-67) biomarkers in our tertiary healthcare facility in Uyo, Akwa Ibom State, Nigeria given the dearth of its data in our environment.&#x0D; Materials and methods: We carried out a retrospective hospital-based immunohistochemical study of archival IDC tissue blocks over a four- and half-year period. Using systematic random sampling method, 64 formalin fixed paraffin embedded (FFPE) IDC tissue blocks were selected for this study. We carried out immunohistochemical evaluation using ER, PR, HER2 and Ki-67 biomarkers. Subsequently, we presented the results and classification schemes as text, tables, graphs, and photomicrographs.&#x0D; Results: We found that the proportion of expressions were ER-negative (88.7%), PR-negative (87.3%), HER2-negative (68.3%) and Ki-67 (&lt;20%) being 83.6% respectively. The immunohistochemical-based classification which was done using combined immunohistochemical profiles of ER/PR/HER2 and ER/PR/HER2/Ki-67 biomarkers respectively, revealed five immunohistochemical-based subtypes. These subtypes were ER-positive luminal A (ER+/±PR+/HER2-) [5.56%], ER-positive luminal B (ER+/±PR+/HER2+) [5.56%], HER2-overexpression (ER-/±PR+/HER2+) [16.66%], Triple negative (ER-/PR-/HER2-) [66.67%] and Unclassified subtypes (ER-/PR+/HER2-) [5.56%]. Furthermore, these five subtypes were further subcategorized into low (Ki-67 &lt;20%) and high (Ki-67 ≥20%) proliferation subtypes accordingly.&#x0D; Conclusion: The commonest pattern of immunohistochemical profile expression of IDC in Uyo was found to be the Triple negative subtype.
Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm

Machine learning methods can generally be categorized as supervised, unsupervised and reinforcement learning. One of these methods, Q learning algorithm in reinforcement learning, is an algorithm that can interact with the environment and learn from the environment and produce actions accordingly. In this study, eight different on-line methods have been proposed to determine online the value of the learning parameter in the Q learning algorithm depending on different situations. In order to test the performance of the proposed methods, these algorithms are applied to Frozen Lake and Car Pole systems and the results are compared graphically and statistically. When the obtained results are examined, Method 1 has produced better performance for Frozen Lake, which is a discrete system, while Method 7 has produced better results for the Cart Pole System, which is a continuous system.
THE EFFECTS OF MIND MAPPING AT WHILE-READING STAGE ON EFL STUDENTS’ READING COMPREHENSION AT A LOCAL HIGH SCHOOL IN KIEN GIANG PROVINCE, VIETNAM

There is an increased attention to research into the use of mind mapping as a useful tool for enhancing students’ reading comprehension. However, few studies have been conducted to examine the effects of mind mapping at the reading stage on students’ reading comprehension within the Vietnamese context. This paper therefore investigates the effects of mind mapping on 11th graders’ reading comprehension and their attitudes towards this strategy. Participants in this study include 60 students at a local school in Kien Giang province. The data discussed in this paper include tests and interviews. The findings reveal that using mind mapping in the teaching process of reading texts had a moderate impact. Pedagogical implications for teachers and school administrators are presented.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0019/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
TEACHERS’ BELIEFS AND PRACTICES OF SCAFFOLDING STUDENTS’ PARAGRAPH WRITING THROUGH MIND-MAPPING AT PRE-WRITING STAGE

Scaffolding is considered as a kind of support assisting students in completing language learning tasks. Research into mind-mapping, a type of graphic organizer, has indicated its role as a scaffolding strategy in teaching English. This paper reports a descriptive study examining teachers’ beliefs and practices of using mind-mapping to scaffold students’ paragraph writing at pre-writing stage. The data discussed in this paper include questionnaires and observations with eighty-four teachers of English as a foreign language. The findings of the research reveal teachers’ positive beliefs about mind-mapping and their practices of mind-mapping to scaffold students’ paragraph writing. Pedagogical implications for teachers and students are presented.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0087/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
The Role of Contrastive Analysis/Error Analysis in Solving Learners Errors Problems in French as a Foreign Language Class in Nigeria

In French as a Foreign Language class in Nigeria where the Nigerian learners of the language study the language against the background of their mother tongue/ English Language which they have attained to some level a reasonable degree of competence, the making of errors is inevitable since the learners concerned are faced with a lot of linguistic problems such as; interference of their Native tongue/English on French language and other linguistic problems emanating from French language. The study is set to take a look into the role of Contrastive Analysis/ Error Analysis in solving French learners’ error problems.