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Sentiment analysis, a vital aspect of natural language processing, involves the application of machine learning models to discern the emotional tone conveyed in textual data. The use case for this type of problem is where businesses can make informed decisions based on customer feedback, identify the sentiments of their employees, and make decisions on hiring or retention, or for that matter, classify a text based on its topic like whether it is about a particular subject like physics or chemistry as is useful in search engines. The model leverages a sequential architecture, transforms words into dense vectors using an Embedding layer, and captures intricate sequential patterns with two Long Short-Term Memory (LSTM) layers. This model aims to effectively classify sentiments in text data using a 50-dimensional embedding dimension and 20 % dropout layers. The use of rectified linear unit (ReLU) activations enhances non-linearity, while the SoftMax activation in the output layer aligns with the multi-class nature of sentiment analysis. Both training and test accuracy were well over 80%.

The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

Over the past few decades, cardiovascular disease and related complications have surpassed all others as the important causes of death on a universal scale. At the moment, they are the important cause of mortality universal, including in India. It is important to know how to find cardiovascular problems early so that patients get better care and prices go down. This project utilizes the UCI Heart Disease Dataset to develop ML and DL models capable of detecting cardiac diseases. Heart illness was categorized using Convolutional Neural Network (CNN) models, which are able to detect intricate patterns in supplied data. A confusion matrix rating, an F1-score, a ROC curve, accuracy, precision, and recall were some of the measures used to grade the model. It did much better than the Neural Network, Deep Neural Network (DNN), and Gradient Boosted Trees (GBT) models, with 91.71% accuracy, 88.88% precision, 82.75% memory, and 85.70% F1-score. Comparative study showed that CNN was the most accurate model. Other models had different balances between accuracy and recall. The experiment results show that the optional CNN model is a decent way to identify cardiovascular disease. This means that it could be used in healthcare systems to find diseases earlier and treat patients better.

This paper examines the impact of primary level classroom environments on the acquisition of English as a second language (L2) in Bangladesh, comparing English-medium and Bangla-medium schools. The study investigates how different instructional approaches and early exposure to English influence language proficiency among students. Through a mixed-methods approach, including surveys, interviews, and proficiency tests, the research reveals significant differences in language acquisition outcomes between the two educational settings. Findings indicate that students in English-medium schools, who are exposed to Natural approach methods of language learning and immersive English-speaking environments, demonstrate higher proficiency in speaking and listening skills compared to their Bangla-medium counterparts, who primarily receive grammar-focused instruction. The study highlights the critical role of early exposure to English, with students who begin learning the language at a younger age showing better phonological and syntactic development. Additionally, the integration of technology in language teaching emerges as a valuable tool for enhancing language learning, particularly in contexts with limited classroom exposure. The research suggests that Bangla-medium schools could benefit from adopting more interactive, student-centered teaching methods and integrating digital tools to support practical language use. The study's findings have significant implications for educational policy, advocating for a shift towards more immersive and communicative teaching practices to improve English language acquisition in Bangladesh. This research contributes to the broader understanding of SLA and offers practical recommendations for enhancing language education in similar contexts.

Intersectionality has significantly enhanced our understanding of how overlapping social identities—such as race, ethnicity, gender, sex, class, and sexual orientation—interact to shape individual experiences. However, despite its theoretical importance, much of the existing literature has relied on qualitative approaches to define and study intersectionality, limiting its application in predictive modeling, comparative analysis, and policy development. This paper introduces the concept of Quantitative Intersectionality Scoring System (QISS), a novel approach that assigns numerical scores to intersecting identities, thereby enabling a more systematic and data-driven analysis of intersectional effects. We argue that QISS can substantially enhance the utility and predictive validity of quantitative models by capturing the complexities of multiple, overlapping social determinants. By presenting concrete examples, such as the varying impacts of socioeconomic mobility on life expectancy among different intersectional groups, we demonstrate how QISS can yield more precise and reliable forecasts. Such a shift would allow policymakers and service providers to dynamically assess economic and health needs, as well as the uncertainties around them, as individuals move through different social and economic contexts. QISS-based models could be more responsive to the complexities of intersecting identities, allowing for a more quantified and nuanced evaluation of policy interventions. We conclude by discussing the challenges of implementing QISS and emphasizing the need for further research to validate these quantifications using robust quantitative methods. Ultimately, adopting QISS has the potential to improve the accuracy of predictive models and the effectiveness of policies aimed at promoting social justice and health equity.

The purpose of this study was to assess different periodontitis groups affected with mild, moderate and severe alveolar bone levels and treated using the Sandwich’s procedures. A total of 53 subjects, who had taken two sets of full-mouth standarized paralleling radiographs with mean observation time was 10.18±3.89 years and (ranges: 5.1 to 18.3 years) were collected for the past 20 years. The radiographic alveolar bone levels (RABL) at mesial and distal aspects of teeth were assessed by measuring the distance between cemento-enamel junction (CEJ) and the bone crest using an electronic digimatic caliper (EDC) under 7.5 magnified radiographs. The patients, who presented with SAP, were between 24 and 84 years of age, with a mean age of 54.8±10.2years. Although, the treatment of angular defects in molars with guided tissue regeneration, emdogain has been reported and has exhibited significant and predictable results, however, afforded very limited and less predictable results in the treatment of advanced class II and III maxillary furcation defects. The majority of root resection procedures are commonly recommended for treating advanced molar furcation, in particular located at molars with class III furcation involvement, there is still some controversy regarding the long-term prognosis after different treatment modalities. In general, the root resection procedure is a surgical approach for simultaneously performing a periodontal flap operation at first and followed by the amputation and/or resection of maxillary root(s). There are some complications and disadvantages, such as post-operative pain and bleeding, swelling, infection, etc. The present report is to describe the combination of therapeutic provisional prosthesis (TPP), fixed prosthesis, non-surgical procedure using root separation and/or resection (RSR) and for the treatment of advanced Class II and III furcation-Involved molars. In addition, evidenced-based clinical trials of retrospective and longitudinal data were also prescribed here. The purpose of this study was to present treatment procedures of Sandwich’s technique and retrospectively to evaluate the long-term clinical trials of this method in treating molar teeth with SAP and molar FI who were diagnosed as guarded and/or hopeless prognosis.

Myeloproliferative neoplasms (MPNs) are hematological disorders affecting myeloid stem cells. They are classified as Philadelphia (Ph) chromosome positive-chronic myeloid leukemia, and Ph-negative polycythemia vera, essential thrombocythemia, primary myelofibrosis, chronic neutrophilic leukemia, chronic eosinophilic leukemia, juvenile myelomonocytic leukemia, and MPN unclassifiable. This review is mainly focused on the Ph-negative MPNs namely, PV, ET, and PMF. These affect both males and females with a slight male predominance, with patients mainly presenting in the seventh decade. Patients often present with thrombotic events resulting in complications that lower survival rates. The major driver mutations that have been identified in MPNs are <i>JAK2 </i>Exon 14, <i>JAK2 </i>Exon 12, <i>MPL </i>Exon 10, and<i> CALR </i>Exon 9. The importance of these driver mutations gives due recognition to their inclusion into the 2022 diagnostic criteria of the MPN WHO Classification. However, other non-driver mutations have also been reported, especially in triple-negative cases. These mutations lead to downstream constitutive activation of the JAK/STAT signaling pathway, as well as the MAPK, and PI3K/Akt pathways. Insights into the molecular pathogenesis of MPN and its association with <i>JAK2</i>, <i>CALR</i>, and <i>MPL</i> mutations have identified JAK2 as a rational therapeutic target. Thus, as an approach to MPN therapy, JAK2 inhibitors, such as ruxolitinib, have been shown to effectively inhibit JAK2, and are currently in clinical trials in combination with other drug classes. This review comprehensively examines the molecular markers of the main Ph-negative MPNs, as well as diagnosis and treatment options.

Challenges are part and parcel of human activities. Quantitatively, a relational survey model research was adopted for the study. The population for this study comprised all Junior High School (JHS) Religious and Moral Education (RME) students in Bokro M/A JHS and the Eziom Methodist JHS in the Nzema-East Municipality of the Western Region. Purposive and random sampling techniques were used to select the schools and respondents for the study. The main instruments for data collection were a test and a questionnaire. The data from both the control and experimental groups on the challenges learners face in using the flipped classroom model in RME were analysed using means and standard deviations. The study indicates the challenges learners face by employing the flipped classroom model to learn include lack of data to access the internet, frequent light outs, lack of technological devices to access contents, frequent power outages, and difficulty in comprehending some of the materials given to them. Given that learners face frequent power outages during periods that they watch videos assigned to them by their teachers, it is recommended that the government and the Electricity Company of Ghana ensure that there is a constant flow of power to allow learners trying to use the flipped classroom to learn are without any hindrance. It is also recommended that the Ghana education service should organise continuing professional development for RME teachers on effective uses of technology in teaching and learning subject concepts. The government should also supply the basic schools with all the needed technological devices to promote effective teaching, learning and assessment.

With the rapid growth of online communication platforms, the identification and management of toxic comments have become crucial in maintaining a healthy online environment. Various machine learning approaches have been employed to tackle this problem, ranging from traditional models to more recent attention-based transformer networks. This paper aims to compare the performance of attention-based transformer networks with several traditional machine learning methods for toxic comments classification. We present an in-depth analysis and evaluation of these methods using a common benchmark dataset. The experimental results demonstrate the strengths and limitations of each approach, shedding light on the suitability and efficacy of attention-based transformers in this domain.

Spline smoothing is a technique used to filter out noise in time series observations when predicting nonparametric regression models. Its performance depends on the choice of the smoothing parameter. Most of the existing smoothing methods applied to time series data tend to over fit in the presence of autocorrelated errors. This study aims to determine the optimum performance value, goodness of fit and model overfitting properties of the proposed Smoothing Method (PSM), Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) smoothing parameter selection methods. A Monte Carlo experiment of 1,000 trials was carried out at three different sample sizes (20, 60, and 100) and three levels of autocorrelation (0.2, 05, and 0.8). The four smoothing methods' performances were estimated and compared using the Predictive Mean Squared Error (PMSE) criterion. The findings of the study revealed that: for a time series observation with autocorrelated errors, provides the best-fit smoothing method for the model, the PSM does not over-fit data at all the autocorrelation levels considered ( the optimum value of the PSM was at the weighted value of 0.04 when there is autocorrelation in the error term, PSM performed better than the GCV, GML, and UBR smoothing methods were considered at all-time series sizes (T = 20, 60 and 100). For the real-life data employed in the study, PSM proved to be the most efficient among the GCV, GML, PSM, and UBR smoothing methods compared. The study concluded that the PSM method provides the best fit as a smoothing method, works well at autocorrelation levels (ρ=0.2, 0.5, and 0.8), and does not over fit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to; non – parametric regression, non – parametric forecasting, spatial, survival, and econometrics observations.

The purpose of this study was to examine Religious and Moral<b> </b>Eduction teachers’ usage of the flipped classroom model Model and its Influence on JHS Students’ Academic Performance in the Nzema-East Municipality of Ghana.The study adopted the quasi-experimental research design. The population for this study comprised all JHS Religious and Moral Education students and teachers within the Nzema-East Municipality of the Western Region. With the help of the Krejcie and Morgan’s sample determination table, a sample of 110 comprising 10 teachers and 100 students were selected for the study through multi-stage sampling. The instruments used for data collection were tests and questionnaires. The study indicated that, the flipped classroom is a very potent method of teaching RME. This is so because the study provides enough evidence that the flipped classroom significantly improves the performance of learners more than the traditional approaches to teaching. This is even more appropriate in a technological era such as ours. The study also revealed that, teachers have a positive view of the use of the flipped classroom in teaching RME. Junior High School RME teachers are ready to adopt the flipped classroom model in their teaching provided challenges students face are eliminated. It is recommended that, School Improvement Support Officers and Headteachers should ensure that teachers use the flipped classroom to bring variations in lesson delivery so as to improve the academic achievements of learners. It is also recommended that government should provide technological devices to schools and teachers and ensure that teachers employ the various technological devices at their disposal to the benefit of their students.

<b>Abstract:</b> The purpose of this study was to examine the perception of Junior High School learners in using flipped classroom model in learning Social Studies in Aowin Municipality in the western north region of Ghana. Qualitatively, descriptive survey design was used for this study. The population of the comprised junior high school students of Enchi Methodist School. Convenient and purposive sampling techniques were used to select both the school and fifty (50) respondents for the study. The main instrument used for data collection was questionnaire. The questionnaire was made up of four-point Likert closed-ended statements that required learners to tick, where appropriate, their responses. The data were analysed using the SPSS software package. The data was edited, coded and analysed into frequencies, percentages with interpretations. The study concluded that, Junior High School Social Studies learners have a generally positive perception regarding the use of the flipped classroom model to learn Social Studies. This implies that, students positively respond to the use of flipped classroom as a teaching method. Since learners have a general positive perception of the use of the flipped classroom, it is recommended that teachers use the flipped classroom as a teaching method while parents are also encouraged to provide their wards with the necessary support such as phones and data to facilitate students’ learning.

The purpose of the study was to examine the use of rewards and punishment to improve discipline in Yiadom Boakye Demonstration Junior High School students (JHS) in Berekum of Ghana. A qualitative approach was adopted for the study. A case study research design was used to analyse the study. The population for the study consisted of all the eight (8) teachers in the Yiadom Boakye Demonstration Junior High School (JHS). A purposive sampling technique was used to select the eight teachers and school for the study. The main instrument used for the study was an interview. The study concluded that rewards and punishment lead to a change in the behaviours of the students affecting their academic performance their courses. The study also concluded that reward policies are needed in teaching to improve student learning by fostering enthusiasm in learning; it arouses students’ interest in learning, and change their personality traits and posture in class. The study revealed that a good punishment minimises bad behaviour, once is not having any bases on the student academic life, then it will not worsen the plight of his or her academic life. It is recommended that the colleges of education in Ghana should collaborate with Ghana Education Service and National Teaching Council to organise workshops and seminars on the use of rewards and punishments in the classroom situation.

Studies have highlighted the benefits of frequent quizzing in class. Frequent quizzing can promote more student attendance, engagement, practice and review, and achievement. Conversely, the opponents of frequent quizzing suggest that too frequent testing might hinder learning by frustrating anxious students and inhibiting larger units of instructional material. Notably, most studies have used degree examinations to evaluate the impact of quizzes on student learning and performance, yet little is known about whether quizzes can reinforce student performance in the in-course assessments (ICAs) despite ICA importance in student learning. The present study aimed to test the hypothesis that administration of weekly MCQ quizzes can enhance the leaning outcomes and performance of biomedical science students in assessment methods such as essay and oral presentation that can directly measure and provide information about student learning. It was therefore limited to in-course assessments. We found that the performance of the weekly quiz student group is remarkably better than that of the control student group in both the essay and oral presentation ICAs, which are two measures and indicators of student learning, suggesting improved student learning outcomes and performance after administrating weekly MCQ quizzes that also promoted student attendance in classrooms. The findings of this research study have implications for students, teachers, and curriculum designers in higher education.

Recently recommender systems are developed for a wide variety of applications. This article focuses on the applications, real-world examples, types, and analysis of various recommender systems.

The purpose of the study was to examine Social Studies teachers’ knowledge in the implementation of Social Studies lessons in formative assessment practices in Asante Akim North Municipality in the Ashanti Region of Ghana. The study employed both descriptive and interpretative techniques. The population for the study consisted of all Social Studies teachers and students in the Senior High Schools in the Asante Akim North Municipality of the Ashanti Region. Purposive, convenient and simple random sampling techniques were used to select the schools, teachers and students for the study in all, seventeen (17) Social Studies teachers and fifty (50) students were selected from six (6) Senior High Schools. The main instrument for data collection observation, interview and focus group discussion. The study revealed that not lessons presented were in line with the general objective of the subject (Social Studies). This affected the students understanding of concepts in the subject in helping them to right wrong their decision making. The study also indicates that teaching and learning Social Studies should not be one-man affair, with the teacher doing all the talking and the students doing all the listening. It is recommended that Ghana Education Service should organise workshops and in-service training for Social Studies teachers at the Senior High School level on how to present and evaluate social Studies lessons in order to realise the goals and objectives envisaged for national development. It is also recommended that teachers who have the exposure of the subject and are equally qualified to teach it at the Senior High Schools but are found teaching at the basic level, should be allowed by the Ghana Education Service to teach the subjects at the Senior High School level. The Government of Ghana should make it a laid down policy for the recruitment of qualified Social Studies teachers to teach at the Senior High School (SHS) level for effective assessment in the classroom.

The International Association for the Study of Pain has classified pain into nociceptive pain, neuropathic pain, and nociplastic pain based on the cause of the pain. At present, nociplastic pain is pain that is not nociceptive pain and has the following characteristics: no clear evidence of actual or threatened tissue damage causing the activation of peripheral nociceptors or evidence for disease or lesion of the somatosensory system causing the pain. If there is tissue damage, disease or lesion, it is neuropathic pain; if there is none, it is nociplastic pain. In other words, the difference in diagnosis is whether or not tissue damage, disease, or lesion can be found at the current medical level (testing equipment). The treatment of nociplastic pain is almost the same as the treatment of neuropathic pain. Fibromyalgia is included in nociplastic pain. To my knowledge, of the nociplastic pain and neuropathic pain, fibromyalgia is the disease with the highest number of pharmacological and non-pharmacological treatments with evidence of efficacy. Effective treatments for fibromyalgia are often given to neuropathic pain. This expands treatment options. I presume that tissue damage, disease or lesion will be found in fibromyalgia through advances in the medical science by the time humans emigrate to Mars. The distinction between nociplastic pain and neuropathic pain complicates the diagnosis of chronic pain. However, the distinction does not improve the treatment outcomes. Medical science is the discipline to find a treatment method that can produce better outcomes. In the event of a medical controversy, the medical theory with better treatment outcomes should be adopted. It is desirable to combine nociplastic pain and neuropathic pain into one pain. This will simplify diagnosis and increase treatment options (improve treatment outcomes) in nociplastic pain and neuropathic pain.

Crime is a serious and widespread problem in their society, thus preventing it is essential. Assignment. A significant number of crimes are committed every day. One tool for dealing with model crime is data mining. Crimes are costly to society in many ways, and they are also a major source of frustration for its members. A major area of machine learning research is crime detection. This paper analyzes crime prediction and classification using data mining techniques on a crime dataset spanning 2006 to 2016. This approach begins with cleaning and extracting features from raw data for data preparation. Then, machine learning and deep learning models, including RNN-LSTM, ARIMA, and Linear Regression, are applied. The performance of these models is evaluated using metrics like Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The RNN-LSTM model achieved the lowest RMSE of 18.42, demonstrating superior predictive accuracy among the evaluated models. Data visualization techniques further unveiled crime patterns, offering actionable insights to prevent crime.

The major automated plants have produced large volumes of high-quality products at low cost by introducing various technologies, including robotics and artificial intelligence. The code of many defects on the surface of products is embedded with economic loss and sometimes functionality loss because products are rarely found with defects. Therefore, most items’ production is done based on prediction and has an invisible fluctuation in production. The detection process for hidden defect images requires a lot of costs and needs to be supported for better progress and quality enhancement. Paint shop defects should be analyzed from color changes to detect defects effectively by preventing the variability of product demand over time. It is not easy to take visible light images without noise because the paint surfaces are glossy. A few parts of illumination and shadows remain in images, even in larger size and high-resolution images. The various painted surfaces are also needed to reflect both color and texture information in computer vision models to classify defects precisely. Several automated detection systems have been applied to paint shop inspections using lasers, infrared, x-ray, electrical, magnetic, and acoustic sensors. The chance of paint shop defects can be low, unnecessarily low, compared to clouds in the sky, but those chances impact defect functionalities. Thus, they are called as “lessons learned.” Lately, artificial intelligence has been introduced to the field of factory automation, and many defect detection feeds have found footsteps in machine learning and deep learning. Recent attempts at deep learning-based defect detection are proposing simple techniques using specific neural network architectures with big data. However, big data is still in its early stages, and significant challenges exist in normalizing and annotating such data. To get cost-efficient and timely solutions tailored to automotive paint shops, it might be a better consideration to combine deep learning solutions with traditional computer vision and more elaborate machine learning methods.

Recent technologies have made life smarter. vehicles are vital components in daily life that are getting smarter for a safer environment. Advanced Driving Assistance Systems (ADAS) are widely used in today's vehicles. It has been a revolutionary approach to make roads safer by assisting the driver in difficult situations like collusion, or assistance in respecting road rules. ADAS is composed of a huge number of sensors and processing units to provide a complete overview of the surrounding objects to the driver. In this paper, we introduce a road signs classifier for an ADAS to recognize and understand traffic signs. This classifier is based on a deep learning technique, and, in particular, it uses Convolutional Neural Networks (CNN). The proposed approach is composed of two stages. The first stage is a data preprocessing technique to filter and enhance the quality of the input images to reduce the processing time and improve the recognition accuracy. The second stage is a convolutional CNN model with a skip connection that allows passing semantic features to the top of the network in order to allow for better recognition of traffic signs. Experiments have proved the performance of the CNN model for traffic sign classification with a correct recognition rate of 99.75% on the German traffic sign recognition benchmark GTSRB dataset.