bluuebunny/crossref_metadata_2025 huggingface dataset:
GENE SELECTION AND MODIFIED LONG SHORT TERM MEMORYNETWORK-BASED LUNG CANCER CLASSIFICATION USING GENE EXPRESSION DATA
Authors:   Rathnavel Subramaniam College of Arts and Science, India, V Yuvaraj , G Pandiyan ,   Rathnavel Subramaniam College of Arts and Science, India, G Purusothaman ,   Rathnavel Subramaniam College of Arts and Science, India
Published: October 2024

Lung cancer is one of the fatal forms of cancer worldwide. Genetic variability has been identified as influencing a person vulnerability to lung cancer in epidemiologic research. A new study undertaken by a team of experts from the United States National Cancer Institute on 14,000 Asian women discovered that Asian women, regardless of whether they smoke or not, are more likely to acquire cancer owing to genetic abnormalities. Early detection of this lethal disease is a novel clinical application of microarray data. Recent research establishes a model for the early diagnosis of lung cancer. Additionally, multilayer perceptron, random subspace, and SMO (Sequential Minimal Optimization) approaches are used for classification. While information acquisition is typically a good indicator of an attribute significance, it is not perfect. A noticeable issue develops when knowledge gain is applied to qualities that might take on many distinct values. This paper provides an efficient gene selection model based on the Improved Whale Optimization Algorithm (IWOA) to address these concerns. It saves time and identifies relevant genes from gene expression data, increasing lung cancer categorization accuracy. Then, a Modified Long Short-Term Memory (MLSTM) Network is used to classify lung cancer. It accepts specified genes as inputs and determines which class they belong to, such as lung cancer or normal subjects. As demonstrated by empirical observations, the suggested model is effective in precision, recall, accuracy, and f –measure.
PERFORMANCE ANALYSIS OF AN EFFICIENT FRAMEWORK FOR INTRUSION DETECTION SYSTEM USING DATA MINING TECHNIQUES
Authors:   Rajah Serfogi Government College, India, C Amali Pushpam , J Gnana Jayanthi ,   Rajah Serfogi Government College, India
Published: October 2024

In the midst of the COVID-19 epidemic crisis, due to the tremendous development of mobile and internet technologies, the excessive growth in cyber-crime makes networksurity a major concern. As a result, individuals and companies are gradually moving towards the use of Intrusion Detection System (IDS), as it plays a persuasive role in monitoring and detecting the traffic of a network. However, high dimensional data affect the performance of IDS by reducing prediction accuracy, increasing false positive rate and classification time. Hence the focus of this research work is to develop a novel framework by integrating Auto – Bi Level (ABL) Classification with Double Filtering Fine Tuning – Ensemble Hybrid (DFFT-EH) feature selection. The experiments are conducted using NSL- KDD a benchmark intrusion detection dataset and it is proved that the proposed framework performs well with good accuracy, less false positive rate and less classification time when compared with voting ensemble classifier and other existing standard algorithms.
FACIAL EXPRESSION RECOGNITION BASED ON FEATURE ENHANCEMENT AND IMPROVED ALEXNET
Authors:   Gujarat Technological University, India, D Nayak Himanshukumar , K Sarvaiya Ashish ,   Government Engineering College, Bhavnagar, India
Published: October 2024

For interpersonal relations among humans, facial expressions are extremely important. Due to the complications in collecting required features from the frequently changing surroundings, uneven reflection from light sources, and many other aspects, facial expression recognition will encounter significant problems. A novel facial image recognition approach is proposed in this paper. Initially, a face image enhancement framework is created that is capable of enhancing the features of a face in a complicated context for this strategy. The improved Alexnet neural network is then created, which is based on the Alexnet architecture. Multi-scale convolution process is utilised in the improved Alexnet to enhance feature extraction capability. Batch normalisation is used for preventing network overfitting while also improving the model’s robustness. The Adabound optimizer and the Relu activation function are used to improve convergence and accuracy. The facial image feature improvement approach is helpful to increasing the capability of the improved Alexnet in trials from many aspects. For face images acquired in the natural surroundings, our technique displays significant stability, serving as a benchmark for the intelligent prediction of other facial images.
AN AUTOMATIC DETECTION OF BREAST CANCER USING EFFICIENT FEATURE EXTRACTION AND OPTIMIZED CLASSIFIER MODEL
Authors:   Government College for Women, Srikakulam, India, S Vani Kumari , K Usha Rani ,   Sri Padmavati Mahila Visvavidyalayam, India
Published: October 2024

Image Processing Techniques (IPTs) are widely used in healthcare. IPTs work on images to extract useful information from them. IPTs are effective in the early diagnosis of cancers which helps in their proper treatment. The number of human beings affected by cancer has drastically increased recently and more in women in the form of Breast Cancers (BCs). This work is on detecting BCs from image datasets using IPTs. This work proposes the use of Weiner Filters (WFs) in Preprocessing BC image data. WFs are applied to images as they can improve the quality of images. Image segmentations are executed using the Watershed Algorithm which detects Regions of Interest (ROIs) in images. Feature Extractions are done using the statistical method Gray Level Co-occurrence Matrix (GLCM). These extracted features are then selected using Black Widow Optimization (BWO) Algorithm. The selected features are then trained by Fuzzy Neural Networks (FNNs) which classify BC cases. The results of the classifier when evaluated were found to have a better performance on medical images in terms of Accuracy.
OPTIMAL ENSEMBLE FEATURE SELECTION (OEFS) METHOD AND KERNEL WEIGHT CONVOLUTIONAL NEURAL NETWORK (KWCNN) CLASSIFIER FOR MEDICAL DATASETS
Authors:   Bishop Heber College, India, C Sathish Kumar , P Thangaraju ,   Bishop Heber College, India
Published: October 2024

Disease detection software that works automatically in healthcare domain refers to the proactive or reactive use of computerised data systems for diagnosis of diseases. Medical knowledge base, data processing, and data analytics are the three major components of the system. The procedures of data processing and data analytics are crucial. Data mining (DM) techniques were used to process these processes. DM is a tool for finding patterns in massive amounts of data and retrieving knowledge. Clinical and diagnostic evidence has created a slew of reliable timely detection services and other health-related technology in the DM and healthcare industries. Artificial Intelligence (AI) in Machine Learning (ML) includes classification and predictive analytics. Identifying key characteristics and developing a classification model to determine whether the cases are disease or not is a difficult task. Feature selection (FS) refers to the process of reducing the quantity of input features when developing a predictive model. Reducing the number of input features is desirable because it cuts the computational cost of modelling while also improving the model’s performance in some cases. Instead of using a single feature selection, Optimal Ensemble Feature Selection (OEFS) solves a feature selection problem by integrating numerous feature selections. The OEFS method works by integrating the outputs of different single feature selection models like Divergence Weight Elephant Herding Optimization (DWEHO), Divergence Weight ButterFly Optimization Algorithm (DWBFO), and Differential Evolution (DE). By merging different subsets of features, Weighted Majority Voting (WMV) is used in finding the optimal feature subset. Classification model using Kernel Weight Convolutional Neural Network (KWCNN) classification is proposed. The convolution operation is a mathematical linear action across matrices that gives it its name. In terms of medical disease diagnosis, the proposed KWCNN classification performs quite well. To determine the performance of all classification algorithms, evaluation criteria such as sensitivity, specificity, f-measure, and accuracy were measured using a confusion matrix.
COMPUTATIONAL MODEL FOR MENTAL FACTOR CLASSIFICATION
Authors:   KBC North Maharashtra University, India, R.J. Ramteke , S.S. Gharde ,   KBC North Maharashtra University, India
Published: October 2024

Artificial Wisdom is advancement of Artificial Intelligence where wisdom should be recognized with the intelligence. Wisdom can be realized by adding values in the positive decisions. This is resulted in to the overall behavior of human being. Behavior can be demonstrated with the help of simulating thought process. Thoughts are generated in the mind along with the mental state. Mental state also known as mental factor is responsible for arousal of different types of thoughts. Ancient Indian cannon Abhidhamm claimed 52 mental factors. These are categorized in three classes such as Ethically Variable Factor, Unwholesome Factor and Beautiful Factor. Proposed computational model of consciousness demonstrates the classification of the mental states. Dataset consists of 445 samples collected from various respondents by asking three questions. Preprocessing is performed by using the techniques of Natural language processing and nonaxiomatic logic. Convolutional Neural Network Machine learning technique applied to classify the mental factors. Performance of the proposed system is measured by applying statistical measures such as Accuracy, Precision, Specificity, Recall and F1-Score. Accuracy for small and large database is obtained as 86.92% and 93.02% respectively.
TOP-DOWN AND BOTTOM-UP APPROACH FOR MINING MULTILEVEL ASSOCIATION RULES FROM CONCEPT HIERARCHICAL DATA IN DISTRIBUTED ENVIRONMENT
Authors:   A.D. Patel Institute of Technology, India, J. Prajapati Dinesh
Published: October 2024

Hierarchical Data mining using distributed environment is an imperative in big data analysis. Multilevel association rules can provide more substantial information than single level rules, and it also determines hierarchical knowledge from the dataset. Nowadays, numerous e-commerce and social networking sites generates vast amount of structural/semi-structural data in the form of sales data, tweets, text mails, web usages and so on. The data generated from such sources is so large that it becomes very difficult to process and analyze it using conventional approaches. This paper overcomes the computing limitation of single node by distributing the task on multi-node cluster. The performance of this system is compared based on minimum support threshold at diverse levels of concept hierarchy and by varying the dataset size. In this paper, the transactional dataset is created from huge sales dataset using Hadoop MapReduce framework. Then, two distributed multilevel frequent pattern mining algorithms MR-MLAB (MapReduce based Multilevel Apriori using Bottom-up approach) and MR-MLAT (MapReduce based Multilevel Apriori using Top-down approach) are implemented to find interesting level-crossing frequent itemset for each level of concept hierarchy. The hierarchical redundancy in multilevel association rules affects the quality of the market basket analysis. Hence, to improve the performance of the system, the hierarchical redundancy has to be removed from it. Finally, the time efficiency of proposed algorithms is compared with existing Traditional Multilevel Apriori (TMLA) Algorithm. The proposed algorithms with MapReduce framework are found efficient compared to the traditional algorithms.
MRI-BRAIN TUMOR CLASSIFICATION USING K-MEANS CLUSTERING AND ENHANCED HARMONY FEATURE SELECTION
Authors:   Bishop Heber College, India, B Sathees Kumar
Published: October 2024

This study introduces an enhanced feature selection method that is efficient in differentiating the malignant tumor patients from the benign patients by using K-Means clustering method combined with enhanced harmony search algorithm. The start of malignant tumor is caused by gene mutation process, it is very vital to identify and classify the presence or absence of the malignant tumor through analyzing the gene information. The planned methodology composed of four steps. The first step is to preprocess the original data by using min-max normalization. In the next step, generalized fisher score is used to find and eliminate the redundant data to confine the significant candidate genes. Selection of representative gene from each cluster is done by the K-Means clustering technique in the next phase. In the final phase the vital features for classification are selected by enhanced harmony search algorithm. The selected gene combination through this method for feature selection is then applied to the classification model and verified by means of 5-fold cross validation method. The projected model obtained a classification accuracy of up to 96.67%. Additionally, on comparing the projected method with other methods, the projected method performs well in classifying malignant tumor. This new method performs well in classification of brain tumors to malignant or benign. The projected model cannot be restricted only with the classification of brain tumors, but can also be used for other gene-related diseases effectively.
OBJECT DETECTION USING SEMI SUPERVISED LEARNING METHODS
Authors:   PSG College of Technology, India, Selvaganapathy Shymala Gowri , N Hema Priya, ,   PSG College of Technology, India, P.D. Rathika ,   PSG College of Technology, India, K Venkatachalam ,   University of Hradec Kralove, Czech Republic
Published: October 2024

Object detection is used to identify objects in real time using some deep learning algorithms. In this work, wheat plant data set around the world is collected to study the wheat heads. Using global data, a common solution for measuring the amount and size of wheat heads is formulated. YOLO V3 (You Look Only Once Version 3) and Faster RCNN is a real time object detection algorithm which is used to identify objects in videos and images. The global wheat detection dataset is used for the prediction which contains 3000+ training images and few test images with csv files which have information about the ground box labels of the images. To build a data pipeline for the model Tensorflow data API or Keras Data Generators is used.
NEURO DEEP FUZZY GENETIC ALGORITHM APPROACH FOR CLASSIFICATION AND DETECTION OF BRAIN TUMOR FROM LARGE DATASETS
Authors:   St Xavier’s Catholic College of Engineering, India, Soosaimariyan Mary Vasanthi , T Jayasree ,   Government College of Engineering, Bodinayakannur, India, P Sri Lekha ,   Vishnu Institute of Technology, India, Voddi Vijay Kumar Reddy ,   Saint Peter's University, United States of America
Published: October 2024

Brain tumors represent one of the most critical and life-threatening diseases. Early detection and accurate classification are essential for effective treatment planning and survival rate improvement. With the exponential growth of medical data, particularly in imaging and diagnostic datasets, traditional algorithms face limitations in handling large-scale, high-dimensional data efficiently. Conventional machine learning and diagnostic methods often struggle with classification accuracy, computational complexity, and overfitting in large, noisy datasets. Addressing these issues is crucial for the development of robust diagnostic tools capable of handling real-world clinical data. This paper presents a novel hybrid approach combining Neural Networks, Deep Learning, Fuzzy Logic, and Genetic Algorithms, termed Neuro Deep Fuzzy Genetic Algorithm (NDFGA), for the classification and detection of brain tumors from large datasets. Neural Networks and Deep Learning architectures are leveraged for feature extraction and hierarchical learning. Fuzzy Logic improves interpretability and manages uncertainty in medical data, while Genetic Algorithms optimize feature selection and model parameters. This hybrid method is designed to maximize classification accuracy while minimizing false positives and computational overhead. The proposed approach was tested on a large brain tumor dataset comprising over 10,000 MRI scans. The NDFGA approach demonstrated superior performance compared to standalone methods. It achieved a classification accuracy of 97.8%, a sensitivity of 96.5%, and a specificity of 98.1%. The model also showed improved robustness in handling large datasets, reducing false positives by 12% and computational time by 15% compared to traditional methods. This hybrid model presents a scalable and efficient solution for brain tumor detection, especially in clinical environments.
ENHANCING ADAPTIVE LEARNING AND DECISION-MAKING SYSTEMS USING SWARM INTELLIGENCE AND DEEP LEARNING FOR ADVANCED AI APPLICATIONS
Authors:   Siddhant College of Engineering, India, Gupta Brijendra , Dusane Atul ,   Shri Vile Parle Kelavani Mandal's Narsee Monjee Institute of Management Studies, India, P. Patil Neeta ,   Universal College of Engineering, India, Mane Yogita Deepak ,   Universal College of Engineering, India, Raut Sanketi ,   Universal College of Engineering, India, Agrawal Akshay ,   Universal College of Engineering, India
Published: October 2024

The rapid development of autonomous vehicles (AVs) demands robust and adaptive AI systems capable of handling complex real-world environments. Traditional optimization and learning algorithms often struggle with dynamic and uncertain conditions, leading to suboptimal decision-making. Swarm intelligence, particularly Hawk Fire Optimization (HFO), offers a promising solution by simulating cooperative behaviors seen in nature, like hawks in hunting, to optimize decision-making processes. Coupled with advanced deep learning techniques like Federated Dropout Learning (FDL), this hybrid approach can enhance the adaptability, scalability, and efficiency of AI systems. This paper addresses the challenge of improving decisionmaking and learning in autonomous vehicles by integrating HFO with FDL. HFO optimizes parameters in real-time, allowing AVs to adapt rapidly to changing environments. Federated Dropout Learning, a variant of federated learning, further improves system resilience by sharing learning across distributed nodes while minimizing communication overhead and enhancing privacy. By combining these methods, the proposed system ensures robust performance in unpredictable scenarios. Experimental results show that the hybrid model outperforms traditional methods in terms of decision accuracy, response time, and energy efficiency. Specifically, the system achieved a 12% improvement in decision accuracy, reduced processing time by 18%, and cut energy consumption by 22%, compared to standard algorithms. These findings suggest that the combination of HFO and FDL can significantly improve the performance of autonomous vehicles, providing safer and more efficient AI-driven navigation.
NSEMBLE FINE TUNED MULTI LAYER PERCEPTRON FOR PREDICTIVE ANALYSIS OF WEATHER PATTERNS AND RAINFALL FORECASTING FROM SATELLITE DATA
Authors:   Sinhgad Institute of Technology, India, V Surana Amruta , Pawar Suvarna Eknath ,   MIT Art, Design and Technology University, India, Raha Shrinwantu ,   Bhairab Ganguly College, India, Mali Nilesh ,   Ajeenkya D Y Patil School of Engineering, India, Mukherjee Tilak ,   Haldia Institute of Technology, India
Published: October 2024

The accurate prediction of weather patterns and rainfall forecasting is critical for various sectors, including agriculture, disaster management, and water resource planning. Traditional models often struggle to capture the complex interactions between atmospheric variables, particularly when integrating diverse types of satellite data (binary, categorical, and numerical). To address this challenge, an ensemble fine-tuned multi-layer perceptron (MLP) model is developed, combining the strengths of multiple machine learning techniques for more robust predictions. The primary problem is the difficulty in handling mixed data types while maintaining high prediction accuracy. Satellite data, including binary indicators (e.g., cloud presence), categorical features (e.g., cloud types), and numerical variables (e.g., temperature, humidity, and wind speed), provide rich information but require specialized processing for effective forecasting. The proposed method involves fine-tuning an ensemble of MLP models with backpropagation, dropout regularization, and batch normalization to reduce overfitting and enhance generalization. The ensemble integrates predictions from individual MLP models, each trained on different subsets of features (binary, categorical, numerical). This technique allows the model to leverage complementary strengths and produce more accurate rainfall forecasts. Satellite data is preprocessed and normalized before training, and categorical variables are one-hot encoded to ensure compatibility with the MLP architecture. Results from testing on historical satellite weather datasets demonstrate significant improvements in forecast accuracy. The ensemble MLP achieved an accuracy of 91.3%, with a precision of 90.7%, recall of 89.5%, and an F1-score of 90.1%. The model performed exceptionally well in identifying critical rainfall events, reducing false positives by 12% compared to traditional models.
AN ETHEREUM-BASED ELECTRONIC HEALTH RECORD SYSTEM WITH SQL QUERY SUPPORT
Authors:   Sri Sathya Sai Institute of Higher Learning, India, Tamang Suvam , M.S. Srinath ,   Sri Sathya Sai Institute of Higher Learning, India, Baruah Pallav Kumar ,   Sri Sathya Sai Institute of Higher Learning, India
Published: October 2024

Blockchains’ power of decentralization, immutability, and transparency has found its application in many fields. The blockchain is an append-only structure. As the blockchain grows, accessing blocks from the past in an efficient manner has become a challenging task. Further, blockchains were not initially envisioned to be read-heavy systems. With the passage of time, more and more applications are using blockchains and therefore blockchains need to provide active support for high read loads concerning the history state as well. To facilitate data querying in blockchain, we have proposed an SQL query processing feature in the Ethereum blockchain through a decentralized application. More specifically, we build an Ethereum-based Electronic Health Record (EHR) system with SQL query support. The following approaches for query processing have been explored and implemented: (i) linearly scanning blockchain, (ii) scanning only from a user-specified block, (iii) replication in database, (iv) indexing in a database, and (v) using smart contracts. Our timing analysis across these implementations reveals that the smart contracts-based approach has reasonable performance gains compared the other approaches.
LEVERAGING DATA-DRIVEN TECHNIQUES FOR EFFICIENT DATA MINING IN CLOUD COMPUTING ENVIRONMENTS
Authors:   Dell EMC Corporation, Bengaluru, India, Venkataramana Jaladurgam
Published: October 2024

The capacity to efficiently use big data and analytics is becoming a critical differentiator for company growth in today's data-driven environment. Using important trends, obstacles, and best practices as a framework, this article investigates how to promote company growth via the use of big data and analytics. An important issue in cloud computing is deciding on an acceptable amount and location of data. Decisions about resource management are based on data aspects and operations in data-driven infrastructure management (DDIM), a novel solution to this problem. It is critical to have a unified system that can manage various forms of big data and the analysis of that data, as well as common knowledge management functions. The approach stated in this research is DD-DM-CCE, or Data-Driven Methods for Efficient Data Mining in Cloud Computing Environments. Improving data using derived information from maximum frequent correlated pattern mining is the main focus of the work. By considering the centrality factor, the DD-DM-CCE method may help choose the best locations to store data in order to reduce access latency. In order to gain a competitive edge, this study offers a cloud-based conceptual framework that can analyze large data in real time and improve decision making. Efficient big data processing is possible with cloud computing infrastructures that can store and analyze massive amounts of data, as this reduces the upfront cost of the massively parallel computer infrastructure needed for big data analytics. According to simulations run on cloud computing, the DD-DM-CCE approach does better than the status quo regarding hit ratio, effective network utilization, and average response time. According to this study, data mining methods are valuable and successful in predicting how consumers will utilize cloud services.
AN OPTIMIZED BIDIRECTIONAL CONVOLUTIONAL RECURRENT NEURAL NETWORK ARCHITECTURE WITH GROUP-WISE ENHANCEMENT MECHANISM OF SENTIMENTS FOR THE PERSPECTIVE OF CUSTOMER REVIEW SUMMARIZATION
Authors:   RVS College of Arts and Science, India, V Sriguru , D Shanmuga Rajathi ,   RVS College of Arts and Science, India
Published: October 2024

Customer reviews play pivotal roles in consumers’ purchase decisions, but the sheer volume of text data can be overwhelming. In existing system, while ensemble methods can enhance performance, the associated computational complexity and resource intensiveness should be carefully considered, and appropriate measures should be taken to address these challenges in the context of Customer Review Summarization. This study introduces a Particle Swarm Optimization (PSO) based optimized architecture for a Bidirectional Convolutional Recurrent Neural Network (BiCRNN) with a group-wise enhancement mechanism tailored for Customer Review Summarization and named as OBiCRNN. This model is designed for the perspective of customer review summarization, aiming to effectively capture sentiments and generate concise summaries. The integration of PSO optimizes the network parameters, enhancing the learning process. Feature extraction is done by Modified Principal Component Analysis (MPCA) which uses correlated feature sets and extracts most informative features for given datasets. BiCRNN utilizes bidirectional LSTM and GRU layers for comprehensive context understanding, while the group-wise enhancement mechanism categorizes sentiment-related features, amplifying essential sentiments and attenuating less relevant ones. With this novel approach, the architecture leverages both PSO and BiCRNN for an advanced framework in customer review summarization where outcomes demonstrate the effectiveness of the deep learning (DL) model in producing coherent and informative summaries, enhancing the accessibility of customer feedback for both consumers and businesses. The study contributes to the field of natural language processing (NLP) and customer sentiment analysis, offering a scalable solution for managing the wealth of information present in online customer reviews.
ADVANCING INFORMATION TECHNOLOGY WITH IMMUNOLOGICAL COMPUTING – SOFT COMPUTING TECHNIQUES FOR ADAPTIVE AND ROBUST SYSTEMS
Authors:   Government Arts and Science College, Dr. Radhakrishnan Nagar, India, S Kaliswaran , R Sivasankari ,   Easwari Engineer College, India, Hanumantharao Attru ,   Aditya University, India, V Saravanan ,   Nehru Institute of Technology, India, G Gokul Kumari ,   Saudi Electronic University, Kingdom of Saudi Arabia
Published: October 2024

The field of Information Technology (IT) is evolving rapidly, and with this growth comes the need for systems that are both adaptive and robust. Biological systems, especially the human immune system, demonstrate remarkable adaptability and resilience, inspiring the development of Immunological Computing (IC). This paper explores the application of immunological principles in Soft Computing techniques to create systems capable of responding to dynamic environments. Current IT systems often face challenges such as handling unpredictable changes, scalability, and security threats. Traditional computing approaches struggle to address these issues efficiently due to their rigid structures and limited adaptability. Immunological Computing, inspired by the immune system’s ability to learn, remember, and adapt, offers a promising solution. The proposed method integrates immune system mechanisms like clonal selection, immune memory, and self/non-self-recognition into computational models. These models are coupled with soft computing techniques such as fuzzy logic, genetic algorithms, and neural networks, enhancing the system’s ability to adapt to changing environments and uncertainties. In simulated tests, this approach demonstrated a significant improvement in robustness and adaptability compared to traditional IT systems. For instance, in a cybersecurity application, the immunological-based system detected and neutralized 94.6% of threats, a notable improvement over the 82.3% detected by conventional systems. Similarly, in a resource optimization scenario, the system adapted to dynamic workloads with an efficiency increase of 15% compared to static systems.
Analysis of Wind Response of Braced and Unbraced Tall Building
Authors: Kumar Vijay
Published: October 2024

As high-rise buildings become increasingly prevalent worldwide, their complex structural components and the need for high stability pose challenges for safety and design requirements. The present study investigates the influence of bracing systems and plan aspect ratios on the ability of tall buildings to withstand wind loads. The study aims to analyze the effects of these variables on the wind-induced responses of tall buildings and improve bracing system design for structures with high aspect ratios. Numerical simulations using finite element analysis software are conducted, creating various building models with different aspect ratios and bracing systems. The displacement, drift, base shear responses and time period of each model are examined to evaluate the effectiveness of different bracing systems. The results highlight the significant impact of aspect ratio on the response to wind loads, with larger aspect ratios experiencing greater motion and stress. However, well-designed bracing systems prove effective in reducing wind-induced motion and stress. The study provides insights on best bracing system for different aspect ratios, offering valuable insights for creating safer and more resilient high-rise buildings.
India’s Economy to Hit and to Become USD 10 Trillion by 2030
Authors: Badkar Dr. Duradundi Sawant
Published: October 2024

India is currently a $ 3.1 trillion economy. It took India 60 years since Independence to become one trillion-dollar economy, but the next trillion dollars was added only in 7 years. The 3rd trillion was added in just 5 years in 2019. The growing momentum could see India add on average 1 trillion dollar to its economy every two years for the next 14-15 years. According to the report of the Centre for Economics and Business Research (CEBR), India will become a $ ten trillion economy by 2035. India's aim to grow into a $10-trillion economy over the next decade will be fuelled by manufacturing in sunrise sectors such as semiconductors, electronics manufacturing, electric vehicles ecosystem, renewable energy and defence, according to a report by Boston Consulting Group and Matrix Partners.
The effect of PBL integrated RMS on biological literacy and critical thinking ability of high school students
Authors: Nihlah Khairotun , Ristanto Rizhal Hendi , Kurniati Tri Handayani
Published: October 2024

This study aimed to determine the effect of Problem-Based Learning (PBL) integrated Reading, Mind Mapping, and Sharing (RMS) on biological literacy and critical thinking skills in biotechnology material. This type of research is Quasi-Experimental with pre-test-post-test experiments and a control group design. The researcher conducted the research because most of the students who became respondents considered biotechnology material less attractive, so students' understanding and activeness could have been more optimal in the learning process. The research samples were 72 students of class X MIPA at SMA Negeri 2 Kota Serang, Banten. The research sample was divided into two groups: the experimental group that applied the PBL integrated RMS model and the control group that applied the PBL model. The research instruments used were biological literacy ability tests and critical thinking ability tests. Biological literacy skills were measured using biological literacy-based multiple-choice test instruments, while critical thinking skills were measured using essay test instruments. The hypothesis test used was MANOVA statistical analysis. The results of statistical tests showed a significant effect of PBL integrated with RMS on biological literacy and critical thinking skills on biotechnology material with a significance value of 0.000 < α. Students with an integrated RMS learning model are more effective than those with a PBL learning model, which is indicated by the post-test value being higher than the pre-test value. This research contributes to the field of biology education by showing that the PBL learning model integrated with RMS can be an effective alternative learning model to improve students' biological literacy and critical thinking skills. The findings of this study are expected to be useful for biology teachers
Development augmented reality assisted teaching modules human sensory system to improve student learning outcomes
Authors: Iwan I. , Sirait Silvia Hanna Kusuma , Rumalolas Nuryanti
Published: October 2024

In the era of digital transformation, the use of technology to improve the quality of learning is very important. This research aimed to develop a teaching module for human sensory system material assisted by augmented reality media to improve students' biology learning outcomes. This research uses research and development methods. The subjects of this research were 22 Biology Education students at the University of Papua. Data collection instruments include validation sheets and questionnaires to determine the validity and responses of students to teaching modules as well as providing multiple choice questions to measure learning outcomes. The data analysis technique is descriptive by calculating validation results, analyzing student response questionnaires and student learning outcomes. The research results show that media and material expert validation test results obtained a percentage of 93.33% with a very valid category. The results of assessing student responses in the trial obtained an average percentage of 85.13% in the very good category. Student biology learning results show an average of 61.36 in the sufficient category. The conclusion of the research on Augmented Reality-assisted teaching modules meets the criteria of being valid, practical and effective and improving students' biology learning outcomes.
Learning obstacles in the generalization process: In case number pattern topic
Authors: Maula Nyla Farhatul , Suryadi Didi , Jupri Al
Published: October 2024

Generalization plays an important role in mathematics because it is considered inherent in mathematical thinking in general. Where number pattern is a topic that is closely related to generalization. Problems are still found in generalizing number patterns so this can cause learning obstacles. This research aims to identify learning obstacles for Junior high school students in the generalization process with a focus on number pattern topics. This research is qualitative research with a case study method. The research subjects consisted of 30 grade 8 students in Jakarta who had studied number pattern. Subjects were given three test tasks, the results of which were used to figure out learning obstacles in the generalization process experienced by students and then continued with interviews with ten subjects. The findings show that learning obstacles in the pattern generalization process occur primarily at the expression and symbolic stages. Most students bypassed the generalization method, relying instead on formulas or manual calculations. Based on these results, it is recommended that educators implement didactic designs that offer targeted interventions during the expression and symbolic stages, such as using more interactive and symbolic reasoning activities to strengthen students' understanding and generalization skills in number patterns.
Farklı Spor Branşındaki Bireylerin Bazal Metabolik Hız Ve Beslenme Bilgi Düzeylerinin İncelenmesi
Authors: Hepsert Sezgin , Sezer Burcu Sıla , Evin Taha Emirhan , Kılıç Yakup
Published: October 2024

Bu araştırmada; katılımcıların bazal metabolik hız ve beslenme bilgi düzeylerini uğraşılan branş ve cinsiyet değişkenleri üzerinden çözümlemek amaçlanmıştır. Araştırma tarama modeli kullanılarak gerçekleştirilmiştir. Araştırmaya Fırat Üniversitesinde 2023-2024 eğitim-öğretim yılında öğrenim gören ve kolayda örnekleme yöntemiyle seçilen yaşları 19 ile 25 ( yaş22,55±3,83) arasında değişen 188’ü kız 146’si erkek olmak üzere toplam 334 öğrenci gönüllü olarak katılmışlardır. Dahil edilme kriterlerini müsabaka takvimine sahip herhangi bir organizasyonda en az son 3 yıldır herhangi bir branşta lisanslı spor yapmak ve veri toplama araçlarını doldurabilecek zihinsel yeterlilikte olmak oluşturmuştur. Örnekleme dahil edilen bireylerin branşları dağılımı sırasıyla basketbol (n=62), voleybol (n=94), futbol (n=110) ve hentbol (n=68) şeklindedir. Araştırmada katılımcıların demografik özelliklerini ortaya koymak için ‘’Katılımcı Bilgi Formu’’, bazal metabolik hızın belirlenmesi için ‘’Bazal Metabolik Hız Denklemi’’ ve beslenme bilgi düzeylerinin belirlenebilmesi için ‘’Beslenme Bilgi Düzeyi Ölçeği’’ kullanılmıştır. Verilerin analizinde betimsel istatistikler ve tek yönlü varyans analizi kullanılmıştır. Araştırma bulguları incelendiğinde kız ve erkeklerde bazal metabolik hız skorunun en yüksek basketbol branşında seyrettiği ancak spor branşı ve bazal metabolik hız arasında anlamlılık seviyesinde bir ilişki olmadığı tespit edilmiştir. Katılımcılarda en yüksek beslenme bilgi skoruna sahip branşın basketbol, cinsiyetin ise erkek olduğu belirlenirken, sırasıyla BBD ve spor branşı arasında anlamlı bir farklılık bulunurken, cinsiyet değişkeninde tespit edilmemiştir. Sonuç olarak farklı spor branşlarının bazal metabolik hız ve beslenme bilgi düzeyi değişkenlerini etkileme potansiyeli olduğu söylenebilir.
Evaluating the Effectiveness of Ahmed Glaucoma Valve Implantation in Reducing Intraocular Pressure in Refractory Glaucoma in the Saudi Arabian Population at a Tertiary Care Centre
Authors: Almarzouki Nawaf
Published: October 2024

Objectives: To assess the efficacy of Ahmed glaucoma valve implantation (AGVI) in lowering intraocular pressure (IOP) in patients with refractory glaucoma.
Methods: In this retrospective study, we analyzed the medical records of 60 patients who received AGVI for refractory glaucoma. Data collected included demographics, type of glaucoma, preoperative and postoperative IOP, number of glaucoma medications, and any surgical complications. Success was defined as achieving an IOP <21 mmHg at the final follow-up visit.                                                                                                                                                  
Results: At the final follow-up (mean duration of 11.6 months), AGVI achieved successful IOP control (IOP of <21 mmHg) in 40 eyes (66.7%). The mean number of glaucoma medications decreased significantly from 2.95 preoperatively to 1.98 postoperatively (p < 0.001). Neovascular glaucoma (NVG) had a lower success rate compared to other types of glaucoma. Prior glaucoma surgery was associated with a higher risk of failing to reach target IOP, although this trend was not statistically significant (p = 0.108).
Conclusion: AGVI implantation effectively lowered IOP in patients with refractory glaucoma, especially in the short term. However, long-term success rates were lower, and NVG showed less favorable outcomes. AGVI is a valuable option for managing refractory glaucoma, but careful patient selection is essential. Further research is needed to evaluate long-term efficacy and refine treatment strategies.
Стандартизация модульных зданий и их конструктивные решения
Authors: Трёкин Николай , Кодыш Эмиль , Келасьев Николай , Терехов Иван , Гасиев Азамат , Шмаков Сергей
Published: October 2024

В статье рассмотрены конструктивные решения модулей из железобетона, в том числе крупногабаритные, а также на стальном каркасе, которые в настоящее время производят в России.
Возросший в начале XXI века объём строительства модульных зданий сдерживается недостаточным количеством нор­мативной и рекомендательной документации, позволяющей ускорить и повысить качество проектирования и возведения зданий самого различного назначения.
В 2023 году институтом «ЦНИИПромзданий» по заказу ФАУ «ФЦС» Минстроя России был разработан ГОСТ Р «Модульные здания и конструкции. Термины и определения. Классификация».
Целью национального стандарта являлась разработка классификации и уточнение терминов и определений, применяемых для обозначений модульных зданий и самих модулей.
В терминологии для модульного строительства в разработанном ГОСТ Р под «модулем» понимается объёмная конструкция, предназначенная для возведения зданий или использования в качестве отдельно стоящего объекта.
В статье приведены блок-схемы классификации модульных зданий и отдельных модулей и даны предложения по даль­нейшему совершенствованию нормативных документов в области модульного строительства.
Проблемы и перспективы развития информационного моделирования для реставрации объектов культурного наследия
Authors: Пухаренко Юрий , Шангина Нина , Харитонов Алексей , Сизов Дмитрий
Published: October 2024

В статье рассмотрено современное состояние процесса внедрения технологий информационного моделиро­вания в практику разработки научно-проектной документации по реставрации и приспособлению памятников архитектуры. Представлены принципы формирования полноценной информационной модели объекта культурного наследия, предложены организационно-методологические подходы и практические решения для повышения эффективности применения цифровых технологий для целей сохранения объектов культурного наследия.
Типология социального жилища для молодежи в Европе, Америке, Азии в XX–XXI веках
Authors: Огиенко Евгений
Published: October 2024

Статья посвящена типологии отечественного и зарубежного социального жилища. В мире разнообразие форм жилища существенно расширилось, отражая изменяющиеся потребности, ценности и тенденции в жилищной политике. Было рассмотрено 118 объектов социального жилища за рубежом, которые были классифицированы, опираясь на общие признаки, в типы социального жилища для молодёжи (МСЖ).
Использованы методы графоаналитического анализа, сбора и анализа данных объектов социального жилища для вы­явления особенностей каждого типа МСЖ.
По результатам исследования был установлен 21 тип, но в данной статье рассмотрено четыре из них. Каждый из этих типов учитывает разнообразные потребности и предпочтения граждан, что подразумевает создание гибких и трансформируемых решений квартир и общественных пространств. Данные типы способствуют не только обеспечению доступного жилища, но и созданию сообщества жильцов. Типология жилища для молодёжи учитывает различные аспекты, включая организацию городского пространства, функциональные особенности, объёмно-пространственную структуру и планировку помещений, формы собственности и предоставления жилья, а также статус и степень общности проживания.
Курзал в Ливадии. Забытый памятник сталинской эпохи
Authors: Слюнькова Инесса , Рыжко Ольга , Карушкина Наталья , Усманова Надежда
Published: October 2024

Статья направлена на устранение пробела знаний и привлечение внимания к замечательному памятнику архитектуры середины XX века, находящемуся под угрозой гибели. Объектом исследования является парковый комплекс и здание театра под открытым небом советского времени, ставшие достойным и органичным продолжением императорского дворцово-паркового ансамбля Ливадии. Установлено проектное название объекта - «Курзал» в дополнение к закрепивше­муся позднее определению Летний кинотеатр. По архивным документам определяются датировки строительства, начатого в бытность Ливадийского ансамбля госдачей И.В. Сталина и завершённого после передачи его профсоюзному санаторию. Впервые предпринята реконструкция градостроительной композиции, структуры и планировки комплекса, определяется степень сохранности. Проводится анализ художественного стиля, пластической декорации произведения.
Работа опирается на поиски архивных документов, на натурные, реставрационные исследования. Сформирована система данных, представлены обоснования для принятия экстренных мер по сохранению ценного памятника советского неокласси­цизма и присвоения ему официального статуса «Объект культурного наследия». Подчёркивается практическая значимость научной реставрации Курзала и введения его в состав объектов показа Ливадийского дворца-музея, резервы введения со­оружения в общественную жизнь Большой Ялты.
Синтез искусств в архитектуре 1960–1980-х годов в городе Горьком
Authors: Красавина Ирина
Published: October 2024

В статье рассматриваются основные черты синтеза искусств в архитектуре 1960-1980-х годов в городе Горьком. Анализируются особенности проявления синтеза искусств на фасадах жилых зданий, детских дошкольных образовательных учреждений, общественных зданий районного и общегородского значения, а также малые архитектурные формы. Рассматри­ваются композиция и идейное содержание мозаичных панно. Анализируется их размещение относительно разных районов города. Даётся оценка существующего состояния исследуемых произведений, устанавливается факт утраты мозаичного панно или повреждения отдельных его частей. Определяется степень влияния творчества отдельных художников-монументалистов на становление приёма синтеза искусств в архитектуре Горького. Проводятся параллели между исследуемыми объектами указан­ного временного периода в Горьком и аналогичными примерами в других городах страны.
Каркас и ткань градостроительной системы. Санкт-Петербург, век XVIII и век XXI
Authors: Лавров Леонид , Молоткова Елена
Published: October 2024

На примере Санкт-Петербурга рассматриваются закономерности управляемого формирования и развития градостроительной структуры. Фиксируются очаговый характер городской ткани, возникавшей на начальной фазе освоения природного транспортного каркаса, функциональная и пространственная локализация поселений. Особое внимание уделяется радикальным преобразованиям второй половины XVIII века, когда внеуличные водные пути были дополнены сетью регулярных городских дорог, обеспечивших переход на новые формы землепользования. Отмечается роль внеуличных транспортных путей в конце XIX - середине ХХ века: железной дороги, способствовавшей развитию агломерации, и метрополитена, повысившего связность городской ткани. Анализируется первый опыт строительства в Санкт-Петербурге автомагистралей и скоростных дорог и подчёркивается, что при оценке воздействия новых элементов транспортного каркаса на градостроительную среду сейчас предпочитают судить только по данным об интенсивности передвижений, остальные составляющие многоаспектно­го влияния не привлекают внимания. Высказывается предположение, что продолжающаяся реконструкция транспортного каркаса будет усиливать локализацию городской ткани в местах её соприкосновения с новым внеуличным транспортом.
Первая часть статьи «Каркас и ткань градостроительной системы. Санкт-Петербург, век XVIII и век XXI» опубликована в № 2 журнала «Academia. Архи­тектура и строительство» за 2024 год.
Влияние единой публичной власти на процесс агломерирования: восходящая и нисходящая модели
Authors: Майборода Виктор
Published: October 2024

В статье приведён анализ сложившейся системы нормативного регулирования смысловой определённости термина «агломерация». На основе системного анализа федерального и регионального законодательного опыта сформули­ровано предложение о возможности использования термина «восходящая модель» для агломераций, формирующихся есте­ственным образом, и термина «нисходящая модель» для агломерационных процессов, выступающих предметом воздействия со стороны единой публичной власти России.