Intelligent Computational Model Research Articles

Optimising Building Energy and Comfort Predictions with Intelligent Computational Model

Building performance prediction is a significant area of research, due to its potential to enhance the efficiency of building energy management systems. Its importance is particularly evident when such predictions are validated against field data. This paper presents an intelligent computational model combining Monte Carlo analysis, Energy Plus, and an artificial neural network (ANN) to refine energy consumption and thermal comfort predictions. This model addresses various combinations of architectural building design parameters and their distributions, effectively managing the complex non-linear relationships between the response variables and predictors. The model’s strength is demonstrated through its alignment with R2 values exceeding 0.97 for both thermal discomfort hours and energy consumption during the training and testing phases. Validation with field investigation data further confirms its accuracy, demonstrating average relative errors below 2.0% for total energy consumption and below 1.0% for average thermal discomfort hours. In particular, an average underestimation of −12.5% in performance discrepancies is observed when comparing the building energy simulation model with field data, while the intelligent computational model presented a smaller overestimation error (of +8.65%) when validated against the field data. This discrepancy highlights the model’s potential and reliability for the simulation of real-world building performance metrics, marking it as a valuable tool for practitioners and researchers in the field of building sustainability.

Three intelligent computational models to predict the high-performance concrete mixture

Three intelligent computational models to predict the high-performance concrete mixture

BBB-PEP-prediction: improved computational model for identification of blood–brain barrier peptides using blending position relative composition specific features and ensemble modeling

BBPs have the potential to facilitate the delivery of drugs to the brain, opening up new avenues for the development of treatments targeting diseases of the central nervous system (CNS). The obstacle faced in central nervous system disorders stems from the formidable task of traversing the blood–brain barrier (BBB) for pharmaceutical agents. Nearly 98% of small molecule-based drugs and nearly 100% of large molecule-based drugs encounter difficulties in successfully penetrating the BBB. This importance leads to identification of these peptides, can help in healthcare systems. In this study, we proposed an improved intelligent computational model BBB-PEP-Prediction for identification of BBB peptides. Position and statistical moments based features have been computed for acquired benchmark dataset. Four types of ensembles such as bagging, boosting, stacking and blending have been utilized in the methodology section. Bagging employed Random Forest (RF) and Extra Trees (ET), Boosting utilizes XGBoost (XGB) and Light Gradient Boosting Machine (LGBM). Stacking uses ET and XGB as base learners, blending exploited LGBM and RF as base learners, while Logistic Regression (LR) has been applied as Meta learner for stacking and blending. Three classifiers such as LGBM, XGB and ET have been optimized by using Randomized search CV. Four types of testing such as self-consistency, independent set, cross-validation with 5 and 10 folds and jackknife test have been employed. Evaluation metrics such as Accuracy (ACC), Specificity (SPE), Sensitivity (SEN), Mathew’s correlation coefficient (MCC) have been utilized. The stacking of classifiers has shown best results in almost each testing. The stacking results for independent set testing exhibits accuracy, specificity, sensitivity and MCC score of 0.824, 0.911, 0.831 and 0.663 respectively. The proposed model BBB-PEP-Prediction shown superlative performance as compared to previous benchmark studies. The proposed system helps in future research and research community for in-silico identification of BBB peptides.

Identifying the Question Similarity of Regulatory Documents in the Pharmaceutical Industry by Using the Recognizing Question Entailment System: Evaluation Study.

The regulatory affairs (RA) division in a pharmaceutical establishment is the point of contact between regulatory authorities and pharmaceutical companies. They are delegated the crucial and strenuous task of extracting and summarizing relevant information in the most meticulous manner from various search systems. An artificial intelligence (AI)-based intelligent search system that can significantly bring down the manual efforts in the existing processes of the RA department while maintaining and improving the quality of final outcomes is desirable. We proposed a "frequently asked questions" component and its utility in an AI-based intelligent search system in this paper. The scenario is further complicated by the lack of publicly available relevant data sets in the RA domain to train the machine learning models that can facilitate cognitive search systems for regulatory authorities. In this study, we aimed to use AI-based intelligent computational models to automatically recognize semantically similar question pairs in the RA domain and evaluate the Recognizing Question Entailment-based system. We used transfer learning techniques and experimented with transformer-based models pretrained on corpora collected from different resources, such as Bidirectional Encoder Representations from Transformers (BERT), Clinical BERT, BioBERT, and BlueBERT. We used a manually labeled data set that contained 150 question pairs in the pharmaceutical regulatory domain to evaluate the performance of our model. The Clinical BERT model performed better than other domain-specific BERT-based models in identifying question similarity from the RA domain. The BERT model had the best ability to learn domain-specific knowledge with transfer learning, which reached the best performance when fine-tuned with sufficient clinical domain question pairs. The top-performing model achieved an accuracy of 90.66% on the test set. This study demonstrates the possibility of using pretrained language models to recognize question similarity in the pharmaceutical regulatory domain. Transformer-based models that are pretrained on clinical notes perform better than models pretrained on biomedical text in recognizing the question's semantic similarity in this domain. We also discuss the challenges of using data augmentation techniques to address the lack of relevant data in this domain. The results of our experiment indicated that increasing the number of training samples using back translation and entity replacement did not enhance the model's performance. This lack of improvement may be attributed to the intricate and specialized nature of texts in the regulatory domain. Our work provides the foundation for further studies that apply state-of-the-art linguistic models to regulatory documents in the pharmaceutical industry.

An Optimized Intelligent Computational Security Model for Interconnected Blockchain-IoT System & Cities

Blockchain technology may provide a potential solution to the Internet of Things (IoT) security challenges by providing a decentralized and secure method for storing, managing, and sharing data. The Secure Hash Algorithm (SHA-256) hashed value of preliminary data (block) is retained in one block along with transaction data in tree form and timestamp in a chain of blocks. However, there are observations about blockchain limitations such as higher energy consumption, secure data, self-maintenance reliance, and higher cost. These constraints can be overcome by incorporating encryption algorithms into accepting blocks of data. In this paper, we propose a secure intelligent computational model for a large-scale interconnected IoT environment; an analytical modeling technique is considered for the proposed system. The proposed system takes advantage of the potential security feature of blockchain, which is considered the most appropriate secure communication system in an IoT. A computational model is built using the proposed blockchain technology to incorporate a secure and intelligent communication system. The proposed system uses the enhanced McEliece encryption approach’s potential to link the blockchain due to the faster mode of encryption and decryption process with a highly reduced number of steps.

Spiking neural P systems with long-term potentiation and depression

Spiking neural P systems (SNP systems) are parallel, distributed, low energy consuming, non-deterministic, interpretable and simple structured computational models abstracted from biological neural systems. In this work, to make SNP systems have the ability to adjust synaptic weights according to changes in the environment and boost SNP systems' learning ability and intelligence, long-term potentiation/depression mechanisms in the signal transmission of two neurons are abstracted from biological neural systems and introduced into SNP systems. As result, a type of novel SNP system, called SNP systems with long-term potentiation and depression (LTPD-SNP systems), is developed. Based on novel synaptic weight learning rules in LTPD-SNP systems, the weights of synapses change dynamically depending on the intensity and timing of the spikes passing through them and their own properties. The universality of LTPD-SNP systems as number generation and acceptance devices is proved. A uniform solution to SAT problem is designed to study the computational efficiency of the LTPD-SNP systems. Simultaneously, an anomaly detection method for AC engines based on LTPD-SNP systems is constructed to show how the system can solve a learning task. This work improves the learning ability and intelligence of SNP systems, and offers some ideas for creating brain-like intelligent computational models.

Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study

Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study

Deep intelligent predictive model for the identification of diabetes

<abstract> <p>Diabetes mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Many complications arise if diabetes remains untreated and unidentified. Early prediction of diabetes is the most high-quality way to forestall and manipulate diabetes and its complications. With the rising incidence of diabetes, machine learning and deep learning algorithms have been increasingly used to predict diabetes and its complications due to their capacity to care for massive and complicated facts sets. This research aims to develop an intelligent computational model that can accurately predict the probability of diabetes in patients at an early stage. The proposed predictor employs hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, an unsupervised principal component analysis (PCA) algorithm for discriminant feature selection, and a deep neural network (DNN) as a classifier. The experimental results show that the proposed technique can perform better on benchmark datasets. Furthermore, overall assessment performance compared to existing predictors indicated that our predictor outperformed the cutting-edge predictors using 10-fold cross validation. It is anticipated that the proposed model could be a beneficial tool for diabetes diagnosis and precision medicine.</p> </abstract>

Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges

We present a survey of machine learning works that attempt to organize the process flow of waste management in smart cities. Unlike past reviews, we focused on the waste generation and disposal phases in which citizens, households, and municipalities try to eliminate their solid waste by applying intelligent computational models. To this end, we synthesized and reviewed 42 articles published between 2010 and 2021. We retrieved the selected studies from six major academic research databases. Next, we deployed a comprehensive data extraction strategy focusing on the objectives of studies, trends of ML adoption, waste datasets, dependent and independent variables, and AI-ML-DL predictive models of waste generation. Our analysis revealed that most studies estimated waste material classification, amount of generated waste per area, and waste filling levels per location. Demographic data and images of waste type and fill levels are used as features to train the predictive models. Although various studies have widely deployed artificial neural networks (ANN) and convolutional neural networks (CNN) to classify waste, other techniques, such as gradient boosting regression tree (GBRT), have also been utilized. Critical challenges hindering the prediction of solid waste generation and disposal include the scarcity of real-time time series waste datasets, the lack of performance benchmarking tests of the proposed models, the reliability of the analytics models, and the long-term forecasting of waste generation. Our survey concludes with the implications and limitations of the selected models to inspire further research efforts.

A sustainable data-driven energy consumption assessment model for building infrastructures in resource constraint environment

As per economic development and urbanization, there is a touchy impact on energy consumption for commercial building infrastructures. Energy consumption analysis of commercial buildings plays an important role in building energy management because it helps to evaluate the energy efficiency of buildings. Therefore, a sustainable energy consumption approach is highly significant and effective. With advancements in technology, it is feasible to design and deploy an intelligent computational model that can efficiently perform in a limited resources-driven territory. Thus, a computationally optimized data-driven approach that utilizes advanced data analytics with minimum computing resources such as processing speed, memory consumption, and sampling rate can be helpful in sustainably estimating energy consumption. In our work, we applied different data pre-processing techniques to handle huge input data and standardized our model using z-score and min–max normalization techniques. The results show that the random forest regressor model performs optimum results for commercial building energy consumption. Our model training and validation latency is only 0.82 sec and 1.14 sec respectively. Predicted accuracy with data pre-processing followed by data standardization and optimization is 0.9375. Regarding the error rate, the recorded R square value, median absolute error, and mean absolute error were 0.82, 0.22, and 0.29 respectively. Therefore, the proposed computationally optimized data-driven model can be more productive in regulating energy consumption in urban building infrastructures with limited computing resources.

NLP-BCH-Ens: NLP-based intelligent computational model for discrimination of malaria parasite

Plasmodium falciparum causes malaria, which is an infectious and fatal disease. In early days, malaria-infected cells were diagnosed using a microscope. owing to a huge number of instances for analysis and intricacy of time, it may lead to false detection. Automated parasite detection technologies are in high demand due to increased time consumption and erroneous detection. To create effective cures and treatments, it is critical to use an accurate approach for predicting malaria parasite. Here, numerous protein sequences formulation techniques namely: discrete methods, Biochemical, physiochemical and Natural language processing techniques are applied for transformation of protein sequences in to numerical descriptors. Four classification algorithms are utilized and the anticipated results of these classifiers were then fused to establish ensemble classification model via simple majority and genetic algorithm. In addition, BCH error correction code is incorporated with support vector machine using all the feature spaces. The simulated results demonstrate the remarkable achievement of proposed compared to previous models. Thus, our proposed model may be an effective tool for discriminating the secretory and non-secretory proteins of malaria parasite.

An Effective Deep Learning-Based Architecture for Prediction of N7-Methylguanosine Sites in Health Systems

N7-methylguanosine (m7G) is one of the most important epigenetic modifications found in rRNA, mRNA, and tRNA, and performs a promising role in gene expression regulation. Owing to its significance, well-equipped traditional laboratory-based techniques have been performed for the identification of N7-methylguanosine (m7G). Consequently, these approaches were found to be time-consuming and cost-ineffective. To move on from these traditional approaches to predict N7-methylguanosine sites with high precision, the concept of artificial intelligence has been adopted. In this study, an intelligent computational model called N7-methylguanosine-Long short-term memory (m7G-LSTM) is introduced for the prediction of N7-methylguanosine sites. One-hot encoding and word2vec feature schemes are used to express the biological sequences while the LSTM and CNN algorithms have been employed for classification. The proposed “m7G-LSTM” model obtained an accuracy value of 95.95%, a specificity value of 95.94%, a sensitivity value of 95.97%, and Matthew’s correlation coefficient (MCC) value of 0.919. The proposed predictive m7G-LSTM model has significantly achieved better outcomes than previous models in terms of all evaluation parameters. The proposed m7G-LSTM computational system aims to support the drug industry and help researchers in the fields of bioinformatics to enhance innovation for the prediction of the behavior of N7-methylguanosine sites.

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

The concentrate process is the most sensitive in mineral processing plants (MPP), and the optimization of the process based on intelligent computational models (machine learning for recovery percentage modelling) can offer significant savings for the plant. Recent theoretical developments have revealed that many of the parameters commonly assumed as constants in gravity concentration modelling have a dynamic nature; however, there still lacks a universal way to model these factors accurately. This paper aims to understand the model effect of operational parameters of a jig (gravimetric concentrator) on the recovery percentage of the interest mineral (gold) through empirical modeling. The recovery percentage of mineral particles in a vibrated bed of big particles is studied by experimental data. The data used for the modelling were from experimental test in a pilot-scale jig supplemented by a two-month field sampling campaign for collecting 151 tests varying the most significant parameters (amplitude and frequency of pulsation, water flow, height of the artificial porous bed, and particle size). It is found the recovery percentage (%R) decreases with increasing pulsation amplitude (A) and frequency (F) when the size ratio of small to large particles (d/D) is smaller than 0.148. An empirical model was developed through machine learning techniques, specifically an artificial neural network (ANN) model was built and trained to predict the jig recovery percentage as a function of operation parameters and is then used to validate the recovery as a function of vibration conditions. The performance of the ANN model was compared with a new 65 experimental data of the recovery percentage. Results showed that the model (R2 = 0.9172 and RMSE = 0.105) was accurate and therefore could be efficiently applied to predict the recovery percentage in a jig device.

IAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model.

Tuberculosis (TB) is a worldwide illness caused by the bacteria Mycobacterium tuberculosis. Owing to the high prevalence of multidrug-resistant tuberculosis, numerous traditional strategies for developing novel alternative therapies have been presented. The effectiveness and dependability of these procedures are not always consistent. Peptide-based therapy has recently been regarded as a preferable alternative due to its excellent selectivity in targeting specific cells without affecting the normal cells. However, due to the rapid growth of the peptide samples, predicting TB accurately has become a challenging task. To effectively identify antitubercular peptides, an intelligent and reliable prediction model is indispensable. An ensemble learning approach was used in this study to improve expected results by compensating for the shortcomings of individual classification algorithms. Initially, three distinct representation approaches were used to formulate the training samples: k-space amino acid composition, composite physiochemical properties, and one-hot encoding. The feature vectors of the applied feature extraction methods are then combined to generate a heterogeneous vector. Finally, utilizing individual and heterogeneous vectors, five distinct nature classification models were used to evaluate prediction rates. In addition, a genetic algorithm-based ensemble model was used to improve the suggested model's prediction and training capabilities. Using Training and independent datasets, the proposed ensemble model achieved an accuracy of 94.47% and 92.68%, respectively. It was observed that our proposed "iAtbP-Hyb-EnC" model outperformed and reported ~10% highest training accuracy than existing predictors. The "iAtbP-Hyb-EnC" model is suggested to be a reliable tool for scientists and might play a valuable role in academic research and drug discovery. The source code and all datasets are publicly available at https://github.com/Farman335/iAtbP-Hyb-EnC.

Marie: Validation of the Artificial Intelligence Model for Covid-19 Detection

Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. In partnership with the municipality of Itapeva, Minas Gerais, we carried out patient analysis and, at the same time, we evolved the algorithms to meet the needs of health professionals and also expand support in tracking COVID-19 in the municipality. In this project we will describe cases and even signs and symptoms that were similar to the clinical performed by the doctor. The average recognition accuracy of COVID-19 was 0.97,4 ± 0.043.

Regularization based discriminative feature pattern selection for the classification of Parkinson cases using machine learning

Abstract Objectives This paper focuses on developing a regularization-based feature selection approach to select the most effective attributes from the Parkinson’s speech dataset. Parkinson’s disease is a medical condition that progresses as the dopamine-producing nerve cells are affected. Early diagnosis often reduces the effect on the individuals, minimizes the advancement over time. In recent times, intelligent computational models are used in many complex cases to diagnose a clinical condition with high precision. These models are intended to find meaningful representation from the data to diagnose the disease. Machine learning acts as a tool, gears up the model learning process through a mathematical baseline. But, not in all cases, machine learning will be demanded to perform optimally. It comes with a few constraints, mainly the representation of the data. The learning models expect a clean, noise-free input, which in-turns produces better discriminative patterns over different categories of classes. Methods The proposed model identified five candidate features as predictors. This feature subset is trained with different varieties of supervised classifiers to trace out the best-performing model. Results The results are validated through accuracy, precision, recall, and receiver’s operational characteristic curves. The proposed regularization- based feature selection model outperformed the benchmark algorithms by attaining 100% accuracy on most of the classifiers, other than linear discriminant analysis (99.90%) and naïve Bayes (99.51%). Conclusions This paper exhibits the need for intelligent models to analyze complex data patterns to assist medical practitioners in better disease diagnosis. The results exhibit that the regularization methods find the best features based on their importance score, which improved the model performance over other feature selection methods.

Intelligent ubiquitous computing model for energy optimization of cloud IOTs in sensor networks

PurposeLimitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization. Ubiquitous machine learning computational model process performs training in a better way than regular supervised learning or unsupervised learning computational models with deep learning techniques, resulting in better learning and optimization for the considered problem domain of cloud-based internet-of-things (IOTs). This study aims to improve the network quality and improve the data accuracy rate during the network transmission process using the developed ubiquitous deep learning computational model.Design/methodology/approachIn this research study, a novel intelligent ubiquitous machine learning computational model is designed and modelled to maintain the optimal energy level of cloud IOTs in sensor network domains. A new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization is developed. A new unified deterministic sine-cosine algorithm has been developed in this study for parameter optimization of weight factors in the ubiquitous machine learning model.FindingsThe newly developed ubiquitous model is used for finding network energy and performing its optimization in the considered sensor network model. At the time of progressive simulation, residual energy, network overhead, end-to-end delay, network lifetime and a number of live nodes are evaluated. It is elucidated from the results attained, that the ubiquitous deep learning model resulted in better metrics based on its appropriate cluster selection and minimized route selection mechanism.Research limitations/implicationsIn this research study, a novel ubiquitous computing model derived from a new optimization algorithm called a unified deterministic sine-cosine algorithm and deep learning technique was derived and applied for maintaining the optimal energy level of cloud IOTs in sensor networks. The deterministic levy flight concept is applied for developing the new optimization technique and this tends to determine the parametric weight values for the deep learning model. The ubiquitous deep learning model is designed with auto-encoders and decoders and their corresponding layers weights are determined for optimal values with the optimization algorithm. The modelled ubiquitous deep learning approach was applied in this study to determine the network energy consumption rate and thereby optimize the energy level by increasing the lifetime of the sensor network model considered. For all the considered network metrics, the ubiquitous computing model has proved to be effective and versatile than previous approaches from early research studies.Practical implicationsThe developed ubiquitous computing model with deep learning techniques can be applied for any type of cloud-assisted IOTs in respect of wireless sensor networks, ad hoc networks, radio access technology networks, heterogeneous networks, etc. Practically, the developed model facilitates computing the optimal energy level of the cloud IOTs for any considered network models and this helps in maintaining a better network lifetime and reducing the end-to-end delay of the networks.Social implicationsThe social implication of the proposed research study is that it helps in reducing energy consumption and increases the network lifetime of the cloud IOT based sensor network models. This approach helps the people in large to have a better transmission rate with minimized energy consumption and also reduces the delay in transmission.Originality/valueIn this research study, the network optimization of cloud-assisted IOTs of sensor network models is modelled and analysed using machine learning models as a kind of ubiquitous computing system. Ubiquitous computing models with machine learning techniques develop intelligent systems and enhances the users to make better and faster decisions. In the communication domain, the use of predictive and optimization models created with machine learning accelerates new ways to determine solutions to problems. Considering the importance of learning techniques, the ubiquitous computing model is designed based on a deep learning strategy and the learning mechanism adapts itself to attain a better network optimization model.

Intelligent and robust computational prediction model for DNA N4-methylcytosine sites via natural language processing

DNA N4-methylcytosine (4 ​mC) is an essential epigenetic modification and performs crucial roles in restriction-modification systems. The 4 ​mC involves many essential cellular processes, namely: correcting DNA replication and controlling DNA replication errors in the prokaryotic organism. In order to understand their biological functional mechanisms, the prediction of 4 ​mC modification is indispensable. Although computationally, it was targeted but the desired outcomes were not obtained. Thus, the development of an intelligent computational prediction system that truly expresses 4 ​mC modification sites is imperative. An efficient and high throughput discriminative intelligent computational system called “iDNA-4mC-DL” is introduced using the natural language processing method “word2vec” along with a convolution neural network. The obtained outcomes authenticated that the proposed iDNA-4mC-DL system performs outstandingly on six publicly available benchmark and independent datasets compared to current tools. It is, thus, highly estimated that the proposed model might be a more supportive and applied tool for rudimentary research and academia.

Creative Partnerships with Technology: How Creativity Is Enhanced Through Interactions with Generative Computational Systems

This paper discusses emerging creative practices that involve interacting with generative computational systems, and the effect of such cybernetic interactions on our conceptions of creativity and agency. As computing systems have become more powerful in recent years, real time interaction with intelligent computational processes and models has emerged as a basis for innovative creative practices. Examples of these practices include interactive digital media installations, generative art works, live coding performances, virtual theatre, interactive cinema, and adaptive processes in computer games. In these types of activities computational systems have assumed a significant level of agency, or autonomy, that provoke questions about shared authorship and originality that are redefining our relationship with technologies and prompting new questions about human capabilities, values and the meaning of productive activities.

WITHDRAWN: Consumer behaviour analysis for business development

Abstract Consumer behaviour in the groups or organizations used to select and secure services, products, experiences, or innovations and the developments for meeting their needs and their influences on the purchaser and the society. Knowledge of consumers' conditions, factors, and behavioural reasons ensure competitiveness in entrepreneurship. In this paper, the Adaptive Hybridized Intelligent Computational model (AHICM) has developed to analyze consumer behaviour for business development. New products, new beliefs, and psychology for society require a thorough analysis of segments of the market and consumer requirements. Later, in customer behaviour analyzing, companies must implement a reliable, effective, and flexible marketing strategy that guarantees profits and sales. It has attained by the strategic planning and the objective market segment objective based on the computational model. The study results are presented by segmenting respondents by the sustainability factor, determining the impact of sustainability values upon consumers' conduct, and identifying barriers to the transformation of values in action.