Search results for: neural activation

Authors: S. Neelima, P. S. Subramanyam

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The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

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Authors: Chaoqun Tan, Naiyun Gao, Xiaoyan Xin

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Perxymonosulfate (PMS)-based oxidation processes, as an alternative of hydrogen peroxide-based oxidation processes, are more and more popular because of reactive radical species (SO4-•, OH•) produced in systems. Magnetic nano-scaled particles Fe3O4 and ferrous anion (Fe2+) were studied for the activation of PMS for degradation of acetaminophen (APAP) in water. The Fe3O4 MNPs were found to effectively catalyze PMS for APAP and the reactions well followed a pseudo-first-order kinetics pattern (R2>0.95). While the degradation of APAP in PMS-Fe2+ system proceeds through two stages: a fast stage and a much slower stage. Within 5 min, approximately 7% and 18% of 10 ppm APAP was accomplished by 0.2 mM PMS in Fe3O4 (0.8g/L) and Fe2+ (0.1mM) activation process. However, as reaction proceed to 120 min, approximately 75% and 35% of APAP was removed in Fe3O4 activation process and Fe2+ activation process, respectively. Within 120 min, the mineralization of APAP was about 7.5% and 5.0% (initial APAP of 10 ppm and [PMS]0 of 0.2 mM) in Fe3O4-PMS and Fe2+-PMS system, while the mineralization could be greatly increased to about 31% and 40% as [PMS]0 increased to 2.0 mM in in Fe3O4-PMS and Fe2+-PMS system, respectively. At last, the production of reactive radical species were validated directly from Electron Paramagnetic Resonance (ESR) tests with 0.1 M 5,5-Dimethyl-1-pyrrolidine N-oxide (DMPO). Plausible mechanisms on the radical generation from Fe3O4 and Fe2+ activation of PMS are proposed on the results of radial identification tests. The results demonstrated that Fe3O4 MNPs activated PMS and Fe2+ anion activated PMS systems are promising technologies for water pollution caused by contaminants such as pharmaceutical. Fe3O4-PMS system is more suitable for slowly remediation, while Fe2+-PMS system is more suitable for fast remediation.

Keywords: acetaminophen, peroxymonosulfate, radicals, Electron Paramagnetic Resonance (ESR)

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Authors: Han Bing, Yin Zhenjie

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In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: fault prediction, neural network, GM(1, 5) genetic algorithm, GBPGA

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Authors: Norazlin Mohamad, Saiful Adli Bukry, Zarina Zahari, Haidzir Manaf, Hanafi Sawalludin

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Background: Deficit in neuromuscular recruitment and decrease force distribution were the common problems among ankle instability patients due to altered joint kinematics that lead to recurrent ankle injuries. Rigid Tape and KT Tape had widely been used as therapeutic and performance enhancement tools in ankle stability. However the difference effect between this two tapes is still controversial. Objective: To investigate the different effect between Rigid Tape and KT Tape on force distribution and muscle activation among ankle instability patients while standing. Study design: Crossover trial. Participants: 27 patients, age between 18 to 30 years old participated in this study. All the subjects were applied with KT Tape & Rigid Tape on their affected ankle with 3 days of interval for each intervention. The subjects were tested with their barefoot (without tape) first to act as a baseline before proceeding with KT Tape, and then with Rigid Tape. Result: There were no significant difference on force distribution at forefoot and back-foot for both tapes while standing. However the mean data shows that Rigid Tape has the highest force distribution at back-foot rather than forefoot when compared with KT Tape that had more force distribution at forefoot while standing. Regarding muscle activation (Peroneus Longus), results showed significant difference between Rigid Tape and KT Tape (p= 0.048). However, there was no significant difference on Tibialis Anterior muscle activation between both tapes while standing. Conclusion: The results indicated that Peroneus longus muscle was more active when applied Rigid Tape rather than KT Tape in ankle instability patients while standing.

Keywords: ankle instability, kinematic, muscle activation, force distribution, Rigid Tape, KT tape

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Authors: Jie Zhao, Meng Su

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Image recognition, as one of the most critical technologies in computer vision, works to help machine-like robotics understand a scene, that is, if deployed appropriately, will trigger the revolution in remote sensing and industry automation. With the developments of AI technologies, there are many prevailing and sophisticated neural networks as technologies developed for image recognition. However, computer vision platforms as hardware, supporting neural networks for image recognition, as crucial as the neural network technologies, need to be more congruently addressed as the research subjects. In contrast, different computer vision platforms are deterministic to leverage the performance of different neural networks for recognition. In this paper, three different computer vision platforms – Jetson Nano(with 4GB), a standalone laptop(with RTX 3000s, using CUDA), and Google Colab (web-based, using GPU) are explored and four prominent neural network architectures (including AlexNet, VGG(16/19), GoogleNet, and ResNet(18/34/50)), are investigated. In the context of pairwise usage between different computer vision platforms and distinctive neural networks, with the merits of recognition accuracy and time efficiency, the performances are evaluated. In the case study using public imageNets, our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints.

Keywords: alexNet, VGG, googleNet, resNet, Jetson nano, CUDA, COCO-NET, cifar10, imageNet large scale visual recognition challenge (ILSVRC), google colab

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Authors: Abdolreza Memari

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In this article in order to estimate the viscosity of crude oil,a numerical method has been used. We use this method to measure the crude oil's viscosity for 3 states: Saturated oil's viscosity, viscosity above the bubble point and viscosity under the saturation pressure. Then the crude oil's viscosity is estimated by using KHAN model and roller ball method. After that using these data that include efficient conditions in measuring viscosity, the estimated viscosity by the presented method, a radial based neural method, is taught. This network is a kind of two layered artificial neural network that its stimulation function of hidden layer is Gaussian function and teaching algorithms are used to teach them. After teaching radial based neural network, results of experimental method and artificial intelligence are compared all together. Teaching this network, we are able to estimate crude oil's viscosity without using KHAN model and experimental conditions and under any other condition with acceptable accuracy. Results show that radial neural network has high capability of estimating crude oil saving in time and cost is another advantage of this investigation.

Keywords: viscosity, Iranian crude oil, radial based, neural network, roller ball method, KHAN model

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Faruk Ortes, Derya Karabulut, Yunus Ziya Arslan

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Myoelectric features gathering from musculature environment are considered on a preferential basis to perceive muscle activation and control human arm prostheses according to recent experimental researches. EMG (electromyography) signal based human arm prostheses have shown a promising performance in terms of providing basic functional requirements of motions for the amputated people in recent years. However, these assistive devices for neurorehabilitation still have important limitations in enabling amputated people to perform rather sophisticated or functional movements. Surface electromyogram (EMG) is used as the control signal to command such devices. This kind of control consists of activating a motion in prosthetic arm using muscle activation for the same particular motion. Extraction of clear and certain neural information from EMG signals plays a major role especially in fine control of hand prosthesis movements. Many signal processing methods have been utilized for feature extraction from EMG signals. The specific objective of this study was to compare widely used time domain features of EMG signal including integrated EMG(IEMG), root mean square (RMS) and waveform length(WL) for prediction of externally applied forces to human hands. Obtained features were classified using artificial neural networks (ANN) to predict the forces. EMG signals supplied to process were recorded during only type of muscle contraction which is isometric and isotonic one. Experiments were performed by three healthy subjects who are right-handed and in a range of 25-35 year-old aging. EMG signals were collected from muscles of the proximal part of the upper body consisting of: biceps brachii, triceps brachii, pectorialis major and trapezius. The force prediction results obtained from the ANN were statistically analyzed and merits and pitfalls of the extracted features were discussed with detail. The obtained results are anticipated to contribute classification process of EMG signal and motion control of powered human arm prosthetics control.

Keywords: assistive devices for neurorehabilitation, electromyography, feature extraction, force estimation, human arm prosthesis

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Authors: Oluwaponmile David Alao

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Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

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Authors: S. N. Turkmen, A. S. Kipcak, N. Tugrul, E. M. Derun, S. Piskin

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Activated carbon is an amorphous carbon chain which has extremely extended surface area. High surface area of activated carbon is due to the porous structure. Activated carbon, using a variety of materials such as coal and cellulosic materials; can be obtained by both physical and chemical methods. The prepared activated carbon can be used for decolorize, deodorize and also can be used for removal of organic and non-organic pollution. In this study, pomegranate peel was subjected to 800W microwave power for 1 to 4 minutes. Also fresh pomegranate peel was used for the reference material. Then ZnCl2 was used for the chemical activation purpose. After the activation process, activated pomegranate peels were used for the adsorption of Zn metal (40 ppm) in the waste water. As a result of the adsorption experiments, removal of heavy metals ranged from 89% to 85%.

Keywords: activated carbon, adsorption, chemical activation, microwave, pomegranate peel

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Authors: Ghorab Sara, Meziani Lila, Rubin Harvey Stuart

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The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method.

Keywords: model compression, deep neural network, pruning, knowledge distillation, quantization, low-rank decomposition

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Authors: Vinai K. Singh

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In Neural Network-based Learning techniques, there are several models of Convolutional Networks. Whenever the methods are deployed with large datasets, only then can their applicability and appropriateness be determined. Clinical and pathological pictures of lobular carcinoma are thought to exhibit a large number of random formations and textures. Working with such pictures is a difficult problem in machine learning. Focusing on wet laboratories and following the outcomes, numerous studies have been published with fresh commentaries in the investigation. In this research, we provide a framework that can operate effectively on raw photos of various resolutions while easing the issues caused by the existence of patterns and texturing. The suggested approach produces very good findings that may be used to make decisions in the diagnosis of cancer.

Keywords: lobular carcinoma, convolutional neural networks (CNN), deep learning, histopathological imagery scans

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Authors: Xiaohan Bookman, Xiaoyan Zhu

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The outputs of non-linear feed-forward neural network are positive, which could be treated as probability when they are normalized to one. If we take Entropy-Based Principle into consideration, the outputs for each sample could be represented as the distribution of this sample for different clusters. Entropy-Based Principle is the principle with which we could estimate the unknown distribution under some limited conditions. As this paper defines two processes in Feed-Forward Neural Network, our limited condition is the abstracted features of samples which are worked out in the abstraction process. And the final outputs are the probability distribution for different clusters in the clustering process. As Entropy-Based Principle is considered into the feed-forward neural network, a clustering method is born. We have conducted some experiments on six open UCI data sets, comparing with a few baselines and applied purity as the measurement. The results illustrate that our method outperforms all the other baselines that are most popular clustering methods.

Keywords: feed-forward neural network, clustering, max-entropy principle, probabilistic models

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Authors: Sadiq J. Abou-Loukh

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The present work deals with implementation of Petri nets, which own the perfect ability of modeling, are used to establish a fault diagnosis model. Fault diagnosis of a control system received considerable attention in the last decades. The formalism of representing neural networks based on Petri nets has been presented. Neural Petri Net (NPN) reasoning model is investigated and developed for the fault diagnosis process of electric control system. The proposed NPN has the characteristics of easy establishment and high efficiency, and fault status within the system can be described clearly when compared with traditional testing methods. The proposed system is tested and the simulation results are given. The implementation explains the advantages of using NPN method and can be used as a guide for different online applications.

Keywords: petri net, neural petri net, electric control system, fault diagnosis

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Authors: Firas M. Tuaimah

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This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: short-term load forecasting, artificial neural networks, back propagation learning, hourly load demand

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Authors: Rehab Abdulmajed, Amr Hamada, Ahmed Elsaid, Hisashi Hayakawa, Ayman Mahrous

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Solar emissions have a high impact on the Earth’s magnetic field, and the prediction of solar events is of high interest. Various techniques have been used in the prediction of solar wind using mathematical models, MHD models, and neural network (NN) models. This study investigates the coronal hole (CH) derived high-speed streams (HSSs) and their correlation to the CH area and create a neural network model to predict the HSSs. Two different algorithms were used to compare different models to find a model that best simulates the HSSs. A dataset of CH synoptic maps through Carrington rotations 1601 to 2185 along with Omni-data set solar wind speed averaged over the Carrington rotations is used, which covers Solar cycles (sc) 21, 22, 23, and most of 24.

Keywords: artificial neural network, coronal hole area, feed-forward neural network models, solar high speed streams

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Authors: Dung V. H. Pham, Kathryn Cullen

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Introduction: 40% of adolescents with major depression (MDD) are unresponsive to 1st line antidepressant treatment. The neural mechanism underlying treatment-responsive and treatment-resistant depression in adolescent are unclear. Amygdala is important for emotion processing and has been implicated in mood disorders. Past research has shown abnormal amygdala connectivity in adolescents with MDD. This research study changes in amygdala resting-state functional connectivity to find neural correlates of successful antidepressant treatment. Methods: Thirteen adolescents aged 12-19 underwent rfMRI before and after 8-week antidepressant treatment and completed BDI-II at each scan. A whole-brain approach, using anatomically defined amygdala ROIs (1) identified brain regions that are highly synchronous with the amygdala, (2) correlated neural changes with changes in overall depression and specific symptom clusters within depression. Results: Some neural correlates were common across domains: (1) decreased amygdala RSFC with the default mode network (posterior cingulate, precuneus) is associated with improvement in overall depression and many symptom clusters, (2) increased amygdala RSFC with fusiform gyrus is associated with symptom improvement across many symptom clusters. We also found unique neural changes associated with symptom improvement in each symptom cluster. Conclusion: This is the first preliminary study that looks at neural correlates of antidepressant treatment response to overall depression as well as different clusters of symptoms of depression. The finding suggests both overlapping and distinct neural mechanisms underlying improvement in each symptom clusters within depression. Some brain regions found are also implicated in MDD among adults in previous literature.

Keywords: depression, adolescents, fMRI, antidepressants

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Authors: Sangil Han, In Su Kim

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Transition-metal-catalyzed C–H bond activation and its subsequent functionalization has been one of the most attractive topics in organic synthesis because of its remarkable potential for atom economy and environmental sustainability. In this addition, a variety of C(sp2)–H functionalization has been developed under metal catalysis in the past decade. Recently, much attention has been moved towards the C(sp3)–H functionalization events, which continue to be a challenging issue. In this area, directing group assisted sp3 C–H functionalization has been explored by use of amides, carboxylic acids, oximes, N-heterocycles, and etc. In particular, 8-methylquinolines have been found as good substrates for sp3 C–H functionalization due to its ability to form cyclometalated complexes. Succinimides have been recognized as privileged structural cores found in a number of bioactive natural products, pharmaceuticals, and functional materials. Furthermore, the reduced derivatives such as pyrrolidines and γ-lactams have been also found in a large number of pharmaceutical relevant molecules, thus making them one of the most important and promising compounds. We herein describe the first C(sp3)–H activation of 8-methylquinolines and subsequent functionalization with maleimides to afford various succinimide derivatives.

Keywords: C(sp3)–H activation, 8-methylquinolines, maleimides, succinimides

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Authors: Beata Spilakova, Daniel J. Shaw, Radek Marecek, Milan Brazdil

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Hyperscanning affords a unique insight into the brain dynamics underlying human interaction by simultaneously scanning two or more individuals’ brain responses while they engage in dyadic exchange. This provides an opportunity to observe dynamic brain activations in all individuals participating in interaction, and possible interbrain effects among them. The present research aims to provide an experimental paradigm for hyperscanning research capable of delineating among different forms of interaction. Specifically, the goal was to distinguish between two dimensions: (1) interaction structure (concurrent vs. turn-based) and (2) goal structure (competition vs cooperation). Dual-fMRI was used to scan 22 pairs of participants - each pair matched on gender, age, education and handedness - as they played the Pattern Game. In this simple interactive task, one player attempts to recreate a pattern of tokens while the second player must either help (cooperation) or prevent the first achieving the pattern (competition). Each pair played the game iteratively, alternating their roles every round. The game was played in two consecutive sessions: first the players took sequential turns (turn-based), but in the second session they placed their tokens concurrently (concurrent). Conventional general linear model (GLM) analyses revealed activations throughout a diffuse collection of brain regions: The cooperative condition engaged medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC); in the competitive condition, significant activations were observed in frontal and prefrontal areas, insula cortices and the thalamus. Comparisons between the turn-based and concurrent conditions revealed greater precuneus engagement in the former. Interestingly, mPFC, PCC and insulae are linked repeatedly to social cognitive processes. Similarly, the thalamus is often associated with a cognitive empathy, thus its activation may reflect the need to predict the opponent’s upcoming moves. Frontal and prefrontal activation most likely represent the higher attentional and executive demands of the concurrent condition, whereby subjects must simultaneously observe their co-player and place his own tokens accordingly. The activation of precuneus in the turn-based condition may be linked to self-other distinction processes. Finally, by performing intra-pair correlations of brain responses we demonstrate condition-specific patterns of brain-to-brain coupling in mPFC and PCC. Moreover, the degree of synchronicity in these neural signals related to performance on the game. The present results, then, show that different types of interaction recruit different brain systems implicated in social cognition, and the degree of inter-player synchrony within these brain systems is related to nature of the social interaction.

Keywords: brain-to-brain coupling, hyperscanning, pattern game, social interaction

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Authors: Nurul Bin Ibrahim

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Advancements in neural networks have offered valuable insights into Fluid Dynamics, notably in addressing turbulence-related challenges. In this research, we introduce multiple applications of models of neural networks, namely Feed-Forward and Recurrent Neural Networks, to explore the relationship between jet formations and stratified turbulence within stochastically excited Boussinesq systems. Using machine learning tools like TensorFlow and PyTorch, the study has created models that effectively mimic and show the underlying features of the complex patterns of jet formation and stratified turbulence. These models do more than just help us understand these patterns; they also offer a faster way to solve problems in stochastic systems, improving upon traditional numerical techniques to solve stochastic differential equations such as the Euler-Maruyama method. In addition, the research includes a thorough comparison with the Statistical State Dynamics (SSD) approach, which is a well-established method for studying chaotic systems. This comparison helps evaluate how well neural networks can help us understand the complex relationship between jet formations and stratified turbulence. The results of this study underscore the potential of neural networks in computational physics and fluid dynamics, opening up new possibilities for more efficient and accurate simulations in these fields.

Keywords: neural networks, machine learning, computational fluid dynamics, stochastic systems, simulation, stratified turbulence

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Authors: Hussam Elias, Ninovic Perez, Holger Hirsch

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The completion of the EMC regulations worldwide is growing steadily as the usage of electronics in our daily lives is increasing more than ever. In this paper, we introduce a novel method to perform the final phase of Electromagnetic compatibility (EMC) measurement and to reduce the required test time according to the norm EN 55032 by using a developed tool and the conventional neural network(CNN). The neural network was trained using real EMC measurements, which were performed in the Semi Anechoic Chamber (SAC) by CETECOM GmbH in Essen, Germany. To implement our proposed method, we wrote software to perform the radiated electromagnetic interference (EMI) measurements and use the CNN to predict and determine the position of the turntable that meets the maximum radiation value.

Keywords: conventional neural network, electromagnetic compatibility measurement, mean absolute error, position error

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Authors: Austin Ikechukwu Gbasouzor, Sam Nna Omenyi, Sabuj Malli

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This study is an extension of the previous work done with ARS-680 Environmental Chamber. Drying is a complex operation that demands much energy and time. Drying is essentially important for preservation of ginger rhizome. Drying of ginger was modeled, and then the effective diffusion coefficient and activation energy where determined. For this purpose, the experiments were done at six levels of varied temperature ranging from (10, 20, 30, 40, 50, 60°C). The average effective diffusion coefficient for their studies samples for temperature range of 40°C to 70°C was 4.48 x10-10m²/s, 4.96 x10-10m²/s, and 5.31 x10-10m²/s for 0.8, 1.5 and 3m/s drying air velocity respectively. These values closely agreed with the values of effective diffusion coefficients obtained in these studies for the variously treated ginger rhizomes and test conducted.

Keywords: activation energy, diffusion coefficients, drying model, drying time, ginger rhizomes, moisture ratio, thin layer

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Authors: Nusrat Masood, Vijaya Dubey, Suaib Luqman

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Caspase 3, a member of cysteine-aspartic acid protease family, is an imperative indicator for cell death particularly when substantiating apoptosis. Thus, caspase 3 is an interesting target for the discovery and development of anticancer agent. We adopted a four level assessment of both terpenoids and flavonoids and thus experimentally performed the enzymatic assay in cell free system as well as in cancer cell line which was validated through real time expression and molecular interaction studies. A significant difference was observed with both the class of natural products indicating terpenoids as better activators of caspase 3 compared to flavonoids both in the cell free system as well as in cell lines. The expression analysis, activation constant and binding energy also correlate well with the enzyme activity. Overall, terpenoids had an unswerving effect on caspase 3 in all the tested system while flavonoids indirectly affect enzyme activity.

Keywords: Caspase 3, terpenoids, flavonoids, activation constant, binding energy

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Authors: Rajeshwar S. Matche, Subhash V. Pawde, Suraj P, Sachin R. Chaudhari

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Time Temperature Indicator’s (TTI) are trending approach in a food packaging that will be insightful to have safe and hygienic food products. Currently, available TTI in the market are mostly a product specific and sometime even difficult to handle especially in supply chain as these are pre-activated and require specific storage conditions. In the present study, research focus is on the development of a cost-effective lactic acid based TTI that can work over a wide range of temperature and can be activated at time of packaging or on demand. The correlation between activation energies of colour change of the developed indicator and packed pasteurized milk spoilage with respect to time and temperature was established. Developed lactic acid based TTI strips have range of activation energy from 10.13 to 24.20 KJ/mol. We found that the developed TTI strip’s with activation energy 12.42, and 14.41KJ/mol can be correlated with spoilage activation energy of packed pasteurized milk which was 25.71 KJ/mol with factor of 2 at storage temperature 4°C. The implementation of these TTI on packed pasteurized milk allow us see visual colour change during the storage and can be fruitful to monitoring quality of the milk and understand its freshness especially in a cold supply chain, viz distributor and road vendor etc.

Keywords: pasteurised packed milk, time temperature indicator, spoilage, freshness

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Authors: Neeraj Kumar Mishra, In Su Kim

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The isoindoline, indazole and indole heterocycles are ubiquitous structural motif found in heterocyclic compounds as they exhibit biological and medicinal applications. For example, isoindoline motif is present in molecules that act as endothelin-A receptor antagonists and dipeptidyl peptidase inhibitors. Moreover, isoindoline derivatives are very crucial constituents in the field of materials science as attractive candidates for organic light-emitting devices. However, compounds containing the indazole motif are known to exhibit to a variety of biological activities, such as estrogen receptor, HIV protease inhibition and anti-tumor activity. The prevalence of indazoles and indoles has led to the development of many useful methods for their preparation. Thus, isoindoline, indazole and indole heterocycles can be new candidates for the next generation of pharmaceuticals. Therefore, the development of highly efficient strategies for the formation of these heterocyclic architectures is an area of great interest in organic synthesis. The past years, transition-metal-catalyzed C−H activation followed by annulation reaction has been frequently used as a powerful tool to construct various heterocycles. Herein, we describe our recent achievements about the transition-metal-catalyzed tandem cyclization reactions of N-benzyltriflamides, 1,2-disubstituted arylhydrazines, acetanilides, etc. via C−H bond activation to access the corresponding bioactive heterocylic scaffolds.

Keywords: biologically active, C-H activation, heterocyclic compounds, transition-metal catalysts

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Authors: Selim M. Khan

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Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

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Authors: Seyed-Ali Sadegh-Zadeh, Mahboobe Bahrami, Sahar Ahmadi, Seyed-Yaser Mousavi, Hamed Atashbar, Amir M. Hajiyavand

Abstract:

This paper presents an investigation into the concept of neural reshaping, which is crucial for achieving strong artificial intelligence through the development of AI algorithms with very high plasticity. By examining the plasticity of both human and artificial neural networks, the study uncovers groundbreaking insights into how these systems adapt to new experiences and situations, ultimately highlighting the potential for creating advanced AI systems that closely mimic human intelligence. The uniqueness of this paper lies in its comprehensive analysis of the neural reshaping process in both human and artificial intelligence systems. This comparative approach enables a deeper understanding of the fundamental principles of neural plasticity, thus shedding light on the limitations and untapped potential of both human and AI learning capabilities. By emphasizing the importance of neural reshaping in the quest for strong AI, the study underscores the need for developing AI algorithms with exceptional adaptability and plasticity. The paper's findings have significant implications for the future of AI research and development. By identifying the core principles of neural reshaping, this research can guide the design of next-generation AI technologies that can enhance human and artificial intelligence alike. These advancements will be instrumental in creating a new era of AI systems with unparalleled capabilities, paving the way for improved decision-making, problem-solving, and overall cognitive performance. In conclusion, this paper makes a substantial contribution by investigating the concept of neural reshaping and its importance for achieving strong AI. Through its in-depth exploration of neural plasticity in both human and artificial neural networks, the study unveils vital insights that can inform the development of innovative AI technologies with high adaptability and potential for enhancing human and AI capabilities alike.

Keywords: neural plasticity, brain adaptation, artificial intelligence, learning, cognitive reshaping

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Authors: Rami El-Hajj Mohamad, Mahmoud Skafi, Ali Massoud Haidar

Abstract:

Several meteorological parameters were used for the prediction of monthly average daily global solar radiation on horizontal using recurrent neural networks (RNNs). Climatological data and measures, mainly air temperature, humidity, sunshine duration, and wind speed between 1995 and 2007 were used to design and validate a feed forward and recurrent neural network based prediction systems. In this paper we present our reference system based on a feed-forward multilayer perceptron (MLP) as well as the proposed approach based on an RNN model. The obtained results were promising and comparable to those obtained by other existing empirical and neural models. The experimental results showed the advantage of RNNs over simple MLPs when we deal with time series solar radiation predictions based on daily climatological data.

Keywords: recurrent neural networks, global solar radiation, multi-layer perceptron, gradient, root mean square error

Procedia PDF Downloads 414

Authors: K. D. Sharma, M. Parida, S. S. Jain, Anju Saini, V. K. Katiyar

Abstract:

Air quality models form one of the most important components of an urban air quality management plan. Various statistical modeling techniques (regression, multiple regression and time series analysis) have been used to predict air pollution concentrations in the urban environment. These models calculate pollution concentrations due to observed traffic, meteorological and pollution data after an appropriate relationship has been obtained empirically between these parameters. Artificial neural network (ANN) is increasingly used as an alternative tool for modeling the pollutants from vehicular traffic particularly in urban areas. In the present paper, an attempt has been made to model traffic air pollution, specifically CO concentration using neural networks. In case of CO concentration, two scenarios were considered. First, with only classified traffic volume input and the second with both classified traffic volume and meteorological variables. The results showed that CO concentration can be predicted with good accuracy using artificial neural network (ANN).

Keywords: air quality management, artificial neural network, meteorological variables, statistical modeling

Procedia PDF Downloads 491