Neural Networks and Artificial Intelligence

Artificial Neural Networks (ANNs) represent a category of machine learning models that draw inspiration from the arrangement and operation of the human brain. These models consist of interconnected units or neurons, which are grouped into layers. ANNs find extensive application in computer-related tasks, encompassing activities like identifying patterns, categorizing information, predicting outcomes, and beyond. Within the domain of artificial intelligence and machine learning, Artificial Neural Networks (ANNs) have surfaced as a crucial and transformative technology. Within this abstract, we delve into a range of computer applications utilizing Artificial Neural Networks (ANNs), with each application strategically designed to leverage the capabilities of neural networks for various objectives. The applications discussed include NeuroNetApp, DeepLearnPro, SmartANN Master, AIBrainWave, and CogniTech Learn. The accompanying table presents distinct metrics related to each application, namely "SI Plus," "SI Negative," and "Ci." Although the precise definitions of these metrics are not revealed, they presumably offer insights into the performance, effectiveness, or pertinent characteristics of the applications.

Quantum mechanics has revolutionized our understanding of reality, yet its potential role in neural processes and cognition remains largely unexplored. This paper introduces the emerging field of Quantum Neuroscience, which investigates the possibility of quantum effects in neural networks-both biological and artificial. By integrating insights from neuroscience, quantum physics, and artificial intelligence, this research aims to uncover new mechanisms underlying cognition, decision-making, and consciousness. Could quantum superposition, entanglement, or coherence influence neural activity? Could quantum-inspired neural networks enhance AI systems? This study outlines key research directions and proposes experimental approaches to bridge the gap between quantum mechanics and intelligence.

The interactions between financial development, productivity and growth have been extensively studied in the literature. However, their nature, directions and magnitudes remain unclear, and no consensus has been reached, notably in the agricultural sector. We conduct a state-of-the-art review of this topic and also present alternative environmental determinants (climate change and extreme event issues; water, soil and land management practices; waste management and circular economy) of agricultural productivity. In doing so, we show where this domain has fallen short on methodological approaches, while emphasizing the relevant feature characterizing this empirical debate. Moreover, we emphasize the heterogeneity of the linkages between financial development, productivity and growth across income groups. Along with prospects for future research, some policy recommendations are offered.

Purpose
In this paper, the heterogeneity of the linkages among financial development, productivity and growth across income groups is emphasized.

Design/methodology/approach
An empirical analysis is conducted with an illustrative sample of 130 economies over the period 1991–2019 and classified into four subsamples: Organisation for Economic Co-operation and Development (OECD), developing, least developed and net food importing developing countries. Forecast error variance decompositions and panel vector auto-regressive estimations are computed, with insightful findings.

Findings
Higher levels of output stimulate the economic development in the agricultural sector, mainly via the productivity channel and, in the most developed economies, also through access to credit. Differently, in developing and least developed economies, the role of access to credit is marginal. The findings have practical implications for stakeholders involved in the planning of long-run investments. In less developed economies, priorities should be given to investments in technology and innovation, whereas financial markets are more suited to boost the development of the agricultural sector of developed economies.

Originality/value
The authors conclude on the credit–output–productivity nexus and contribute to the literature in (at least) three ways. First, they assess how credit access, agricultural output and agricultural productivity are jointly determined. Second, they use a novel approach, which departs from most of the case studies based on single-country data. Third, they conclude on potential causality links to conclude on policy implications.

Diabetes disease can be considered as one of the major health problem over the world. Diabetes is a condition in which sugar amount in the blood is higher than normal. There is an urgent need to develop efficient systems for diabetes diagnoses according to high health care costs for individuals and hospitals. Artificial Neural Networks (ANNs) were successfully used in image and signal processing systems. Many literature studies were considered ANN approaches and algorithms for classification of the diabetes diseases. The objective of this paper is to review and discuss the studies related to diabetes diagnosis using ANNs. Models, Algorithms, data sets, strengths and limitations of these studies were considered in this paper. Finally, recommendations were suggested for building ANNs methodologies for diabetes classification and diagnosis.

The application of fiber-reinforced polymer bars is rapidly rising in concrete structures because of corrosion resistance and high tensile strength. By contrast, concrete structures reinforced with Fiber-Reinforced Polymer (FRP) bars illustrate less ductility and brittle failure without warning than reinforced concrete structures with conventional steel bars. Hybrid concrete structures with the combination of FRP and steel bars can simple beams to calculate the effective moment of inertia for hybrid reinforced concrete beams. The result shows that both soft computing models are highly precise compared to experimental data.

Purpose-In this paper, the heterogeneity of the linkages among financial development, productivity and growth across income groups is emphasized. Design/methodology/approach-An empirical analysis is conducted with an illustrative sample of 130 economies over the period 1991-2019 and classified into four subsamples: Organisation for Economic Cooperation and Development (OECD), developing, least developed and net food importing developing countries. Forecast error variance decompositions and panel vector auto-regressive estimations are computed, with insightful findings. Findings-Higher levels of output stimulate the economic development in the agricultural sector, mainly via the productivity channel and, in the most developed economies, also through access to credit. Differently, in developing and least developed economies, the role of access to credit is marginal. The findings have practical implications for stakeholders involved in the planning of long-run investments. In less developed economies, priorities should be given to investments in technology and innovation, whereas financial markets are more suited to boost the development of the agricultural sector of developed economies. Originality/value-The authors conclude on the credit-output-productivity nexus and contribute to the literature in (at least) three ways. First, they assess how credit access, agricultural output and agricultural productivity are jointly determined. Second, they use a novel approach, which departs from most of the case studies based on single-country data. Third, they conclude on potential causality links to conclude on policy implications.

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep lea...

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep lea...

The application of fiber-reinforced polymer bars is rapidly rising in concrete structures because of corrosion
resistance and high tensile strength. By contrast, concrete structures reinforced with Fiber-Reinforced Polymer
(FRP) bars illustrate less ductility and brittle failure without warning than reinforced concrete structures with conventional steel bars. Hybrid concrete structures with the combination of FRP and steel bars can
simultaneously increase strength and ductility. This paper aims to estimate the effective moment of inertia in hybrid concrete beams by using a neuro-fuzzy technique and Artificial Neural networks. A new equation has
been proposed for hybrid beams with attention to the importance of calculating the effective moment of inertia
in concrete beams. The proposed equation has been considered the effect of elastic modulus and hybrid reinforcement ratio on this parameter for hybrid reinforced concrete beams having FRP bars. This equation has
been presented based on the neural networks and experimental data conducted by other researchers on the simple beams to calculate the effective moment of inertia for hybrid reinforced concrete beams. The result shows that both soft computing models are highly precise compared to experimental data.

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network's prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC).

We p r e s e n t an unsupervised segmentation algorithm based on a Markov Random Field model for noisy images. The algorithm nds the the most likely number of classes, their associated model parameters and generates a corresponding segmentation of the image into these classes. This is achieved according to the MAP criterion. To facilitate this, an MCMC algorithm is formulated to allow the direct sampling of all the above parameters from the posterior distribution of the image. To allow the number of classes to be sampled, a reversible jump is incorporated into the Markov Chain. The jump enables the possible splitting and combining of classes and consequently, their associated regions within the image. Experimental results are presented showing rapid convergence of the algorithm to accurate solutions.

Financial development, productivity, and growth are interconnected, but the direction of causality remains unclear. The relevance of these linkages is likely different for developing and developed economies, yet comparative cross-country studies are scant. The paper analyses the relationship among credit access, output and productivity in the agricultural sector for a large set of countries, over the period 2000–2012, using an Artificial Neural Networks approach. Empirical findings show that these three variables influence each other reciprocally, although marked differences exist among groups of countries. The role of credit access is more prominent for the OECD countries and less important for countries with a lower level of economic de-elopement. Our analysis allows us to highlight the specific effects of credit in stimulating the development of the agricultural sector: in developing countries, credit access significantly affects production, whereas in developed countries, it also has an impact on productivity.

DC motors are proven enhanced performance while it used in different applications where accurate speed control is demanded. This study emphasises to deploy artificial neural controller for accurate and rapid control of speed. A comparative approach is made to prove the strength of ANN controller over Proportional Integral (PI) controller. MATLAB ANN toolbox and Simulink library is used to emulate the paradigm. Observations are made base on experimental system and the same is revealed more rapid response to speed fluctuation is made by ANN controller under different load circumstances. Neural network is designed to ensure perfect speed regulation after it fed by reference speed and other electrical parameters such as voltage and armature current. Moderated error is detected at ANN controllers of 1e-7 and 8 after 5000 epics of training process.

Series Capacitors (SC) Device is "placed in the middle of the" transmission line is used to decrease the reactive power in the "distribution networks 33KV and the powerful capability of series line compensation "to control the transmitted power can be used" much more effectively "to increase the transient stability limit" and provide power oscillation damping.

The power system blackout history of last two decades is presented.Conventional load shedding techniques, their types and limitations are presented.Applications of intelligent techniques in load shedding are presented.Intelligent techniques include ANN, fuzzy logic, ANFIS, genetic algorithm and PSO.The discussion and comparison between these techniques are provided.Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time.