Artificial Neural Networking and Human Brain

Neural Network technology performs “intelligent” tasks. ANN(Artificial Neural Network) can be employed to solve a wide spectrum of problems as optimization , Parallel Computing , Matrix Algebra and Signal Processing . ANN is an information processing paradigm inspired by the way biological nervous System , such as brain , process information ANN is an interconnected web of neurons which a building block of neural network . Neurons receives inputs from other sources, combines them in some way performs a generally non linear operation and then outputs the final results. This paper gives overview of working of Neurons for basic understanding how electronic model of ANN are work for problem solving

Parallel quantized charge pumping

Two quantized charge pumps are operated in parallel. The total current generated is shown to be far more accurate than the current produced with just one pump operating at a higher frequency. With the application of a perpendicular magnetic field the accuracy of quantization is shown to be $<$20 ppm for a current of $108.9$pA. The scheme for parallel pumping presented in this work has applications in quantum information processing, the generation of single photons in pairs and bunches, neural networking and the development of a quantum standard for electrical current. All these applications will benefit greatly from the increase in output current without the characteristic decrease in accuracy as a result of high-frequency operation

Developing a Prototype to Translate Pakistan Sign Language into Text and Speech While Using Convolutional Neural Networking

The purpose of the study is to provide a literature review of the work done on sign language in Pakistan and the world. This study also provides a framework of an already developed prototype to translate Pakistani sign language into speech and text while using convolutional neural networking (CNN) to facilitate unimpaired teachers to bridge the communication gap among the deaf learners and unimpaired teachers. Due to the lack of sign language teaching, unimpaired teachers face difficulty in communicating with impaired learners. This communication gap can be filled with the help of this translation tool. Research indicates that a prototype has been evolved that can translate the English textual content into sign language and highlighted that there is a need for translation tool which can translate the signs into English text. The current study will provide an architectural framework of the Pakistani sign language to English text translation tool that how different components of technology like deep learning, convolutional neural networking, python, tensor Flow, and NumPy, InceptionV3 and transfer learning, eSpeak text to speech help in the development of a translation tool prototype. Keywords: Pakistan sign language (PSL), sign language (SL), translation, deaf, unimpaired, convolutional neural networking (CNN). DOI: 10.7176/JEP/10-15-18 Publication date:May 31st 201

Neural Network Approaches for Early Breast Cancer Detection

Research on breast cancer is crucial due to its significant impact on public health, with high mortality rates underscoring the urgency for improved diagnostic methods. Early detection plays a pivotal role in enhancing treatment outcomes and reducing mortality rates. This paper addresses the pressing need for more effective early disease detection methods, particularly focusing on breast cancer diagnosis. It proposes the utilization of neural networking techniques, known for their potential to enhance accuracy and efficiency in cancer diagnosis. The study aims to provide a comprehensive overview of breast cancer detection using neural networking, emphasizing its significance in improving patient outcomes. By showcasing the effectiveness of neural network approaches, the research contributes to advancing early cancer detection efforts, aligning with global health initiatives prioritizing early diagnosis and intervention

The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence

Intelligent systems based on first-order logic on the one hand, and on artificial neural networks (also called connectionist systems) on the other, differ substantially. It would be very desirable to combine the robust neural networking machinery with symbolic knowledge representation and reasoning paradigms like logic programming in such a way that the strengths of either paradigm will be retained. Current state-of-the-art research, however, fails by far to achieve this ultimate goal. As one of the main obstacles to be overcome we perceive the question how symbolic knowledge can be encoded by means of connectionist systems: Satisfactory answers to this will naturally lead the way to knowledge extraction algorithms and to integrated neural-symbolic systems.Comment: In Proceedings of INFORMATION'2004, Tokyo, Japan, to appear. 12 page

NEURAL NETWORKING OF INFILLED RC LOW-RISE SERVICE BUILDINGS

Artificial neural networks (ANNs) are one of the most research areas that attracts the attention of experts of various scientific areas. Recent research activities regarding ANNs indicated that this method is a powerful tool to solve complicated problems in engineering fields.In this paper, ANNs were utilized to predict the lateral behavior of school buildings in Egypt. For this, reinforced concrete (RC) frames representing common school buildings with different characteristics were analyzed using nonlinear dynamic pushover analysis to obtain their capacity curves, failure loads and displacements. Parameters included number of stories, location and dimensions of the frames, distribution of masonry infill panels, and properties of concrete and reinforcement. Obtained data were used to train several ANN models with different topologies and learning algorithms. The most representative ANN was used to obtain more insight into the behavior of school building frames with different parameters

Developing Model for Fuel Consumption Optimization in Aviation Industry

The contribution of aviation to society and economy is undisputedly significant. The aviation industry drives economic and social progress by contributing prominently to tourism, commerce and improved quality of life. Identifying the amount of fuel consumed by an aircraft while moving in both airspace and ground networks is critical to air transport economics. Aviation fuel is a major operating cost parameter of the aviation industry and at the same time it is prone to various constraints. This article aims to develop a model for fuel consumption of aviation product. The paper tailors the information for the fuel consumption optimization in terms of information development, information evaluation and information refinement. The information is evaluated and refined using statistical package R and Factor Analysis which is further validated with neural networking. The study explores three primary dimensions which are finally summarized into 23 influencing variables in contrast to 96 variables available in literature. The 23 variables explored in this study should be considered as highly influencing variables for fuel consumption which will contribute significantly towards fuel optimization. Keywords: Fuel Consumption, Civil Aviation Industry, Neural Networking, Optimizatio

American Sign Language (ASL): Linguistically and Cognitively - Why Deaf People Should Learn ASL & Learn it Early

This thesis presents data supporting the value of including American Sign Language (ASL) in the education of Deaf people. Historically, Deaf education has not fully included or has excluded ASL in an effort to focus on English due to a belief that ASL hinders learning English. ASL must fit within the definition of language with unique linguistic features for its inclusion in language education. Plasticity of the brain lends itself to the ability for language processing networks to form based on language experience. Deaf people can fully access visual language versus auditory language. Therefore, acquiring ASL early in life, during the critical period, allows Deaf people to establish a strong language foundation, upon which they could also learn English. Late language learning alters neural networking, which can lessen one’s ability to process language and use other cognitive processes. Despite differing modalities, ASL and English engage similar neural networks, called language regions. Consequently, ASL follows similar cognitive processes to English, which supports the value of ASL as a language fully accessible for Deaf people to acquire and use. Learning ASL prepares Deaf people for success in communication, learning English, and other endeavors in life because of effective neural networking

Forecasting bearing capacity of the mixed soil using artificial neural networking

Abstract. The bearing capacity of soil changes owing to the mechanical properties of the soil and influences on structural stability. In most of the geotechnical engineering projects, there are several soil mechanic experiments, they need interpretation before application. The mechanical properties of soil interaction make complex predict of soil bearing capacity. However, to enhancement safety of construction project need to the interpretation of soil experiments and design results for proper application in a geotechnical engineering project. In this study, artificial neural networking is proposed for the evaluation of the mixed soil characteristics to forecast the safe bearing capacity of soil because of the mechanical properties of the soil interaction phenomenon. The results reveal for prediction of the safe bearing capacity, the R2 and RMSE for all mechanical properties effects on safe bearing capacity are 0.98 and 0.02, these values can provide a suitable accuracy for prediction safe bearing capacity of the mixed soil. The higher inaccuracy obtained when only the influence of single mechanical property on the mixed soil considered in prediction of the safe bearing capacity. This study supports the enhancement of geotechnical engineering design quality through prediction safe bearing capacity from characterized mechanical properties of the soil