Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic analysis for better performances. These (...) previously suggested techniques for network traffic analysis typically need voluminous information on network usages. Hence, this work proposes OFedeMWOUAA (optimal federated learning-based UAA technique with Meadow Wolf Optimisation) and DNN (deep Neuron Networks) for minimizing risks of data leakages in MWNs (Mobile Wireless Networks). In the proposed OFedeMWOUAA, the need to submit data to cloud servers does not arise because it trains DLTs locally and only uploads model gradients or knowledge weights. The OFedeMWOUAA approach effectively decreases dangers to data privacies with very minor performance losses in simulations. (shrink)

The problem of immigrant migration risk analysis is deeply studied and there are number of issues identified in the selection of country. There exists number of approaches available in the selection of countries towards migration. However, the methods do not produce effective results in the selection of migrant country. To handle this issue, an efficient real-time migrant risk analysis model (RMAM) is presented in this article. The method uses the traces of previos migration histories and data set maintained by the (...) model. Using the migration data set, the method preprocesses the data set for noise removal and normalization. With the normalized data set, the method applies Nation Centric Religious Support Analysis and Psychological Support Analysis. The nation centric religious support analysis algorithm measure the risk factors on religious features by computing National Religious Support (NRS) and the Psychological Support Analysis algorithm computes the value of Psychological Support (PS) for different countries. Based on the result of different analysis, the method computes Migration Support (MS) for various countries towards the selection of migration countries. The proposed model improves the performance of risk analysis and migration control. (shrink)

The article suggests an unsupervised model for featuring student’s learning patterns in an open-ended learning scenario. The article proceeds by generating powerful metrics to characterize the learner’s behavior and efficacy through Coherence investigation. Then, the selected features are combined through a Gaussian Hybrid Fuzzy Clustering (GHFC) that categorizes students based on their learning patterns. The proposed system features the essential behaviors of every group and associate the behaviors with ability to develop right models to gauge the learning gains between pre- (...) and post-test scores. Also, this article explains the deployment of behavior characterization to be developed as a adaptive framework of learning behaviors. (shrink)

Given the growing dependence on digital systems and the escalation of financial fraud occurrences, it is imperative to implement efficient cyber security protocols and fraud detection methodologies. The threat's dynamic nature often challenges conventional methods, necessitating the adoption of more sophisticated strategies. Individuals depend on pre-established regulations or problem-solving processes, which possess constraints in identifying novel and intricate fraudulent trends. Conventional techniques need help handling noise data and the substantial expenses incurred by false positives and true positives. To tackle these (...) obstacles, the present study introduces Deep Fraud Net, a framework that utilizes deep learning to detect and classify instances of financial fraud and cyber security threats. The Deep Fraud Net system model entails the utilization of a deep neural network to acquire intricate patterns and characteristics from extensive datasets through training. The framework integrates noise reduction methods to enhance the precision of fraud detection and improve the quality of input data. The Deep Fraud Net method attains a precision of 98.85%, accuracy of 93.35%, sensitivity of 99.05%, specificity of 93.16%, false positive rate of 7.34%, and true positive rate of 89.58%. The findings suggest that Deep Fraud Net can effectively detect and categorize instances of fraudulent behavior with a reduced occurrence of misclassifications. The method exhibits potential implications for diverse domains that prioritize robust security and fraud detection, including but not limited to banking, e-commerce, and online transactions. (shrink)

Detecting fraud in financial transactions is crucial for guaranteeing the integrity and security of financial systems. This paper presents an integrated approach for detecting fraudulent activities that incorporates Support Vector Machines (SVM) and Feedforward Neural Networks (FFNN). The proposed methodology utilizes the strengths of SVM and FFNN to distinguish between classes and capture complex patterns and relationships, respectively. The SVM model functions as a feature extractor, supplying the FFNN with high-level representations as inputs. Through an exhaustive evaluation utilizing labeled financial (...) transaction data, the integrated SVM-FFNN model shows promise in detecting fraud with increased accuracy and precision. This research contributes to the development of innovative techniques for enhancing financial fraud detection systems. (shrink)

With the growth of wireless networks and Internet resources, cloud robotics is becoming more and more popular. A prominent innovation that has gained traction is computation offloading, which gives robots more computational power by using cloud resources and parallel processing power. Nevertheless, because there are so many variables affecting performance, research on how effective computing offloading is in cloud robotics is still underway. Our research proposes a distributed cloud robotic architecture that uses Kafka middleware as a message broker to offload (...) compute in order to overcome this problem. We evaluate the performance of our solution using image processing techniques through experimental validation. Our tests show a significant decrease in the robot's CPU burden, but an anticipated rise in the robot's communication latency. (shrink)

The present study was carried out to revive the old art of dyeing with natural dye obtained from barks of Bombax ceiba Linn. locally known as semal tree. The commercialization of the present process will be helpful for their viable application in handloom and textile industries. Further, this will also have great impact especially for the economic growth of the rural dyer communities. In the present study, Cotton fabric was pre treated with eco friendly and non eco friendly mordants. The (...) various shades were obtained. The name of the shade and shade code was obtained by matching the shade card presented by Asian paint India. Colour strength of dyed cotton fabric was measured in terms of L, a, b colour scale by using hunter lab instrument Hunterlab EasyMatchQC Colour flex EZ 45 0, Reflectance . mode. The dyed samples have shown good to excellent and poor to good fastness properties Dr. Yogesh Vadwala | Dr. Namrita Kola "Dyeing of Cotton with Natural Dye Obtained from Barks of Bombax Ceiba Linn locally known as Semal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017. (shrink)

The COVID-19 outbreak has had a significant influence on the health of people all across the world, and preventing its further spread requires an early and correct diagnosis. Imaging using X-rays is often used to identify respiratory disorders like COVID-19, and approaches based on machine learning may be used to automate the diagnostic process. In this research, we present a deep learning approach for COVID-19 identification in X-ray pictures utilizing global thresholding. Our framework consists of two main components: (1) global (...) thresholding to preprocess X-ray images and extract features, and (2) a Convolutional Neural Network (CNN) for classifying the images as positive (indicating COVID-19) or negative (indicating no COVID-19). Global thresholding is used to convert X-ray images into binary images, which can highlight the features of interest, such as lung opacity. The Network is instructed to use a dataset consisting of X-ray pictures that have been labeled with the COVID-19 status of each image. The usefulness of our approach was shown by experimental findings obtained using a dataset of X-ray pictures that were made accessible to the public. We achieve high accuracy in COVID-19 detection, outperforming traditional machine learning techniques. Our framework is also able to correctly identify COVID-19 in images that were misclassified by human experts. (shrink)

Coronavirus disease has a crisis with high spread throughout the world during the COVID19 pandemic period. This disease can be easily spread to a group of people and increase the spread. Since it is a worldly disease and not plenty of vaccines available, social distancing is the only best approach to defend against the pandemic situation. All the affected countries' governments declared locked-down to implement social distancing. This social separation and persons not being in a mass group can slow down (...) the spread of COVID19. It reduces the physical contact between infected persons and normal healthy persons. Almost every health organization tells that to follow social distancing people should maintain at least 6 feet of distance from each other. This research proposes a deep learning approach for social distancing which is developed for tracking and detecting people who are in indoor as well as outdoor scenarios using YOLO V3 video analytic technique. This approach focuses to inspect whether the people are maintaining social distancing in many areas, using surveillance video with measuring the distance in real-time performance. Most of the early studies of detecting social distance monitoring were based on GPS for tracking the movements of people where the signals could be lost. On the other hand, some countries use drones to detect large gatherings of people who cannot have a clear view at night times [10]. In the future, the proposed system can be used fully for detecting threats in the public crowded or it can detect any person affected by critical situations (ie fainting, Cordia arrest) or planting the crops in the forms evenly with a uniform measurement. This proposal could be used in many fields like crowd analysis, autonomous vehicles, and human action recognition and could help the government authorities to redesign the public place layout and take precautionary action in the risk zones. This system analyses the social distancing of people by calculating the distance between people to slow downing the spread of the COVID 19 virus. (shrink)

Near the end of December 2019, the globe was hit with a major crisis, which is nothing but the coronavirusbased pandemic. The authorities at the train station should also keep in mind the need to limit the spread of the covid virus in the event of a global pandemic. When it comes to controlling the COVID-19 epidemic, public transportation facilities like train stations play a pivotal role because of the proximity of so many people who may be exposed to the (...) virus. Using common place CCTV cameras and deep learning with simple online and real-time (DeepSORT) methods, this study develops social distance monitoring using a YOLOv4 identification of a Surveillance Object Model. Based on experiments conducted with a minicomputer equipped with an Intel 11th Gen Intel(R) Core(TM) i3-1115G4 at 3.00GHz, 2995 Mhz, two Core(s), four Logical processor, four gigabytes of random-access memory (RAM), this paper makes use of CCTV surveillance, which was put into practice at the Guindy railway station, Chennai, Tamilnadu in India in order to detect the violation of social distancing. (shrink)

The coronavirus disease has spread throughout the world and its fear has made people to be more cautious in public places. Since precautionary measures are the only reliable protocol to defend ourselves, social distancing is the only best approach to defend against the pandemic situation. The reproduction number i.e. R0 factor of COVID-19, can be slowed down only through the physical distancing norms. This research proposes a deep learning approach for maintaining the social distance by tracking and detecting the people (...) present indoor and outdoor scenarios. Surveillance video is taken as the input and applied into you only look once (YOLO) V3 algorithm. The persons in the video are identified based on the segmentation algorithm present within the framework and then using Euclidean distance the image is evaluated. The bounding box algorithm helps to segregate the humans based on the minimum distance threshold. The proposed method is evaluated for images with peoples in the market, availing essential commodities and students entry inside a campus. Our proposed region-based convolutional neural network (RCNN) algorithm gives a better accuracy over the traditional models and hence the service can be implemented in general for places where social distancing is mandatory. (shrink)

We present a framework that we are currently developing, that allows one to extract knowledge from the knowledge discovery in database (KDD) dataset. Data mining is a very active and space growing research area. Knowledge discovery in databases (KDD) is very useful in scientific domains. In simple terms, association rule mining is one of the most well-known methods for such knowledge discovery. Initially, database are divided into training and testing for the aid of fuzzy generating the rules using fuzzy rules (...) generation the set of rules are generated from the given dataset. From the generated rules, we are extracting the significant rules by using the improved artificial bee colony algorithm and cuckoo search algorithm (IABCCS). After extracting optimal knowledge from the dataset via rules, the data will be classified using fuzzy classifier with the aid of this finally we will classify the attack and normal. (shrink)

Cloud Computing provides vast storage facility. The requirement of this system is to improve the security and transmission performance in the cloud storage environment. Methods: This system provides two level of security for the cloud data. The Client Data Security Contrivance (CDSC) and Cloud Service Provider (CSP) Data Security Contrivance are the two methods which transforms the original data to cipher text. The security algorithm used in CDSC is Linguistic Steganography. Blowfish algorithm is used in CSP Data Security Contrivance to (...) convert the cipher text using a secret-key block cipher of 64-bits. This system is designed to increase security and to improve the transmission performance. Storage-cheating attack model and Privacy-cheating attack model are implemented to protect the data on cloud. Findings: This system increases security and improves transmission performance. The blowfish algorithm is secure against unauthorized attacks and runs faster than the popular existing algorithms. Storage-cheating attack model and Privacy-cheating attack model provides the data confidentiality for data stored in the cloud in higher way. Improvements: The system can be implemented in real cloud system in the future. (shrink)

Cloud Computing provides vast storage facility. The requirement of this system is to improve the security and transmission performance in the cloud storage environment. Methods: This system provides two level of security for the cloud data. The Client Data Security Contrivance (CDSC) and Cloud Service Provider (CSP) Data Security Contrivance are the two methods which transforms the original data to cipher text. The security algorithm used in CDSC is Linguistic Steganography. Blowfish algorithm is used in CSP Data Security Contrivance to (...) convert the cipher text using a secret-key block cipher of 64-bits. This system is designed to increase security and to improve the transmission performance. Storage-cheating attack model and Privacy-cheating attack model are implemented to protect the data on cloud. Findings: This system increases security and improves transmission performance. The blowfish algorithm is secure against unauthorized attacks and runs faster than the popular existing algorithms. Storage-cheating attack model and Privacy-cheating attack model provides the data confidentiality for data stored in the cloud in higher way. Improvements: The system can be implemented in real cloud system in the future. (shrink)

Stock market prediction is essential and of great interest because success- ful prediction of stock prices may promise smart bene ts. These tasks are highly complicated and very dicult. Many researchers have made valiant attempts in data mining to devise an ecient system for stock market movement analysis. In this paper, we have developed an ecient approach to stock market prediction by employing fuzzy C-means clustering and arti cial neural network. This research has been encouraged by the need of predicting (...) the stock market to facilitate the investors about buy and hold strategy and to make pro t. Firstly, the original stock market data are converted into interpreted historical ( nancial) data i.e. via technical indi-cators. Based on these technical indicators, datasets that are required for analysis are created. Subsequently, fuzzy-clustering technique is used to generate di erent training subsets. Subsequently, based on di erent training subsets, di erent ANN models are trained to formulate di erent base models. Finally, a meta-learner, fuzzy system module, is employed to predict the stock price. The results for the stock market prediction are validated through evaluation metrics, namely mean absolute deviation, mean square error, root mean square error, mean absolute percentage error used to estimate the forecasting accuracy in the stock market. Comparative analysis is carried out for single Neural Network (NN) and existing technique neu- ral. The obtained results show that the proposed approach produces better results than the other techniques in terms of accuracy. (shrink)

Since Vehicular ad hoc networks (VANETs) are vulnerable to various kinds of attacks, there is a need to fulfill the security requirements like message privacy, integrity, and authentication. The authentication technique is said to be efficient if it detects compromised nodes accurately with less complexity, reduced authentication delay, and keying overhead. In this paper, a trust-based authentication scheme for cluster-based VANETs is proposed. The vehicles are clustered, and the trust degree of each node is estimated. The trust degree is a (...) combination of direct trust degree and indirect trust degree. Based on this estimated trust degree, cluster heads are selected. Then, each vehicle is monitored by a set of verifiers, and the messages are digitally signed by the sender and encrypted using a public/ private key as distributed by a trusted authority and decrypted by the destination. This verifies the identity of sender as well as receiver thus providing authentication to the scheme. By simulation results, we prove that the proposed technique provides high security with less overhead and delay. (shrink)

Background/Objective: The primary intension is to provide utility trade off and good privacy for intermediate datasets in cloud. Methods: An efficient conditional entropy and database difference ratio is employed for the process. Utility is taken care with the employment of conditional entropy with the help of Swarm Optimization (PSO). Privacy handled by database difference ratio. Findings: Conditional entropy is found out between the first column and the original database and this is taken as the fitness function in Particle Swarm Optimization (...) (PSO). Database difference is taken between the original database and convoluted database in the consequent module to yield right selection data and tuple information from the intermediate datasets in cloud. Applications/Improvements: Improved methods are required to provide privacy and utility for the right selection of datasets. This approach has better results by having higher entropy values and lower dataset difference ratio. (shrink)

Small MGS (microgrid systems) are capable of decreasing energy losses. Long-distance power transmission lines are constructed by integrating distributed power sources with energy storage subsystems, which is the current trend in the development of RES (renewable energy sources). Although energies produced by RES do not cause pollution, they are stochastic and hence challenging to manage. This disadvantage makes high penetration of RES risky for the stability, dependability, and power quality of main electrical grids. The energies obtained from RES must thus (...) be integrated in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid energy systems must manage energy efficiently. In order to improve power management and make better use of RES, this study offers a hybrid energy power management controller based on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS, PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and energy sources is the suggested approach for power control. D/R (demands/responses), customer reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are considered in this work. Along with current techniques, a suggested model is implemented in the MATLAB/Simulink platform. (shrink)

Values are an important part of human existence, his society and human relations. All social, economic, political, and religious problems are in one sense is reflection of this special abstraction of human knowledge. We are living in a globalized village and thinking much about values rather than practice of it. If we define religion and spirituality we can say that religion is a set of beliefs and rituals that claim to get a person in a right relationship with God, and (...) spirituality is a focus on spiritual things and the spiritual world instead of physical/earthly things. If we think rationally we can find the major evils related to religion exiting in present society are due to lack of proper understanding of religion and spirituality. If we really know our own religions and values associated with it, we can create a beautiful world, full or love and respect for each and every human being. The proper knowledge and practice of any religion’s values can make an integrated man. In the book, The Buddha and His Dhamma, Dr. Ambedkar elucidated the significance and importance of Dhamma in human life. The Dhamma maintained purity of life, which meant abstains from lustful, evil practices. The Dhamma is a perfection of life and giving up craving. Dhamma’s righteousness means right relation of man to man in all sphere of life. The basic idea underlying religion is to create an atmosphere for the spiritual development of the individual. He said that Knowing the proper ways and means is more important than knowing the ideal. The major objective of this paper is to the study the religious philosophy of Dr. B.R. Ambedkar and to study how he established that religious and spiritual values enables religious people in particular and humanity at large to solve contemporary problems. (shrink)

A general definition of consciousness is: ‘consciousness is a mental aspect of a system or a process, which is a conscious experience, a conscious function, or both depending on the context’, where the term context refers to metaphysical views, constraints, specific aims, and so on. One of the aspects of visual consciousness is the visual subjective experience (SE) or the first person experience that occurs/emerges in the visual neural-network of thalamocortical system (which includes dorsal and ventral visual pathways and frontal (...) cortex) during dynamic interactions among widely distributed neuronal groups. In this article, however, consciousness and SE are interchangeably used unless noted. Consciousness can be either access (reportable) or phenomenal consciousness. For access consciousness, the interactions are between feed forward stimulus dependent signals and fronto-parietal feedback attentional signals. The necessary ingredients for access (reportable) consciousness are (i) wakefulness, (ii) reentrant interactions among neural populations, (iii) fronto-parietal and thalamic-reticular-nucleus attentional signals that modulate consciousness, (iv) working memory that retains information for consciousness, (v) stimulus at or above threshold level, and (vi) neural-network proto-experiences (PEs) that are superposed SEs embedded in a neural-network. Attention and the ability to report are not necessary for phenomenal consciousness. The neural source for the arousal system is the ascending reticular activating system in the brain stem, which brings the thalamocortical neural networks to wakeful state as a baseline for consciousness to occur. Reentrant interactions among neural populations bind stimulus attributes (such as location and features) and entail consciousness. Attention could be the results of reentry and competitive interactions, and modulates the stimulus related feed forward signal and consciousness. The ‘sources’ of attention may be thalamic reticular nucleus for bottom-up or frontal cortex for top-down direction. The ‘target’ of visual attention is ‘V4/V8/VO’ for Red-Green (R-G) channel. The neural correlates of the psychophysical entity R-G channel appear to be ‘V4/V8/VO’-neural-network (retina → LGN ↔ V1 ↔ V2 ↔ ‘V4/V8/VO’, and areas for attention, memory, and wakefulness). The psychophysical Red-Green Channel, its neurophysiological correlates V4/V8/VO-neural-network, and related experience (such as redness) are integrated. The dual-aspect-dual-mode PE-SE framework (Vimal, J Integr Neurosci 7:49–73, 2008) was used to address the explanatory gap problem of materialism. Neural-network and related SEs are the results of the co-evolution and co-development of the material aspect (mass, charge, spin, force, quanta, and space-time) and the mental aspect of fundamental particles (strings or elementary particles (fermions and bosons)). Their mental aspects are considered as the carriers of superposed multiple possible experiences (SEs/PEs) in unexpressed form. These possibilities are actualized when neural-networks are formed via neural Darwinism, and a specific SE is selected by a matching process when the necessary ingredients of consciousness/SE are satisfied. A simple experimental design is suggested to address the necessary and sufficient attributes of consciousness. (shrink)

Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic analysis for better performances. These (...) previously suggested techniques for network traffic analysis typically need voluminous information on network usages. Hence, this work proposes OFedeMWOUAA (optimal federated learning-based UAA technique with Meadow Wolf Optimisation) and DNN (deep Neuron Networks) for minimizing risks of data leakages in MWNs (Mobile Wireless Networks). In the proposed OFedeMWOUAA, the need to submit data to cloud servers does not arise because it trains DLTs locally and only uploads model gradients or knowledge weights. The OFedeMWOUAA approach effectively decreases dangers to data privacies with very minor performance losses in simulations. (shrink)

The global catastrophe known as COVID-19 has shattered the world’s socioeconomic structure. Effective and affordable diagnosis techniques are crucial for better COVID-19 therapy and the eradication of bogus cases. Due to the daily upsurge in cases, hospitals only have a small supply of COVID-19 test kits. The study describes a deep Convolutional Neural Network (CNN) design for categorizing chest x-ray images in the diagnosis of COVID-19. The lack of a substantial, high-quality chest x-ray picture collection made efficient and exact CNN (...) categorization problematic. The dataset has been pre-processed using an image enhancement strategy to provide an effective training dataset for the proposed CNN model to achieve performance. The proposed model achieves 99.73% of accuracy, 98.95% of specificity, 99.47% of precision, 99.62% of sensitivity, and 98.71% of F1 score. A comparative study between the proposed model and numerous CNN-based COVID-19 detection algorithms is carried out to demonstrate that it outperforms other models. When evaluated on a separate dataset, the suggested model excelled over all other models, generally and explicitly. (shrink)

The ongoing COVID-19 pandemic has caused a global health crisis, and accurate diagnosis and early detection are essential for successful management of the outbreak. Convolutional neural networks and pre-processed chest X-ray pictures are the two main components of the unique proposed method for the identification of COVID-19, which is presented in this paper (CNNs). Image enhancement and segmentation are performed during the pre-processing stage. These operations increase the overall quality and contrast of the pictures, which in turn makes it simpler (...) for the CNN to recognise significant aspects of the images. The CNN model was trained using a large dataset of pre- processed X-ray pictures that included both COVID-19 positive and negative instances. The dataset was used to train the model. In comparison to more conventional diagnostic approaches, and this strategy was successful in achieving high levels of accuracy, sensitivity, and specificity in the detection of COVID-19. Moreover, this model designed an automated reporting system that saves time and costs by providing healthcare providers with diagnostic reports that are both prompt and accurate. This research demonstrates the viability of using CNNs and pre-processed X-ray images for the purpose of early identification of COVID-19 and offers an important resource for the efficient management of this worldwide health concern. (shrink)

Vibration, especially basic vibration, may cause loose contacts, open circuits, or other contact problems, which account for a large proportion of system failure causes. Due to the complexity of the chassis structure used in the biomedical motors, the vibration analysis of a single component cannot solve the vibration problems encountered in the work of the system. Therefore, it is necessary to use system simulation and experiments to monitor and enhance the health of the structure. Finite element method is used to (...) carry out the dynamic analysis from the component to the system, and calculate the natural frequency, modal shape and harmonic response. Then, the effectiveness and accuracy of the analysis results are verified by experiments through simulation and experiments. The results show that the measures taken have effectively reduced the vibration of the chassis equipment and improved its safety for better health safety of patients. There is just a 2% variation between the natural frequencies acquired from the experiment and the simulation computation. In terms of natural frequency, the first 6-order natural frequencies all have errors of less than 10%, and the natural frequency error is comparable to that of fireproof board. (shrink)

Since Vehicular ad hoc networks (VANETs) are vulnerable to various kinds of attacks, there is a need to fulfill the security requirements like message privacy, integrity, and authentication. The authentication technique is said to be efficient if it detects compromised nodes accurately with less complexity, reduced authentication delay, and keying overhead. In this paper, a trust-based authentication scheme for cluster-based VANETs is proposed. The vehicles are clustered, and the trust degree of each node is estimated. The trust degree is a (...) combination of direct trust degree and indirect trust degree. Based on this estimated trust degree, cluster heads are selected. Then, each vehicle is monitored by a set of verifiers, and the messages are digitally signed by the sender and encrypted using a public/ private key as distributed by a trusted authority and decrypted by the destination. This verifies the identity of sender as well as receiver thus providing authentication to the scheme. By simulation results, we prove that the proposed technique provides high security with less overhead and delay. (shrink)

Small MGS (microgrid systems) are capable of decreasing energy losses. Long-distance power transmission lines are constructed by integrating distributed power sources with energy storage subsystems, which is the current trend in the development of RES (renewable energy sources). Although energies produced by RES do not cause pollution, they are stochastic and hence challenging to manage. This disadvantage makes high penetration of RES risky for the stability, dependability, and power quality of main electrical grids. The energies obtained from RES must thus (...) be integrated in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid energy systems must manage energy efficiently. In order to improve power management and make better use of RES, this study offers a hybrid energy power management controller based on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS, PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and energy sources is the suggested approach for power control. D/R (demands/responses), customer reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are considered in this work. Along with current techniques, a suggested model is implemented in the MATLAB/Simulink platform. (shrink)

Vibration, especially basic vibration, may cause loose contacts, open circuits, or other contact problems, which account for a large proportion of system failure causes. Due to the complexity of the chassis structure used in the biomedical motors, the vibration analysis of a single component cannot solve the vibration problems encountered in the work of the system. Therefore, it is necessary to use system simulation and experiments to monitor and enhance the health of the structure. Finite element method is used to (...) carry out the dynamic analysis from the component to the system, and calculate the natural frequency, modal shape and harmonic response. Then, the effectiveness and accuracy of the analysis results are verified by experiments through simulation and experiments. The results show that the measures taken have effectively reduced the vibration of the chassis equipment and improved its safety for better health safety of patients. There is just a 2% variation between the natural frequencies acquired from the experiment and the simulation computation. In terms of natural frequency, the first 6-order natural frequencies all have errors of less than 10%, and the natural frequency error is comparable to that of fireproof board. (shrink)

Small MGS (microgrid systems) are capable of decreasing energy losses. Long-distance power transmission lines are constructed by integrating distributed power sources with energy storage subsystems, which is the current trend in the development of RES (renewable energy sources). Although energies produced by RES do not cause pollution, they are stochastic and hence challenging to manage. This disadvantage makes high penetration of RES risky for the stability, dependability, and power quality of main electrical grids. The energies obtained from RES must thus (...) be integrated in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid energy systems must manage energy efficiently. In order to improve power management and make better use of RES, this study offers a hybrid energy power management controller based on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS, PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and energy sources is the suggested approach for power control. D/R (demands/responses), customer reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are considered in this work. Along with current techniques, a suggested model is implemented in the MATLAB/Simulink platform. (shrink)

Ayurveda is the science of living being. With the aim of health and procurement of disease it almost covers all facets of life. It includes health of an individual at physical, mental, spiritual, social level. Ayurvedic classics includes brihatrayee samhita like Charak, Sushruta and Ashtanga Hridaya. A review based study of music (geet, sangeet) was done in these classics to explore whether these classics includes any form of music as therapy or not. Based on review of these classics it was (...) found that music in form of geet (vocal or instrument) has been given in contexts like vajikara(aphrodisiac), vata/pitta prakriti (basic consititutional make up), gandharva sattva, madyapana (intake of alcohol), therapy for rajyakshma (immune deficiency diseases), sanyasa (syncope/coma), jivadan (haemetemesis) etc. various swara have been correlated with their pacifying power of three sharirikadoshas viz. vata, pitta and kapha. Also a relationship between various raga of indian classical music have been said to be based on time of its singing which may be used as a therapy to pacify the dearrangement of prevalent doshas. several articles have shown music as therapy in sleep disorder, psychiatric disorders, schizophrenia, cardiovascular, cancer etc. with this paper a perspective of Ayurveda have been explored in music therapeutics. (shrink)

Dr. B.R. Ambedkar is one of the names who advocated to change social order of the age-old tradition of suppression and humiliation. He was an intellectual, scholar, statesman and contributed greatly in the nation building. He led a number of movements to emancipate the downtrodden masses and to secure human rights to millions of depressed classes. He has left an indelible imprint through his immense contribution in framing the modern Constitution of free India. He stands as a symbol of struggle (...) for achieving the Social Justice. We can assign several roles to this great personality due to his life full dedication towards his mission of eradicating evils from Indian society. The social evils of Indian society, also neglected this great personality even in intellectual sphere too. The so-called intellectuals of India not honestly discussed his contribution to Indian intellectual heritage, rather what they discussed, also smells their biases towards a Dalit literate and underestimated his great personality. This paper will attempt to discuss important facts about life and a short description of the literature written by Dr. B.R. Ambedkar. This is followed by discussion his philosophy in the five major sections i.e. Feminism and women empowerment, philosophy of education, ideas on social justice and equality, philosophy of politics and economics and philosophy of religion. (shrink)

Ayurveda is science of living being. Aim of Ayurveda is mantainance of healthy life and pacification of diseases of diseased ones. Dharma, artha, kama and moksha these four are together called chaturvidha purushartha which is achieved by arogya (health).Ayurveda holds view of its independent darshanika viewthough it has shades of nearly all six astika darshanas. Mimamsa’s first verse implies its motto to explore Dharma. Ayurveda considers dharma as one of basic component to health. Dharma has been described under trieshana by (...) acharya Bhela. Various conducts are described under sadavritta, achararasayana, moksha marga, naishtikichikitsa. Ayurveda describes three types of treatment which are yuktivyapshraya, daivavyapashraya and sattvavajaya. This daivyapashraya includes mantra, ausadhi, mani, mangal, bali, upahaar, homa, niyama,prayashchita, upawaas, swastyayana, pranipata, gamana etc. These type of methods are widely described in karma kanda of mimamsa darshana. This treatment method has been used in curing of diseases like unmada,graharoga. Methods of yagya has been described as well as used in putreshti and punsawansamskara described in charakasamhita. With this paper karma kanda widely used in treatment has been described in Ayurveda. (shrink)

Dr. B. R. Ambedkar is one of the most eminent intellectual figures of modern India. The present year is being celebrated as 125th Birth Anniversary of Dr. B. R. Ambedkar. Educationist and humanist from all over the world are celebrating 125th Birth Anniversary of Dr. B. R. Ambedkar by organizing various events and programmes. In this regard the Centre for Positive Philosophy and Interdiscipinary Studies (CPPIS) Pehowa (Kurukshetra) took an initiative to be a part of this mega event by organizing (...) an national level esssay competition for students, publication of books, posters and research journals on Dr. B.R. Ambedkar. Dr. B.R. Ambedkar’s ideas, writings and outlook could well be characterized as belonging to that trend of thought called Social Humanism. He developed a socio-ethical philosophy and steadfastly stood for human dignity and freedom, socio-economic justice, material prosperity and spiritual discipline. He showed the enlightening path for Indian society via his ideals of freedom, equality and fraternity and made India a democratic country. The complete works of Dr. B.R. Ambedkar published by the Governemtn of Maharastra and it has taken about 25 years to complete this initiative in 21 Volumes with the name, “Dr. Babasaheb Ambedkar: Writing and Speeches” and covers 14000 pages. In the words of Trilochan Sing, “Above all, Dr. Ambedkar is a philosopher. Those who read his books cannot be failed to be impressed with steadffastness with which he pursues truth; and only those who have dispassionately read his books can frame true estimate of the greatness of the man”. These 21 Volumes includes books published by Dr. B.R. Ambedkar himself and unpublished writings and speaches too. The present volume entitled “Dr. B.R. Ambedkar: The Maker of Modern India” contains 12 research papers on the different aspects of philosophy of Dr. B.R. Ambedkar written by academicians from different branches of knowledge. You can find a variety of dialogues and concen about the theme of the book here. We are not defending this book as a highly an intellectual work but a smaller step to know the various aspects of this great personality and is a start to study his vast wisdom. You suggestions and comments are welcome to its first hand review version. (shrink)

Agritourism allows tourists to fully experience themselves in agricultural life by combining the beauty of rural settings with real-life farming experiences. Agritourism remains underrecognized in many regions due to obstacles like inadequate infrastructure and limited awareness. Through an user friendly digital platform that provides features like farm listings, tour listings ,booking systems, and educational content to connect tourists with farm owners and farmers, this project aims to address these issues. These efforts suggest that digital platforms can support rural economies, preserve (...) agricultural heritage, and maintain a positive relationship between tourists and the natural environment. Additionally, they can effectively promote agritourism. (shrink)

Cyber attackers targeting large corporations achieved a high perimeter penetration success rate during 2013, resulting in many corporations incurring financial losses. Corporate information technology leaders have a fiduciary responsibility to implement information security domain processes that effectually address the challenges for preventing and deterring information security breaches. Grounded in corporate governance theory, the purpose of this correlational study was to examine the relationship between strategic alignment, resource management, risk management, value delivery, performance measurement implementations, and information security governance (ISG) effectiveness (...) in United States-based corporations. Surveys were used to collect data from 95 strategic and tactical leaders of the 500 largest for-profit United States headquartered corporations. The results of the multiple linear regression indicated the model was able to significantly predict ISG effectiveness, F(5, 89) = 3.08, p = 0.01, R² = 0.15. Strategic alignment was the only statistically significant (t = 2.401, p <= 0.018) predictor. The implications for positive social change include the potential to constructively understand the correlates of ISG effectiveness, thus increasing the propensity for consumer trust and reducing consumers' costs. (shrink)

The problem of personal identity contains various questions and issues, but the main issue is persistence; how can one person remain the same over time? Modern philosophers have proposed various solutions to this problem; however, none are without problems. David Hume rejected the notion of personal identity as fictitious and posited a theory that personal identity is merely a bundle of perceptions which does not remain the same over time. Hume’s approach to personal identity is flawed, and Derek Parfit pushed (...) back against Hume’s complete rejection of personal identity through his argument of psychological continuity; a continuous chain of overlapping cognitive connections between beliefs, preferences, memories, and other characteristics can meet the criterion of identity. However, Parfit falls short in only acknowledging mental features as the essential property of personal identity; physical features such as age, race, sex, etc. have a significant impact on one’s understanding of themselves. Through a combination of Hume’s bundle theory and Parfit’s psychological continuity, an understanding of what personal identity is and how it is able to remain constant over time can be reached. In this paper, I argue that personal identity is a bundle of mental and physical features that persists through time through psychological continuity. (shrink)

Disagreements that resist rational resolution, often termed “deep disagreements”, have been the focus of much work in epistemology and informal logic. In this paper, I argue that they also deserve the attention of philosophers of mathematics. I link the question of whether there can be deep disagreements in mathematics to a more familiar debate over whether there can be revolutions in mathematics. I propose an affirmative answer to both questions, using the controversy over Shinichi Mochizuki’s work on the abc conjecture (...) as a potential example of both phenomena. I conclude by investigating the prospects for the resolution of mathematical deep disagreements in virtue-theoretic approaches to informal logic and mathematical practice. (shrink)

MLNova is a structured platform designed to bridge the gap between theoretical knowledge and practical application in machine learning. Focused on enhancing student learning, the platform offers a path for beginners to explore key concepts in data preprocessing and model evaluation. Through interactive modules, MLNova delivers prerecorded lessons and real-world projects, allowing learners to experience hands-on engagement. This paper outlines the platform’s design, methodology, and the impact of interactive learning on improving comprehension of machine learning principles. Early feedback indicates a (...) significant improvement in user engagement and learning outcomes. (shrink)

This commentary explores the notion of vulnerability applied to pregnant women in clinical research. The use of this notion, related to a semantic problem, raises an ethical issue and participates in the quasi-systematic exclusion of this sub-population from the research process.

Ascorbic acid (vitamin C) is a water-soluble vitamin; it is present in the highest concentration in the brain. Ascorbic acid in high doses acts as a potential treatment for various neuropathological and psychiatric conditions. Flumazenil is a benzodiazepine antagonist; it competitively inhibits the activity of benzodiazepine and non-benzodiazepine substances that interact with benzodiazepine receptors site on the GABA/benzodiazepine receptor complex. This study aims to investigate the effect of flumazenil on the anxiolytic action of ascorbic acid using an elevated plus maze (...) model of anxiety in rats. Male Albino Wistar rats weighing between 250 and 320 grams were used. Rats were divided into four equal groups of seven rats each and treated as follows: Group I, the control group received a single dose of 1.0% tween 80; Group II treated with a single dose of 125 mg/kg ascorbic acid; Group III was injected by a single dose of 1.0 mg/kg flumazenil; Group IV received a combination treatment of 125 mg/kg ascorbic acid and 1.0 mg/kg flumazenil. Behavioural measurements using a plus maze were scored 30 min after the administration. The parameters scored are the time spent on the open and closed arms, the lines and number of entries into open and closed arms, and the anxiety measure. Ascorbic acid decreased anxiety measure and increased the total lines and total number of entries; this effect was abolished by the administration of flumazenil with ascorbic acid. Thus, ascorbic acid produces an anxiolytic-like effect in rats; this effect was abolished by flumazenil administration with ascorbic acid. This may indicate that the GABA/benzodiazepine receptor complex has to be stimulated to produce the anxiolytic effect. (shrink)

This work presents the design of an expert system that aims to provide the patient with background for suitable diagnosis of some of the eye diseases. The eye has always been viewed as a tunnel to the inner workings of the body. There are many disease states that may produce symptoms from the eye. CLIPS language is used as a tool for designing our expert system. An initial evaluation of the expert system was carried out and a positive feedback was (...) received from the users. (shrink)

Throughout this research, imposing the training of an Artificial Neural Network (ANN) to play tic-tac-toe bored game, by training the ANN to play the tic-tac-toe logic using the set of mathematical combination of the sequences that could be played by the system and using both the Gradient Descent Algorithm explicitly and the Elimination theory rules implicitly. And so on the system should be able to produce imunate amalgamations to solve every state within the game course to make better of results (...) of winnings or getting draw. (shrink)

Intelligent tutoring system (ITS) is a computer system which aims to provide immediate and customized or reactions to learners, usually without the intervention of human teacher's instructions. Secretariats professional to have the common goal of learning a meaningful and effective manner through the use of a variety of computing technologies enabled. There are many examples of professional Secretariats used in both formal education and in professional settings that have proven their capabilities. There is a close relationship between private lessons intelligent, (...) cognitive learning and design theories; and there are ongoing to improve the effectiveness of ITS research. And it aims to find a solution to the problem of over-reliance on students' teachers for quality education. The program aims to provide access to high-quality education to every student, and therefore the reform of the education system as a whole. In this paper, we will use Intelligent Tutoring System Builder (ITSB) to build an education system on cloud computing in terms of the concept of cloud computing and components and how to take advantage of cloud computing in the field. (shrink)

Abstract: Background: Tuberculosis is a major public health problem world-wide, it is contagious disease caused by organism mycobacterium tuberculosis. The aim of this study was to assess the level of zinc and albumin in newly diagnosed patients with pulmonary tuberculosis in Khartoum state. Methods: Fifty blood samples were collected from newly diagnosed pulmonary tuberculosis patients during the period between April to May 2017, chosen randomly from Abu Anga Teaching Hospital and fifty blood samples from apparently healthy individuals serve as control (...) group. Estimation of serum albumin level was performed using spectrophotometer and estimation of serum zinc by using flame atomic absorption spectrophotometer. The results were analyzed by using (SPSS) computer program. Results: The study showed that, serum level of zinc and albumin were significantly decrease in patients compared to control group (mean± SD for cases versus control). For zinc :( 0.16±0.05 versus 0.58± 0.13 mg/l, P. value= 0.000). For albumin: (2.5±0.58 versus 4.1±0.31g/dl, P. value= 0.000). The result of this study showed that, there was statically significant decrease in the mean of BMI in patients compared to control group. Mean ±SD for case versus control (18.7±0.17 versus 21.5±1.7). The result of this study showed that, pulmonary tuberculosis most common among age between (20-39) years (52%) and (48%) of patients between age (40-58) years and this disease most abundant in males (78%) than females (22%). Persons correlation showed that there was no correlation between serum zinc level and age (r =-0.128, p. value=0.374) also there was no correlation between the level of serum albumin and age (r=0.137, p. value =0.344). Conclusion: The serum levels of zinc and albumin were significantly decreased in newly diagnosed patients with pulmonary tuberculosis in Khartoum state. (shrink)

This study employs a neural network to analyze the connection between smoking, drinking, and various health-related factors using a dataset of 5148 samples. Achieving an impressive 99.94% accuracy and an average training error of 0.0016, the model identifies influential factors such as serum aminotransferases, serum creatinine, sex, weight, and triglyceride levels. These findings enhance our understanding of lifestyle choices and their impact on health. This research underscores the potential of machine learning in studying complex health phenomena.

The study aimed to measure the effect of Crowdfunding financing on entrepreneurship aspirations in Arabic region. Population consists of members in (15) Crowdfunding Arabic platforms which are working with all models and online at (2018). Descriptive approach and a questionnaire used as a tool for this research. The results showed that Crowdfunding financing effect positively on entrepreneurship aspirations, Crowdfunding used mostly to finance small business or microfinance and charity. Add to that, Crowdfunding leads to financial efficiency and costs reduction, more (...) flexible payments methods and more opportunities for entrepreneurs. The study recommended the importance of entrepreneurship culture and its role in promoting entrepreneurs, the effect off Fintech and various e-banking and e-payments methods, and the need of more interest in innovation and creativity. (shrink)

This paper aims to identify computerized management information systems resources and their relationship to the development of performance in the Electricity Distribution Company in Gaza. This research used two dimensions. The first dimension is computerized management information systems and the second dimension the Development of Performance. The control sample was (063). (360) questioners were distributed and (306) were retrieved back with a percentage of (85%). Several statistical tools were used for data analysis and hypotheses testing, including reliability correlation using Cronbach’s (...) alpha, “ANOVA”, Simple Linear Regression and Step Wise Regression. The overall findings of the current study suggested the presence of a statistically significant relationship between resources (physical, software, and human and organizational) for the computerized management information systems and the development of performance in the Electricity Distribution Company in Gaza. The study recommended the following: The need to strengthen the company's management interest in the potential of computerized management information systems and using them in the computerization of all the company's activities. And the need to involve workers and users in the design of computerized management information systems and assessment and development process. And strengthen the relationship between users and information systems personnel in the department responsible for the system. And it is essential that the company is developing the infrastructure for information technology in general, and computerized management information systems, in particular for the development of performance. And increase interest in providing resources (physical, software, and human and organizational) for the computerized management information systems. The current study is unique by the virtue of its nature, scope and way of implied investigation, as it is the first study at Electricity Distribution Company in Gaza resources explores the status of Computerized management information systems and their relationship to the development of performance in the Electricity Distribution Company in Gaza increasing interest in Computerized management information systems through continuity, keeping pace with technological means and modern techniques. (shrink)

This paper aims to identify computerized management information systems resources and their relationship to the development of performance in the Electricity Distribution Company in Gaza. This research used two dimensions. The first dimension is computerized management information systems and the second dimension the Development of Performance. The control sample was (063). (360) questioners were distributed and (306) were retrieved back with a percentage of (85%). Several statistical tools were used for data analysis and hypotheses testing, including reliability correlation using Cronbach’s (...) alpha, “ANOVA”, Simple Linear Regression and Step Wise Regression. The overall findings of the current study suggested the presence of a statistically significant relationship between resources (physical, software, and human and organizational) for the computerized management information systems and the development of performance in the Electricity Distribution Company in Gaza. The study recommended the following: The need to strengthen the company's management interest in the potential of computerized management information systems and using them in the computerization of all the company's activities. And the need to involve workers and users in the design of computerized management information systems and assessment and development process. And strengthen the relationship between users and information systems personnel in the department responsible for the system. And it is essential that the company is developing the infrastructure for information technology in general, and computerized management information systems, in particular for the development of performance. And increase interest in providing resources (physical, software, and human and organizational) for the computerized management information systems. The current study is unique by the virtue of its nature, scope and way of implied investigation, as it is the first study at Electricity Distribution Company in Gaza resources explores the status of Computerized management information systems and their relationship to the development of performance in the Electricity Distribution Company in Gaza increasing interest in Computerized management information systems through continuity, keeping pace with technological means and modern techniques. (shrink)

Sign Language Recognition (SLR) aims to translate sign language into text or speech in order to improve communication between deaf-mute people and the general public. This task has a large social impact, but it is still difficult due to the complexity and wide range of hand actions. We present a novel 3D convolutional neural network (CNN) that extracts discriminative spatial-temporal features from photo datasets. This article is about classification of sign languages are not universal and are usually not mutually intelligible (...) although there are also similarities among different sign languages. They are the foundation of local Deaf cultures and have evolved into effective means of communication. Although signing is primarily used by the deaf and hard of hearing, hearing people also use it when they are unable to speak, when they have difficulty speaking due to a health condition or disability (augmentative and alternative communication), or when they have deaf family members, such as children of deaf adults. In this article we use the 43500 image in the dataset in size 64*64 pixel by use CNN Architecture and achieved 100% accuracy. (shrink)

Abstract: Sign language recognition is one of the most rapidly expanding fields of study today. Many new technologies have been developed in recent years in the fields of artificial intelligence the sign language-based communication is valuable to not only deaf and dumb community, but also beneficial for individuals suffering from Autism, downs Syndrome, Apraxia of Speech for correspondence. The biggest problem faced by people with hearing disabilities is the people's lack of understanding of their requirements. In this paper we try (...) to fill this gap. By trying to translate sign language using artificial intelligence algorithms, we focused in this paper using transfer learning technique based on deep learning by utilizing a MobileNet algorithm and compared it with the previous paper results[10a], where we get in the Mobilenet algorithm on the degree of Accuracy 93,48% but the VGG16 the accuracy was 100% For the same number of images (43500 in the dataset in size 64*64 pixel ) and the same data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch . (shrink)