Search

The evaluation of musician performance has traditionally been subjective and challenging, relying on human judgment and personal preferences. However, recent advancements in artificial intelligence (AI) have opened up new possibilities for objective and automated assessment. This paper presents a comprehensive survey and analysis of AI-enabled techniques in evaluating musician performance. The research addresses the limitations of subjective evaluations and aims to enhance objectivity and efficiency in assessment. By leveraging AI and machine learning, various aspects of performance, including technical skills, musical interpretation, and emotional expression, can be objectively evaluated. The findings demonstrate the potential of AI-based assessment tools in providing objective feedback, enhancing music education, and enabling fair evaluations in competitions and auditions. The survey encompasses ten key papers that highlight different methodologies, advantages, and limitations in the field of automated music performance assessment. The reviewed literature showcases approaches such as deep neural networks, sensor localization algorithms, multimodal analysis, and machine learning techniques. Through a comparative analysis, this paper provides insights into the strengths and limitations of each approach. [ABSTRACT FROM AUTHOR]

Early identification and accurate diagnosis are crucial for successful therapy and improved patient outcomes of brain tumors. The study looks at how deep learning may help detect brain tumors, including how convolutional neural networks (CNNs) can change the face of diagnostic imaging. This underscores the need of precise and timely identification of brain cancer for efficient therapy and enhanced patient results. The study discusses the challenges associated with tumor segmentation and classification, such as variations in size, shape, and location. To overcome these challenges, deep learning techniques are utilized to analyze brain scans and identify tumor abnormalities. However, the paper underscores the importance of these models serving as tools to assist medical professionals rather than replacing their expertise. Additionally, the paper presents a comparative study that evaluates different techniques, artificial neural networks (ANN) achieve the highest accuracy at 99%, followed by Support Vector Machine (SVM) at 98.9%. vision transformer (ViT), Extreme Gradient Boosting (XG Boost), CNN-based dense Efficient Net, and Deep Neural Network (DNN) also show high accuracies above 98%. However, U-Net, Alex Net, hybrid ensemble method (Random Forest (RF), K-Nearest Neighbour (KNN), Decision Tree (DT), and Region-based convolutional neural networks (R-CNN)) have lower accuracies ranging from 91.66% to 97.305%. Overall, the findings highlight the potential of deep learning in enhancing the efficiency, precision of brain tumor diagnosis, leads to earlier treatment and enhanced patient prognosis. [ABSTRACT FROM AUTHOR]

The paper is focused on the determination of mechanical fracture parameters of alkali-activated concrete (AAC) specimens of nominal dimensions 80 × 80 × 240 mm with central notches of the following types: straight-through notch, chevron notch with 90° notch angle and chevron notch with 120° notch angle. In accordance with the results of the experimental research, a numerical simulation of the fracture test of the specimens tested in three-point bending was carried out. The 3D FEM computational model is based on the configuration of the laboratory test and was developed in ATENA-GiD software. Artificial neural network (ANN)-based inverse analysis was used to identify the mechanical fracture parameters of studied composite. A neural network ensemble was created, with one robust network for each type of notch, covering the entire domain of identified parameters. The main goal of this paper is to present the results of the identification of the mechanical fracture parameters of the AAC and their comparison with the values obtained by direct evaluation of the experiments. Another objective is to investigate the influence of the notch type on the mechanical fracture parameters of the AAC. [ABSTRACT FROM AUTHOR]

The rise of cyber-attacks such as Distributed Denial of Service (DDoS) and SQL injection has become a significant concern for organizations and individuals who rely on the internet. Traditional detection methods have become increasingly ineffective in addressing these challenges, necessitating the development of new and innovative solutions. This paper proposes using Convolutional Neural Networks (CNNs) to detect DDoS and SQL injection attacks. Our paper proposes a Convolutional Neural Network model with a multi-layer neural and relu activation function optimizer that reaches a higher degree of precision than previous Deep Learning models. In this study, we tested the relatively new dataset CIC-IDS-2018, which contains different types of attacks. With this dataset, our model achieves an unprecedented accuracy of>96%, minimizing computational time. [ABSTRACT FROM AUTHOR]

The enormous benefits and applications of Image classification and recognition are umpteen. Machine learning algorithms and Deep Neural Networks are like windfall to fathom the objective proficiently in streamlined manner. The prevalent improvement in this technology is that these networks do not call for any prior blueprints in terms of algorithms as prerequisites. The presented paper is an attempt to create a Convolutional Neural Network from scratch to classify the images from the well-known dataset – Cats and Dogs into their relevant baskets. Manifold open source accessible approaches to amplify the efficiency of the network are no more onerous. Further, data augmentation technique boosts the efficiency tremendously by extending the dataset with reoriented features from the same images. To untangle the same problem, Transfer Learning is also a compelling technique in which all the layers, neurons in each layer, weights of each neuron and all other parameters are predefined and we can amend the output layer as per the classes in the respective problem statement. In the present paper, we have tried to obtain a comparable efficiency with a significant reduction in parameters. [ABSTRACT FROM AUTHOR]

This paper describes an Artificial Neural Network (ANN) based speed estimation of BLDC drive using the three-phase current of the motor. The star point of three parallel resistors is used as the artificial star point of the motor. The comparators are connected to motor terminals and star point of resistors. This circuit determines the zero crossing of the back-EMF of the motor phases. Using zero crossing detection of the back-EMF with the estimated speed from the ANN, precise control of the BLDC motor is achieved, without the need for a sensor. The ANN is an offline trained network, trained using data obtained from simulations. The network is validated against different Inputs. The network is precise but at the same time simple enough to be implemented on a low-cost processor. The results are validated with simulations done in MATLAB using the trained neural network The findings and applications of neural networks to control the speed of PMSM are very well presented in the referenced papers, but the same for BLDC motors, especially those used in electric vehicles, have not been explored yet. The presented paper attempts to address the challenges and advantages of adopting this sensor-free approach for speed control in EVs. [ABSTRACT FROM AUTHOR]

Handwriting Detection is a computer's approach or ability to access and interpret understandable handwritten source inputs such as paper documents, touch screens, images, etc. Recognition of handwriting is characterized as the task of turning a language into its symbolic representation represented in its spatial form of graphical marks. The progress is base on artificial neural networks, which is an artificial intelligence area of study. To build a Handwriting character recognition system, similar techniques and methods are used. When the data is implemented and translated into digital form by an OCR (Optical Character Recognition) device, it can be edited, manipulated, and processed in a computer-understandable form. For extracting the characteristics of the handwritten alphabets, a method of feature extraction is implemented and then we use the data to train the artificial neural network. It contributes enormously to the development of an automation process and in various applications will enhance the interface between man and machine. [ABSTRACT FROM AUTHOR]

Agriculture is essential for everyone to promote sustainable development, the farming, which combines image processing, artificial intelligence, Deep learning, and Internet of Things (IOT). World population incensing every day. Due to the rising demand in the Agriculture industry, the need to collectively improve a plants and growth its field is very useful. In this paper, it is important to maintain the crop during its initial time, and also at period of harvesting. The image processing and artificial networks are used as a different techniques to maintain the detecting the diseases on the leaves and correct time to harvesting. When we take images with help of drones, the images are divided and changed to disease described three things vectors namely the first one is color, one more is texture and morphology. The vectors morphology gives 95% accuracy and its give more compare to other two vector features. This research paper proposed effective and useful algorithms for detection of disease with help implementation of Artificial Neural network algorithms using MATLAB. Detection of leaf or plant diseases with some manual techniques are requires a lot of work by maintaining a huge farm of crops, and it's very early stages it detects different types of symptoms to different diseases on plants, when the displayed on crop leaves. In this research paper survey on various disease classification techniques that can be for plant leaves diseases detection. For this purpose Artificial Intelligence, Neural network algorithms and back propagation techniques for adjustment of training data sets. [ABSTRACT FROM AUTHOR]

The wireless networks of the future generation are evolved as complex systems due to broadening in service prerequisites, application heterogeneity and networking of gadgets. In recent times, the major step forward of machine learning techniques is deep learning. However, in wireless/heterogeneous networks, the deep learning application for network traffic control is relatively new. The key disputes in the wireless backbone networks since the advancement of wireless networks are resource allocation and efficient network traffic control such as routing. In larger scale of networks and tangled radio environments, the method of adding intelligence to wireless networks is achieved by Deep Learning (DL). The tangled wireless networks supported with several nodes and their quality of variable link can be investigated by Deep Learning. This paper is presented with a perception of harnessing the next generation communication networks with the artificial neural networks. This work helps the readers to explore the unsolved issues to pursue their research and deeply understand the wireless network design with DL based state of the art facilities. In this work, we integrate the deep learning and wireless networking research with a widespread survey. Finally, this paper summarizes the disputes and benefits of acquiring ML and AI for next generation wireless systems. [ABSTRACT FROM AUTHOR]

Considering the linear limitation of the traditional PID controller, a scheme combining the fuzzy system with BP neural network was proposed, that was, and the fuzzy neural PID controller. At the same time, in order to optimize the accuracy of parameter, this paper combined the chaos optimization with ant colony algorithm, namely, chaotic ant swarm (CAS). It, which took the error integral performance index as the objective function and the value range of design parameters and the minimum gain and phase margin as the constraint condition, established the optimization mathematical model, further optimized and trained the weight and bias error of the fuzzy BP neural network, and reduced the error caused by the fuzzy neural network used for function approximation. Comparing the traditional fuzzy PID controller and the PID controller based on chaos genetic algorithm (GA) with the PID controller designed in this paper, the simulation results show that the dynamic performance and steady-state accuracy of the system are improved well [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motor is used widely in industry, the performance requirements wouldn't be met by adopting traditional PID control in some of the occasions with high requirements. In this paper, a hybrid control strategy - nonlinear neural network PID and traditional PID parallel control are adopted. The high stability and reliability of traditional PID was combined with the strong adaptive ability and robustness of neural network. The permanent magnet synchronous motor will get better control performance when switch different working modes according to different controlled object conditions. As the results showed, the speed response adopting the composite control strategy in this paper was faster than the single control strategy. And in the case of sudden disturbance, the recovery time adopting the composite control strategy designed in this paper was shorter, the recovery ability and the robustness were stronger. [ABSTRACT FROM AUTHOR]

Driving while drowsy is one of the significant causes of road accidents, and detection of drowsiness of drivers is necessary to minimize such accidents. There are many methods proposed for drowsiness detection. In this paper, we present the details of a drowsiness detection system using deep learning techniques with ECG as its input. Continuous wavelet transforms (CWT) are applied on the input signal, segmented to 1-minute duration, and then it is converted into scalogram images to be fed as input to deep neural networks (DNN). We use pre-trained transfer learning models, AlexNet, ResNet-50, and VGG-19 for this work. The best results were obtained for Reset-50 with an accuracy of 88.31% without dropping the final layers, which is an improvement of 4.5% over the spectrogram-based implementation. This paper evaluates the effectiveness of wavelet-based features, and we note that the time-frequency domain features outperform. To check the robustness of the net-work, a second model is implemented by dropping the final layers, and the validation accuracy of AlexNet is 51.5%, ResNet-50 is 67.2%, and VGG-19 is 81.8%. [ABSTRACT FROM AUTHOR]

This paper presents a proposed design and investigation of an impressed current and Single-Ended Primary Inductive Converter (SEPIC) converter based on an artificial neural network to assess the potential difference required to control cathodic protection system. The cathodic protection methods proved their effectiveness in the metallic equipment protection in the industrial applications that contain liquids and that are buried in electrolyte mediums against the electrochemical corrosion process, where D.C power supply plays an important role in the cathodic protection system. The proposed design consists of an intelligent d.c power supply and a group of anodes. This model has been adjusted to be able to equalize the normal voltage difference between the surrounding medium and the metal part being protected. The proposed design has been tested and investigated the cooling water carrying pipeline of AL-Quds electrical power station as a part of the unified Iraqi power network. The protection voltages and currents were calculated with a permissible tolerance based on; the pipeline characteristics, its coating, and the buried soil along the pipeline length to ensure better performance of protection. The obtained experimental and simulation results showed high effectiveness and accuracy by maintains the negative potential of the pipeline within the permissible and standard limits of the Iron metal of the pipeline. [ABSTRACT FROM AUTHOR]

Nowadays, regular intake of nutritious foods is essential for maintaining a healthy eating routine and avoiding obesity in the human body.. In this paper, a novel framework dependent on AI that naturally performs exact grouping of food pictures and gauges food credits. In the preparation section of the model system, it proposes a profound learning model that includes a convolutional neural organization that orders food into explicit classifications. The principle reason for the proposed technique is to improve the exactness of the pre-preparing model. The paper plans a model framework dependent on the customer worker model. The customer sends a picture location solicitation and cycles it on the worker side. Support Vector Machine (SVM), Artificial Neural Network (ANN), and Convolution Neural Network (CNN) are the three classifiers that have been modified to analyze the framework's enhanced precision. Exploring various avenues about a variety of food groups, each with a large number of images, using Artificial Intelligence (AI) to achieve higher grouping precision. [ABSTRACT FROM AUTHOR]

This article describes a method for evolvement predicting of an emergency zone on the example of a forest fire using neural networks and applying the Hurst exponent to increase the accuracy and reduce the computational complexity of the algorithm. The inputs in this method are a set of environment state maps and the fire characteristics. The lack of a sufficient number of maps and a large amount of available data has a negative effect on the forecast accuracy and the overall system performance. It proposed to divide the fire area into sectors and perform the forecast by sectors with an accuracy that depends on the sector behavior characteristics. The sector behavior characteristic in this paper presented by the persistence analysis of the sector vectors, and be replaced by any other indicator. Persistence indicates trend stability in the behavior of the time series and, accordingly, the forecast reliability. In the case of low forecast reliability, the sector is iteratively divided into segments, which are analyzed and characterized. In this case, forecasting carried out for each sector and segment, which generally increases overall forecast accuracy. The paper presents the key mathematical calculations and the results of experimental studies. [ABSTRACT FROM AUTHOR]

The optimization of energy consumption is important, especially in building these days. The energy consumption of Mechanical – Ventilating – Air conditioning (MVAC) systems always the largest proportion in total energy consumption. One of the most effective approach is the estimation and prediction as close as possible to the real operation mode of the MVAC in reality. However, the tasks of energy consumption estimation request large efforts, time consuming but low precise results. Most designers roughly calculate the energy consumption at first and the using automation control to manipulate the system operation later. With the help of the newest, cutting edge technology in soft computing like ANN, ANFIS and intelligent optimization methods, the designers now can apply these technologies to achieve the reliable, rapid, precise results for the further convincing designs. In this paper, the authors simply apply the Multi Layers, Back Propaganda Neural Network (MLB ANN) to estimate the building Heating Load (HL) and Cooling Load (CL). The Mean Square Errors (MSE) and Regression R Values (R) are used as the indicators for ANN with the dataset provided open access by UCI. The results prove that ANN can be reliable approach to predict energy consumption for complex, multi design option building. [ABSTRACT FROM AUTHOR]

This paper proposes an ANN based adaptive Auto Reclosing AR which integrated of an operation of distance measuring relay in HV Over Head Transmission Line OHTL. This work is brought to enhance stability synchronism, and reliability during the transient faults of OHTL. Thus, it leads to increase the continuity of the electric power system, especially of hydropower plants are connected to utility grid. This work are used Matlab\Simulink environment to design and simulate a single-phase auto-reclosing, which depends on the Artificial Neural Networks ANN integrated with a distance relay DR. In addition, the simulation model consists of Boolean algebra circuits, and timers are utilised to build Auto-reclosure of a circuit breaker in each phase. The ANN is here trained to recognize angle values of actual impedances (in the positive sequence only) in which it can classify which phase exactly is line to ground fault. The simulation results show the ability of this combination to distinguish among various fault types and giving multi pulses-shot of tripping and reclosing for the specific circuit breaker with a successful mechanism. In this paper, a realistic system with real parameters was represented by the important transmission line (Mosul Dam-East Mosul), which currently is only the main and alone line that connects the northern Iraq network with a national grid. [ABSTRACT FROM AUTHOR]

There has been a lot of research in recent years into employing optimization approaches to improve artificial intelligence (AI). In this research review paper, we have compared and contrast some of the most usual optimization algorithms, such as Backtracking searching method (BSA), the genetic algorithm (GA), particle swarm optimization (PSO), an artificial bee colony (ABC), and the genetic algorithm, which are all artificial neural networks (ANNs)-based algorithms. The number of recently developed optimization techniques, such as the lightning search algorithm (LSA) and the whale optimization algorithm (WOA) are also compared. All the techniques are categorized based on randomly generated populations. To produce the best possible results, the processing parameters are set within a certain range according to their knowledge. This review paper emphasis on applying optimization techniques to improve the accuracy of simply adjusting the parameters of the neural network. This review paper also presents some results for enhancing neural network performance using various optimization techniques like PSO, GA, and ABC optimization methods to get optimal processing parameters, such as the number of hidden layers, neurons and learning rate etc. The findings of this research review paper will aid in the improvement in the quality of plastic injection molded parts. [ABSTRACT FROM AUTHOR]

Artificial intelligence is the simulation of human intelligence and seeks to mimic human knowledge acquisition by using planning, reasoning, and research. Sometimes AI faces challenges due to a large set of rules, and growing demand for learning and searches optimization. Computational intelligence is a form of computing modeled on the methods by which humans learn. Artificial neural networks system learns by being given different image examples, and contain many layers of learning, some of them more complex than others. This paper presents a comprehensive survey on the various artificial neural network open source software for implementing computational intelligence as they seem to be effective in simulation, prediction, building, and studying artificial neural networks. This paper helps the researchers to find which software they should prefer by knowing their requirements. [ABSTRACT FROM AUTHOR]

The constant change in the components and variables of the economic environment is one of the most important phenomena that characterize this environment at present, and these changes have made the goal of survival in the market a priority due to the prevalence of financial distress in companies. The main purpose of this paper is to test the impact and effectiveness of the model of artificial neural networks and Naive Bayes technology and to see how accurate they are and their ability to classify companies. In this paper, the information of the companies listed on the Iraq stock exchange for 2017, which represents 36 companies with high financial distress, 20 companies with medium financial distress, in addition to 43 non-distressed companies for a group of 99 companies. According to the experimental results, the model of artificial neural networks gives the highest rating accuracy of 96.97% and an error rate compared to Naive Bayes, which gave a rating accuracy of 74.74%. [ABSTRACT FROM AUTHOR]

Associative Hopfield Memory is a very useful form of Artificial Neural Networks. This paper outlinesan optimization relaxation approach for signature verification based on the Hopfield neural network (HNN). The standard sample signature of the customer is cross matched with the one supplied on the check. The difference percentage is obtained by calculating the different pixels in both the images. The network topology is built so that each pixel in the difference image is a neuron in the network. Each neuron is categorized by its status, which in turn signifies that if the particular pixel is changed. The network converges to unwavering condition based on theenergy function which is derived in experiments. The Hopfield‟s model allows each node to take on two binary state values (changed/unchanged) for each pixel. The performance of the proposed technique is evaluated by applying it in various binary and gray scale images. This paper contributes to finding an automated scheme for verification of authentic signature on bank check. The derived energy function allows a trade-off between the influence of its neighborhood and its own criterion. This device is able to recall as well as complete partially specified inputs. The network is trained via a storage prescription that forces stable states to correspond to (local)minima of a network "energy" function. [ABSTRACT FROM AUTHOR]

Facial expression is an important basis for judging human emotions. The driver's mood can be accurately determined by recognizing their facial expression. Because the facial expression data set based on ordinary life scenes cannot accurately show the relationship between the driver's facial expression and his psychological state in the real driving environment. This paper uses 1000 facial expression images of drivers based in the real driving environment. These images include 6 different facial expressions to express 6 different emotions. This paper uses Edge Impulse to train several neural network models capable of recognizing these 6 different facial expressions and to realize the purpose of judging the driver's psychological state. From the experiment results, the MobileNetV2 model used has the highest accuracy rate of 99.1% on training data and 97.4% on testing data. This shows that the lightweight neural network model MobileNetV2 can accurately recognize the driver's facial expressions in the real driving environment. [ABSTRACT FROM AUTHOR]

A modeling method for color probability map using an artificial neural network architecture is examined in this paper. An optimal architecture of the artificial neural network is determined after evaluating various structures extensively. For evaluating the performance of a specific structure of the Multi-Layered Perceptron type Neural Network (MLPNN), a measurement for evaluating the quality of a Color Probability Map (CPM) image is proposed in this paper. An optimal architecture of MLPNN for CPM is found based on the quality measurement. The resulting MLPNN model trained with CPM data is applied to traffic sign datasets. The results show that the architecture of MLPNN obtained in this experiment can detect traffic signs very efficiently when compared with the look-up table method with improved accuracy. [ABSTRACT FROM AUTHOR]

Based on nonsubsampled contourlet transform (NSCT) and modified pulse coupled neural network (MPCNN), the paper proposes an effective method of image fusion. Firstly, the paper decomposes the source image into the low-frequency components and high-frequency components using NSCT, and then processes the low-frequency components by regional statistical fusion rules. For high-frequency components, the paper calculates the spatial frequency (SF), which is input into MPCNN model to get relevant coefficients according to the fire-mapping image of MPCNN. At last, the paper restructures the final image by inverse transformation of low-frequency and high-frequency components. Compared with the wavelet transformation (WT) and the traditional NSCT algorithm, experimental results indicate that the method proposed in this paper achieves an improvement both in human visual perception and objective evaluation. It indicates that the method is effective, practical and good performance. [ABSTRACT FROM AUTHOR]

Despite advances in technology Handwritten text is still considered as a method for correspondence and recording data in everyday life. Offline handwritten text recognition is an infamously challenging problem in Artificial Intelligence. Recent studies show great potential of Neural Networks for handwritten text recognition segmented at word level. In this paper, a prototype is presented for offline handwritten text recognition using deep neural networks. Preprocessing techniques like data augmentation and line-level segmentation enrich the input images and hence benefits the classifier. The proposed approach performs thorough feature engineering on preprocessed input images and propagates them through a three layered architectures comprising of CNN (Convolutional Neural Network), LSTM(Long Short Term Memory) and CTC(Connectionist Temporal Classification). [ABSTRACT FROM AUTHOR]

Automatic speech recognition is a research field for enabling computers to receive voice feedback from humans and to translate it with the greatest probability of correctness. Speech is the easiest way to communicate the message in basic context. Speech recognition is referring the allowing of machines to perceive and accept speech information from humans for the greatest probability of accuracy. Speech recognition will play an ever more important role in the future of human with machine interfaces. Arabic speech recognition (ASR) is a most complex job and has received little attention than other languages. In this paper we reviewing the most important deep learning methods used in speech recognition in Arabic language. Through the search the main finding in this review that the Arabic is one of the most spoken languages and least highlighted in terms of speech recognition. One of the emerging techniques is using neural networks with deep learning for speech recognition. Deep Neural Networks (DNNs) have reached to suitable performance, Recurrent Neural Networks (RNN) is one of the best models applied for sequential data and convolution neural network (CNN) has obtained a best accuracy rate in speech recognition. [ABSTRACT FROM AUTHOR]

Reliability, efficiency, and cost-effectiveness of smart grids are enhanced with power demand softening by means of efficient load management in electric vehicles. In such initiatives, the involvement of EV users may reduce due to the lack of adaptable user-centric approaches. During the connection sessions, the EV charging time is determined using a deep learning algorithm-based smart charging strategy proposed in this paper. Here, the total energy cost of the vehicle is minimized by making charging decisions considering demand time series, pricing, environment, driving, and other auxiliary data. The memorization technique is used for the estimation of the optimal solution of the existing connection sessions in the initial stage. The deep learning models are trained with this existing data and optimal decisions to make suitable decisions in real-time scenarios where car usage or future energy price values are undetermined. A significant reduction in the charging cost is observed by training the neural network with the proposed model. The results obtained are compared to the optimal charging costs computed and are found to be closely similar. [ABSTRACT FROM AUTHOR]

Timely analysis and recognition of leaf diseases in plants ensures good quality and productivity of crops in the field of agriculture. Recognition of rice leaf diseases is one of the biggest challenges for the farmers due to incomplete awareness regarding latest sophisticated techniques in the field of leaf disease identification. Much of the growth of rice is disturbed due to the prevalence of rice leaf diseases. Earlier detection of rice leaf diseases done manually by farmers which is very laborious and time consuming. However, requisite of automatic identification of rice leaf diseases helps farmers to save their agricultural crops more efficiently. Innovations in the field of computer vision and deep learning meets the expectations and proves to be the best solution for classification of rice leaf diseases using convolutional neural networks using samples of rice leaf diseases. In this review paper, the major focus is on performance analysis of detection of rice leaf diseases based on the architectures employed. [ABSTRACT FROM AUTHOR]

In today's world, a means to communicate with virtually no loss is highly essential, with innovations in SDR and improvements in cognitive computing, we could achieve a better way of communication than existing ones, and newer algorithms make it less hard to develop and test these systems. The cognitive radio, which allows dynamic allocation of the spectrum, could serve as a viable solution to congestion in the network and paves the way for dynamic spectrum allocation. The Cognitive Radio may be implemented, may vary with location and architecture, but the most common Software-Defined Radio could be converted into Cognitive Radio CR by a simple predictive and decisive algorithm. Here, we aim to analyze one of the techniques that could improve spectrum sensing of SDR implementation of CR. SDRs can tune to any wireless standard with modulation type, bandwidth, and variable carrier frequency; This paper proposes a Goodness of Fit technique to re-enforce and predict better channels for transmission or reception. [ABSTRACT FROM AUTHOR]

The demand for improved Power Quality (PQ) in distributed power systems has raised due to the increase in sensitive irregular load integration and PV system-based power generation. In order to upgrade the entire system with developed PQ, a Unified Power Quality Conditioner (UPQC), a unique power device is proposed in this work. Using a shunt compensator, the PV system delivers active power to the load. Photovoltaic (PV) systems is used for uninterrupted energy flow to the load. The output voltage obtained from PV module is irregular, so that a DC-DC converter is required to step-up/step-down the output. In this paper, Luo converter is used in which the converter operation is regulated using Grey Wolf Optimisation (GWO) based Proportional Integral (PI) controller. PWM generator provides necessary pulses to the converter which in turn provides constant supply to the load. The battery converter is used for charging and discharging of the batteries in both direction for further use. To overcome harmonic distortion observed in the input AC source, an Artificial Neural Network (ANN) aids in the operation of shunt active filter of the UPQC. The proposed ANN controller reduces control complexity and effectively overcomes power quality issues. The AC source output is fed as input to ANN which generates reference current. PWM generator with hysteresis control converts the reference current as PWM pulses received from ANN and supplies to shunt converter of UPQC. Then the DC supply is converted to AC by reducing harmonic distortion using LC filter and it is fed to the load. On the basis of MATLAB simulations, the effectiveness of the proposed PV-UPQC configuration in eliminating load current harmonics and source voltage fluctuations thereby improving PQ is analyzed. [ABSTRACT FROM AUTHOR]

The quality of food is significantly influenced by temperature. In Cold Storage facilities, it is essential to maintain optimal chamber freezer conditions within a specific temperature range. To achieve this, regular automatic measurement of the freezer temperature becomes necessary, although it can be time-consuming, it ultimately enhances crew efficiency. Hence, the implementation of an automatic system for measuring freezer temperature is significant in alleviating the workload of the crew when it comes to monitoring freezer temperature. The boosting demand for viable and efficient cold storage has led to the advancement of new techniques and technologies. Vintage refrigeration systems are known for their high-energy consumption and substantial aid to the release of greenhouse gases. This proposed paper introduced a novel optimization tactic for cold storage, incorporating Internet of Things (IoT) technology and solar panels to minimize both energy expenses and the environmental influence of greenhouse gas emissions. The proposed system comprises essential components, including a DHT11 temperature and humidity sensor, a gas sensor, and a microcontroller, responsible for data collection and refrigeration system regulation along with a battery connected to a solar panel. The power supply section of proposed system is derived from a solar panel array, utilizing daytime sunlight and storing surplus energy in a battery for nighttime operation. Real-time data analysis enables prompt adjustments to the refrigeration system, ensuring optimal storage conditions for the products. A remarkable advantage of the proposed system is its self-governance from the electrical grid, providing independence in operation. This holds significant advantages in regions with unstable electricity provision or areas burdened by expensive power tariffs. These results underscore the considerable potential of combining IoT technology and solar panels to optimize cold storage systems, contributing to a more sustainable future. Moreover, a machine learning based approach has been developed which can classify ripe and unripe fruits/vegetables. Statistical feature extraction is followed by classification using a deep neural network. It has been shown that the proposed approach achieves an accuracy of 97% thereby rendering high classification accuracy. In summary, our results exemplify the immense potential of combining IoT and solar panels to optimize cold storage systems, fostering a more sustainable future. Furthermore, the automated classification mechanism would facilitate the continuous monitoring of fruits/vegetables thereby reducing the chances of spoilage. [ABSTRACT FROM AUTHOR]

To create, the deep reinforcement learning technique combines deliberative reasoning with visual perception. It does this by using an end-to-end learning approach to merge the reinforcement learning's decision-control abilities with convolution neural networks' perceptual abilities. It's utilized frequently. It has been applied to tackle issues with complex decision control and high-dimensional visual input ever since it was created. MPC works best when I understand what is required of me in terms of authority and responsibility as well as the state constraints that always exist in real-world scenarios, which validates its application in this case. DRL is then used to create the algorithm, which includes observation states. By utilizing the reward function, network structure, and parameter optimization for a 2D environment, the laborious 3D environment effort is avoided. We use the tried-and-true method to obtain the converged network's parameters, such as the deep neural networks (DNN) weights and biases, in a simple 3D environment. This research gives a general overview of how the agent should be represented in technologies employing applied Markov decision processes. This paper presents a proof-of-concept application scenario to illustrate the effect of multichannel Kalman filtering on the reduction of uncorrelated noise in magnetotelluric data. We evaluate our strategy using a variety of maps that depict the kinds, locations, and numbers of obstacles. The experimental findings show strong generalization, the superiority of G2RL over prior technologies, and its capacity to perform almost as well as entirely despite relying on distributed architectures and modern benchmarks at their core. [ABSTRACT FROM AUTHOR]

Devanagari is an ancient script that is used to write Hindi, Nepali, Marathi, Maithili, Awadhi, Newari, and Bhojpuri, among other Indo-Aryan languages. Thousands of individuals in India use this script to write documents in Marathi and Hindi. Indian mythology is based on this script. Because of the script's prominence, handwritten Devanagari character identification has grown in popularity over time. Handwritten recognition of languages such as English has received a lot of attention, but Indian languages written in the Devanagari script are also a rich source of information. Most of the work on this problem statement has been done either using deep neural networks like CNN at its heart coupled with other machine learning techniques like SVM,Random Forest etc. In this paper we are utilising a recently introduced transformer model for computer vision known as Vision Transformer for the task of Devanagari Character Recognition. We have also compared our model with various pretrained CNN-based architectures like ResNet50,VGG16 and InceptionV3 and ViT has outperformed these models both on DHCD dataset and the modified slightly more complex version of it with accuracy scores of 99.68% on the original testing dataset of the DHCD dataset and accuracy score of 96.55% on the modified(blurred) slightly more complex version of the original testing dataset. The ViT model thus generalized better than standard CNN-based models on the problem of Devanagari Character recognition. [ABSTRACT FROM AUTHOR]

Sustainable River water quality is to be maintained and it is the responsibility of all concerned stakeholders. The prime objective of Hydrology Project of Government of India is to ensure the river water quality and quantity by continuous capacity building and development of relevant both structural infrastructure (such as water quality laboratories) and cyber infrastructure (such as Decision Support Systems). Engineers and scientists working in the area of water quality need to be open minded with the ever uplifting attitude of lifelong learning to upskill with the ever-evolving relevant software and hardware technologies. Dissolved oxygen in rivers is essential for sustainability of aquatic life. Twelve year annual mean values from 2003 to 2014 of Dissolved Oxygen, pH, Electrical Conductivity, Nitrates, Biochemical Oxygen Demand and Temperature at nine stations along Krishna River are considered. Four types of models with varied input variables are developed in Multiple Linear Regression (MLR) using Microsoft Excel and Artificial Neural Networks (ANN) using nonlinear autoregressive model Artificial neural network identification model (NARX-ANN) to predict the dissolved oxygen. Levenberg – Marquardt Method (LMM) algorithm is used. The performances of both methods are compared. This paper takes care of Goal 6 of United Nations Sustainable Development of ensuring availability and sustainable management of water and sanitation for all as river water quality prediction is involved. ANN model with Mean Square Error (MSE) of 0.01 and R value of 0.987 is adopted. MLR model with R value of 0.574 is adopted. [ABSTRACT FROM AUTHOR]

In this paper, we aim to provide a method based on deep learning to help clinicians diagnose lumbar spine MRI for stenosis more efficiently and with instruments that produce noisy images to make maximal usage of medical resources. To achieve the same, this paper evaluates the effect of various MRI noises, such as Gaussian, salt and pepper, speckled, etc. in boundary delineation of stenosis in MRIs of the lumbar spine using recently introduced semantic segmentation algorithm based on deep neural networks. A U-Net model was trained for segmentation and its performancewas tested on various MRI images with added noise. The proposed methodology adopted in this paper is successful in producing a considerably good performance with higher scores, which helps in identifying Lumbar Spinal Stenosis in a more efficient manner. [ABSTRACT FROM AUTHOR]

A key component of tumour diagnosis, treatment planning, and creating an outcome for assessing brain tumours is highly accurate and dependable segmentation. Magnetic resonance imaging (MRI) is a tool used in medicine to help find very small abnormal growths in any human body part. Deep neural networks (NNs) and machine learning methods are efficient at segmenting 2D images, but it is challenging for NNs to distinguish significant organs from 3D medical MR images. The segmentation for tumour detection includes a number of processing techniques thatfall into the categories of pre-processing, segmentation, optimization, and feature extraction. The study focuses mainlyon 3D-based Convolution neural networks (CNN), ANN (Artificial Neural Networks), SVM, and Multi-class Support vector machines for deeper segmentation. In order to lessen the computational burden of processing 3D medical scans,this survey paper will examine recent developments in image segmentation and image classification based on effective and efficient processing of tumor-infected human brain MRI adjacent image patches that can pass through the network. The focus will be on gliomas. More discriminative 3D NNs and computational machine learning are used to process the input images at different scales simultaneously. This article also suggests the state of the art in tumor-based imageprocessing using deep learning models for tumour segmentation and detection. [ABSTRACT FROM AUTHOR]

It's possible that our Human Visual System (HVS) only sees a particular section of an image instead of the whole picture. This phenomenon is a trending and demanding topic in the fields of computer vision research field. For predicting co-salient features many deep learning algorithms have recently been used. This paper examines the visual saliency prediction ability of five cutting-edge deep CNN models namedResnet-50, InceptionResNet-v2, VGG-16, Xception and MobileNet-v2. By using the SALICON dataset, we used five deep learning pre trained models that are used to suggestco-saliency maps on four popular datasets: DUT-OMRON, TORONTO, CoSOD3k, MIT103. According to the data, the ResNet-50 model outperforms in obtaining desirable results and assisted in giving more accurate results that closely resembles the desired output. [ABSTRACT FROM AUTHOR]

In recent times, several fields that exist in engineering have been heavily dominated through the integration of artificial intelligence, however machine vision systems are simultaneously being studied and observed, they encapsulate certain tools that help in deriving technical information such as the working, errors and relative faults. Such systems play a vital role in manufacturing production lines, where important factors such as efficiency and productivity are sought to be at optimum levels. This paper captures the essence of using convolutional neural networks and also the addition of VGG-19 that are used in the detection and inspection of the various type of bottle caps. The respective bottle cap classes that have been pre-determined include normal cap, no cap a misplaced cap. The implementation of this system should enable us to determine whether a bottle with a specific cap class should be rejected or accepted. High metrics of precision and accuracy of the system should be observed and beneficial to the existing and upcoming processes. [ABSTRACT FROM AUTHOR]

Regression is a powerful technique for predicting a single scalar value with a high degree of certainty. A regression model requires a dataset that consists of only numeric features. However, datasets frequently contain both numeric and categorical features. This paper sets out to study several different text encoding techniques, which can solve this problem by transforming a certain text value into a corresponding numerical value that is statistically sane in relation to the dataset. The scenario we study is a neural network regression model predicting support case durations. Many of the dataset's features are indeed categorical such as issue description, team, username, severity and impact among others. The most challenging aspect of the encoding is the resulting change of the dimensionality of the dataset. Every encoding method affects the dimensionality in various degrees depending on the feature cardinality. This result is the main challenge for the tuning of the neural network hyperparameters. We must make such a setup that can robustly handle the altered dataset. The paper compares five different approaches for encoding: one-hot, hashing, binary, target and entity embeddings. The hyperparameter settings for each approach are presented by using common neural network performance metrics and a baseline neural network setup. We can conclude that a moderately increased dimensionality can enhance the model's predictive power as observed in the case of the binary and the hashing encoder [ABSTRACT FROM AUTHOR]

Fused deposition modeling (FDM) has many different levels of parameters hence prediction method is required for FDM users. The main aim of this paper is to predict the mechanical strength of the components before fabrication of FDM process. In this paper, the most influenced FDM process parameters of Layer thickness, Infill density, print speed, temperature and build orientation were considered for prediction of tensile strength of the fabricated part. The tensile test specimen (ASTM-D638) was fabricated by FDM process with PLA (Polylactic Acid) material, based on the central composite design. The actual tensile strength values were obtained by conducting a test on Universal Testing Machine (UTM) for the PLA material. The results data were used to develop the mathematical models for the prediction of tensile strength of fabricated part from response surface methodology (RSM) and analysis of variance (ANOVA). Similarly, ANN tool was used to predict the tensile strength of the specimen with the aid of 33 experiments and result data. The predicted values from the results of RSM, ANN, ANOVA were compared with the actual tensile strength. The ANN technique gives the minimum value of percentage deviation in Error (1-2%). [ABSTRACT FROM AUTHOR]

This paper reviews the effect of weather parameters on natural degradation of solar reflectors mirrors exposed in Morocco. Outdoor aging tests of four types of solar glass mirrors and two polymeric reflectors have conducted in two different Moroccan sites (desert and sea side sites) for about 3 years. The neural network modeling approach was adopted to model the effect of weather factors on mirrors degradation. The measurements of meteorological and environmental factors formed the neural network inputs while mirrors degradation (loss of reflectance, corrosion rate, backside paint yellowness) measured during the exposure test forms the model output. This paper focus on modeling results obtained for reflectance loss due to dust deposition phenomenon. [ABSTRACT FROM AUTHOR]

Tiny electronic component detection is mostly suffered from compact size and multiple interferences of environment. To solve this problem, this paper presents an object detection method based on convolutional neural networks (CNNs). This method combines residual blocks in residual networks with CNNs. The CNNs can reduce the dimensions of the image, and the residual block in the residual network can train deeper neural networks, and then use the successful application of CNNs in computer vision to improve the recognition rate of components and improve the efficiency of automated processing of tiny electronic components. After the hyper-parameter adjustment, this method achieved 95.63% accuracy on our test set. [ABSTRACT FROM AUTHOR]

In this paper we study travelling wave solutions of the viscoelastic Burgers’ equation. RTD-based Cellular Neural Networks (CNN) model of this equation is presented. Using such realization new wave profiles of the travelling wave solutions are obtained. [ABSTRACT FROM AUTHOR]

Agriculture's production is heavily impacted by the state of the economy. Plant disease is increasingly common in agricultural settings and is now easier to detect as a result of the previously described issue. It is receiving more attention due to the monitoring of crops in a wide range of settings. Farmer transitions between different disease control strategies and encounters significant challenges. We are able to identify or detect leaf illnesses, which is how surveillance and monitoring experts usually find them. If the appropriate control measures aren't followed, the plants suffer a major consequence, which symbolizes how the quality of the plants' production will be impacted. The adoption of a mechanical approach or method for identifying diseases is effective and beneficial since it lessens the burdensome work of surveillance in the vast cultivation. The early stages of plant illnesses allow us to identify the signs even before they manifest on the plant's leaves. The early stage denotes the starting period which is one or two days after affecting the disease. The paper's primary objective is to use ResNet50 for early detection of plant disease. ResNet50 has been successfully applied to plant disease image classification tasks and has shown promising results. ResNet architecture, with its deep layers and residual connections, has the ability to capture intricate patterns and features, which is useful for identifying the differences in plant images. The results proved that the ResNet 50 has better accuracy, precision and recall than Support vector machines (SVM), artificial neural networks (ANN), Convolutional neural networks (CNN) for plant disease prediction. [ABSTRACT FROM AUTHOR]

Chronic Renal Disease (CRD) is a significant global health issue, with an increasing number of people affected by the disease. Accurate and timely prediction of CRD is crucial for effective treatment. In recent years, machine learning approaches have gained popularity as a tool for CRD prediction due to their high accuracy and efficiency in predicting disease progression. In this paper, we have proposed a Chronic Renal disease prediction using Artificial neural network with Feature selection Technique (CRAFT) that is a combination of Artificial Neural Network with Principal Component Analysis for the prognosis of CRD. The performance of the proposed model was evaluated using various criteria and is compared with five commonly used machine learning classification approaches, namely K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Naïve Bayes. Our results show that CRAFT outperforms all the other methods, with a higher accuracy and AUC-ROC score. This study provides valuable insights for researchers interested in leveraging machine learning classification approaches for the prognosis of CRD, with ANN being the most effective method. [ABSTRACT FROM AUTHOR]

Image based Steganography has been in existence for a very long time and Kurak and McHugh were one of the earliest who proposed a method in which the secret data or image is embedded into the fourth least significant bit. But this method was not of much use since the intruder can easily access the hidden image or data since they know exactly the position of the hidden data. This is an example to show that this is an area in which security and efficiency can be improved by using different methodologies. This paper is proposed to build an architecture to hide single or multiple images also called hidden images which are the inputs to be hidden inside an outer image and then the combined image is converted into an Stego image, and the image looks like the outer image except that it is filled with the bits of information from the hidden image. From this Stego-image can be retrieved the original outer image using disclosure component which is the reveal network. A deep learning model has been developed and trained which consists of two layers namely preprocessing layers and embedding layers which will hide the data or image inside the outer image and convert it into a Stego image. Then from this Stego image the hidden data is extracted using reveal network. Only this reveal network can extract the data and no intruder can extract it since the embedding is done by the model and it does not use same or fixed pattern. This method helps to transmit sensitive information in a more secure and efficient manner. The extracted image from the outer image has an accuracy of 95 percent only for the first image and it decreases for each added hidden image and the MSE value is 0.01 and it increases for increasing number of hidden images. The model has a F1 score of 0.96. [ABSTRACT FROM AUTHOR]

India ranks second in production of cement. In order to produce one ton of cement about one ton of Co2 is released into atmosphere. As per Global Cement and Concrete Association, Cement is the third highest contributor of Green house gases in the atmosphere. In order to decrease the negative impacts of cement in the atmosphere a new type of concrete is evolved by conducting countless researches that is Geopolymer Concrete. The Amount of Cement Content in Geopolymer Concrete is null and void. Artificial Neural Networks is bough of Artificial Intelligence. It is one of the sophisticated Computed technique to prognosticate the properties of Geoploymer Concrete with minimum Percentage of error. In this paper more than 170 hunks of data gathered from the literature to train and test the data. In this study evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). After performing ANN the statistical parameters found be 0.92, 0.934 and 0.905. which resembles that ANN outcomes are satisfactory and can be used in evaluating the properties of the Geopolymer concrete. [ABSTRACT FROM AUTHOR]

The deep learning technology is growing on day by day with increasing the internet access. The techniques of deep learning used to process information as human brain activities like learning some knowledge, solving the problems, and making the decision. Generally, deep learning provides self.training. In deep learning, CNN architecture is commonly used in many areas such as image processing, video processing, speech recognition, natural language processing etc. In the paper, the overview of deep learning is represented in which artificial intelligence, machine learning deep neural network are introduced. After that, layers of CNN architecture with its layers and techniques of deep learning are discussed. The CNN architecture is also used in image processing for security purposes. [ABSTRACT FROM AUTHOR]

Fossil fuels are depleting at a significantly higher rate, global warming reaching its new high and entire ecosystem suffering from air pollution. Biodiesel has emerged as potential substitute of diesel fuel that can be operated in a conventional diesel engine without any sort of major modification and provides comparable performance and lower emissions. One of the cumbersome tasks is to find the optimum blend of diesel-biodiesel and engine operating parameters. In this regard, many optimization and simulation methods have been used to optimize the biodiesel percentage in blend and engine operating parameters, which gives maximum power output and minimum emissions. There are several optimization techniques such as Genetic algorithm, Ant colony optimization, Grey relational analysis, Response surface methodology, Taguchi, artificial neural network, VIKOR and TOPSIS, which have been employed to optimize the working of a diesel engine run on biodiesel blends. In this research paper, the various optimization methods that have been used in a diesel engine for predicting & optimizing the several output parameters of the input model and ranking them accordingly are discussed. [ABSTRACT FROM AUTHOR]

Road traffic crashes result in a significant number of fatalities and economic losses worldwide. Rear-end collisions, caused by insufficient distance between vehicles, are a major contributor to these accidents. To address this issue, the use of Dedicated Short-Range Communication (DSRC) technology has been developed prominence in last decade. However, uncertainties associated with DSRC-based systems, such as GPS positioning errors and transmission delays, pose challenges to the development of robust collision warning models. In this paper, a design of three DSRC-based rear-end collision warning models to mitigate the impact of uncertainties. The models include the vehicle kinetic model (VK), the maximum compensation vehicle kinetic model (MC VK), and the artificial neural network model (NN). The VK model shows both a high possibility of false warning and missing. The MC VK model shows a 0% possibility in missing warning, which can be determined as the early warning boundary. The NN model shows low probability in both false and missing warning, and 80% correct warning makes it a good candidate for emergency warning boundary. [ABSTRACT FROM AUTHOR]