Smart Computer Research Articles

Impact of Machine Learning and Artificial Intelligence in Business Based on Intuitionistic Fuzzy Soft WASPAS Method

Artificial intelligence (AI) is a well-known and reliable technology that enables a machine to simulate human behavior. While the major theme of AI is to make a smart computer system that thinks like a human to solve awkward problems, machine learning allows a machine to automatically learn from past information without the need for explicit programming. In this analysis, we aim to derive the idea of Aczel–Alsina aggregation operators based on an intuitionistic fuzzy soft set. The initial stage was the discovery of the primary and critical Aczel–Alsina operational laws for intuitionistic fuzzy soft sets. Subsequently, we pioneer a range of applicable theories (set out below) and identify their essential characteristics and key results: intuitionistic fuzzy soft Aczel–Alsina weighted averaging; intuitionistic fuzzy soft Aczel–Alsina ordered weighted averaging; intuitionistic fuzzy soft Aczel–Alsina weighted geometric operators; and intuitionistic fuzzy soft Aczel–Alsina ordered weighted geometric operators. Additionally, by utilizing certain key information, including intuitionistic fuzzy soft Aczel–Alsina weighted averaging and intuitionistic fuzzy soft Aczel–Alsina weighted geometric operators, we also introduce the theory of the weighted aggregates sum product assessment method for intuitionistic fuzzy soft information. This paper also introduces a multi-attribute decision-making method, which is based on derived operators for intuitionistic fuzzy soft numbers and seeks to assess specific industrial problems using artificial intelligence or machine learning. Finally, to underline the value and reasonableness of the information described herein, we compare our obtained results with some pre-existing information in the field. This comparison is supported by a range of numerical examples to demonstrate the practicality of the invented theory.

Efficient secure data storage based on novel blockchain model over IoT-based smart computing systems

In recent years, edge-related smart computing is the way to process and store data via outsourcing environments. Security is the main progressive concept in smart-related Internet of Things (IoT) software denied networks to share and increase the scalability and efficiency in data storage. Because of the rapid growth of different smart services, different security-related problems may appear in resource processing and sharing data in real-time computing systems. To increase the reliability in secure data storage and satisfy the basic requirements related to IoT-related smart computing networks. So propose and implement a Novel Artificial Intelligence based Blockchain Secure Model (NAIBSM) to provide efficient secure data storage in IoT-related smart computing systems. This model flexibly captures each user authentication for the detection of different user-related attacks (i.e. distributed denial-of-service (DDOS)) in the storage of data via applying Artificial Intelligence (AI) calculation method i.e. Leverage Bat algorithm to explore complex features. Build a blockchain at the server side to provide secure communication and reliability of storing data on the terminal of IoT. This approach provides random hash values to each data to ensure blockchain for the integrity of data and uses a weight-based data storage procedure to arrange/store and classify data to each user with secure and unsecured storage in smart computing networks. The experimental results of the proposed model are to explore complex security features and ensure the performance of secure authentication to each user and better security, accuracy, and low communication overhead in IoT-related smart computing data storage and sharing systems.

Crop Selection and Yield Prediction using Machine Learning Approach

In recent years, Agriculture sector has been researched a lot with the advancements in technologies like machine learning and smart computing. With the dynamic economics of Agri-produce, it is becoming challenging for farmers to utilize the land efficiently to get maximum profit in the specific landscape. Crop Yield Prediction (CYP) is crucial and is greatly dependent on environmental factors like soil contents, humidity, rainfall as well as area under cultivation and other required metrics. Due to insufficient incorporation of the multiple environmental circumstances, a number of existing tools and techniques used for CYP, such as historical averages, tend to produce inaccurate findings. In such situation, with multiple options of crop, it is essential for farmers to plan the crop strategy in advance. If the farmer can get estimate of the crop yield in advance, cultivation can be done accordingly. To solve this problem, machine learning approach is implemented as a base for accurate predictions. Crop prediction is done by classification model and yield prediction uses regression models to learn from the data. Multiple ML models are analyzed based on performance metrics. Best performer model is incorporated in backend. Among the used models for yield prediction, Random Forest Regression gives best results with MAE of 0.64 and R2 score of 0.96. For crop prediction, Naïve Bayes classifier gives most accurate results with accuracy of 99.39. The study emphasizes how machine learning could revolutionize crop management techniques by giving farmers insights about optimizing resource allocation and boost overall crop yield.

An FPGA-Based Design of a Fault-Tolerant Shared Memory Structure

In this current era of smart computation, faster processing speed is needed. Execution of a process in a parallel manner can achieve higher throughput. For avoiding the Von-Neumann bottle-neck phenomena, the speed of the memories should be high. A high-speed memory can provide contiguous data to a high-speed processor that retains the high performance of that processor. Various high-speed memory technologies are existing in the market such as interleaved memory, cache memory, etc. Multiprocessing technology is also used to achieve high-performance computation. It is still challenging to make a high-speed memory device that can provide data to a multiprocessing system in a contiguous way. Shared memory can fulfill all of the requirements of a multiprocessing system. The efficiency of shared memory can be enhanced by introducing the fault-tolerant mechanism in it without affecting the inter-process communication, which will be discussed in this paper.

ANALYSIS OF FACTORS THAT INFLUENCE STUDENTS' MATHEMATICAL CRITICAL THINKING SKILLS: INTRAPERSONAL INTELLIGENCE AND LEARNING MOTIVATION

Mathematical critical thinking skills is a person's capacity that includes a thought process that aims to obtain a rational thought to do something or believe what will be done. Many factors influence students' mathematical critical thinking skills, including intrapersonal intelligence and learning motivation. This research points to control the stimulate of intrapersonal intelligence and learning motivation on students' mathematical critical thinking skills. This study is quantitative research through an ex-post-facto approach. The population in this research is a class XI high school in the Sidrap district. The quantity of samples in this research was 85 students who were selected by the purposive sampling technique. The data collection technique in this study used an instrument consisting of (1) an intrapersonal intelligence questionnaire, (2) a learning motivation questionnaire, and (3) a mathematical critical thinking ability questionnaire. Analysis of the data used is Structural Equation Modeling (SEM) using the PLS 3.0 smart computer program. The results of this study are that there is an influence between intrapersonal intelligence on students' mathematical critical thinking skills of 0.284 (P values = 0.039) and learning motivation affects students' mathematical critical thinking skills of 0,579 (P values = 0.00).

Traffic Management: Multi-Scale Vehicle Detection in Varying Weather Conditions Using YOLOv4 and Spatial Pyramid Pooling Network

Detecting and counting on road vehicles is a key task in intelligent transport management and surveillance systems. The applicability lies both in urban and highway traffic monitoring and control, particularly in difficult weather and traffic conditions. In the past, the task has been performed through data acquired from sensors and conventional image processing toolbox. However, with the advent of emerging deep learning based smart computer vision systems the task has become computationally efficient and reliable. The data acquired from road mounted surveillance cameras can be used to train models which can detect and track on road vehicles for smart traffic analysis and handling problems such as traffic congestion particularly in harsh weather conditions where there are poor visibility issues because of low illumination and blurring. Different vehicle detection algorithms focusing the same issue deal only with on or two specific conditions. In this research, we address detecting vehicles in a scene in multiple weather scenarios including haze, dust and sandstorms, snowy and rainy weather both in day and nighttime. The proposed architecture uses CSPDarknet53 as baseline architecture modified with spatial pyramid pooling (SPP-NET) layer and reduced Batch Normalization layers. We also augment the DAWN Dataset with different techniques including Hue, Saturation, Exposure, Brightness, Darkness, Blur and Noise. This not only increases the size of the dataset but also make the detection more challenging. The model obtained mean average precision of 81% during training and detected smallest vehicle present in the image

A Radial Basis Function Neural Network Approach to Predict Preschool Teachers' Technology Acceptance Behavior.

With the continual development of artificial intelligence and smart computing in recent years, quantitative approaches have become increasingly popular as an efficient modeling tool as they do not necessitate complicated mathematical models. Many nations have taken steps, such as transitioning to online schooling, to decrease the harm caused by coronaviruses. Inspired by the demand for technology in early education, the present research uses a radial basis function (RBF) neural network (NN) modeling technique to predict preschool instructors’ technology usage in classes based on recognized determinant characteristics of technology acceptance. In this regard, this study utilized the RBFNN approach to predict preschool teachers’ technology acceptance behavior, based on the theory of planned behavior, which states that behavioral achievement, in our case the actual technology use in class, depends on motivation, intention and ability, and behavioral control. Thus, this research design is based on an adapted version of the technology acceptance model (TAM) with eight dimensions: D1. Perceived usefulness, D2. Perceived ease of use, D3. Perceived enjoyment, D4. Intention to use, D5. Actual use, D6. Compatibility, D7. Attitude, and D8. Self-efficacy. According to the TAM, actual usage is significantly predicted by the other seven dimensions used in this research. Instead of using the classical multiple linear regression statistical processing of data, we opted for a NN based on the RBF approach to predict the actual usage behavior. This study included 182 preschool teachers who were randomly chosen from a project-based national preschool teacher training program and who responded to our online questionnaire. After designing the RBF function with the actual usage as an output variable and the other seven dimensions as input variables, in the model summary, we obtained in the training sample a sum of squares error of 37.5 and a percent of incorrect predictions of 43.3%. In the testing sample, we obtained a sum of squares error of 14.88 and a percent of incorrect predictions of 37%. Thus, we can conclude that 63% of the classified data are correctly assigned to the models’ dependent variable, i.e., actual technology use, which is a significant rate of correct predictions in the testing sample. This high significant percentage of correct classification represents an important result, mainly because this is the first study to apply RBFNN’s prediction on psychological data, opening up a new interdisciplinary field of research.

Jarvis: AI Virtual Assistant

"Jarvis" was the protagonist of Tony's Stark starring in the movie Iron Man. Unlike the first jokes when Jarvis was Stark's servant, Jarvis' movie version is a smart computer that communicates critical issues, monitors his family, and helps build and organize his hero's suit. In this project, Jarvis is a Digital Health Assistant who uses social media i.e., voice that creates two-way communication between people and his personnel Computer, performing various tasks such as calling and chatting on WhatsApp, taking notes, locating, etc. In our project, we use the word just as communication means that Jarvis is a Speech recognition program. The concept of speech technology combines two technologies: Synthesizer and vision. Speech synthesizer captures input and generates audio streams as output this is achieved using the pyttsx3 library accompanied by Speech function. Speech recognition. On the other hand, he did the opposite. It treats audio streaming as input and thus converts it into text writing and this output uses the Speech Recognition library accompanied by the TakeCommand function. The word is a symbol of endless knowledge. Direct analysis and integration of complex voice signal is due to the vast amount of information contained in the signal. Therefore, digital signal processes such as Feature Visibility and Feature Matching are introduced to represent the voice signal. For this project, we are directly using a speech engine that uses an output element such as Mel scaled frequency cepstral. The mel scale frequency cepstral coefficients (MFCCs) obtained from the fourier transform and filter bank analysis are probably the most widely used in high-level speech recognition systems. We aim to create more jobs that can help people in their daily lives and reduce their efforts as in this project we have introduced Chrome and Youtube Automation. In our test, we checked that all of this functionality works well. We test this on 2 speakers (1 female and 1 male) to find the exact motive for the speculation.

Smart Computing Techniques for Predicting Soil Compaction Criteria under Realistic Field Conditions

The primary objective of this paper was to develop an artificial neural network (ANN) simulation environment and mathematical models for predicting with high accuracy soil compression parameters. The experiments were conducted at the College of Agriculture - University of Basra, located at Garmat Ali, the soil was silty clay loam. The factors that were investigated are moisture content (14 and 24%), tillage depths (0, 15, 30, 45, and 50 cm) forward speeds (0.57, 0.94, and 1.34 m.s-1) and tire pressures (50, 100, and 150 kPa). ANN environment was developed with the back propagation algorithm using MATLAB software with various structures and training algorithms. Design Expert software utilized to evaluate the studied parameters and produce mathematical models. The results showed that all studied parameters had a significant effect on soil physical properties including bulk density and cone index. The effects of the studied factors on bulk density were depth > moisture content > forward speed, > tire pressure (6% 4%, 2.4%, 2%, respectively). Whereas, the order of the investigated factors based on their effects on cone index were depth > moisture content > tire pressure > forward speed (6%, 4%, 2.4% and 2%, respectively). The best model for predicting the bulk density under different field conditions was the 4-8-1 architecture. Levenberg-Marquardt (Trainlm) produced outstanding performance with an MSE of 0.00226 and R2 of 0.986. Moreover, this performance was occurring at an epoch of 100. For predicting cone index, the best performance was achieved by Levenberg-Marquardt (trainlm) in 85 epochs, giving minimum MSE equal to 0.005112 and greater (R2) equal to 0.967 during the training process. Thus, the optimal structure for predicting cone index was 4-7-1.

Parylene-based memristive crossbar structures with multilevel resistive switching for neuromorphic computing

Currently, there is growing interest in wearable and biocompatible smart computing and information processing systems that are safe for the human body. Memristive devices are promising for solving such problems due to a number of their attractive properties, such as low power consumption, scalability, and the multilevel nature of resistive switching (plasticity). The multilevel plasticity allows memristors to emulate synapses in hardware neuromorphic computing systems (NCSs). The aim of this work was to study Cu/poly-p-xylylene(PPX)/Au memristive elements fabricated in the crossbar geometry. In developing the technology for manufacturing such samples, we took into account their characteristics, in particular stable and multilevel resistive switching (at least 10 different states) and low operating voltage (<2 V), suitable for NCSs. Experiments on cycle to cycle (C2C) switching of a single memristor and device to device (D2D) switching of several memristors have shown high reproducibility of resistive switching (RS) voltages. Based on the obtained memristors, a formal hardware neuromorphic network was created that can be trained to classify simple patterns.

Building Security Mechanisms for Cross-Border Business Customer Data Analysis Based on Smart Computing

Dynamic social networks also suffer from privacy violation due to the continuous release of data. In order to defend against enemy attacks, a new dynamic privacy-preserving method is innovated, called Dynamic k w Re-Structured Diversity Anonymity ( k w -SDA). This method limits the probability of disclosure of node/community identities to 1 / k when data are continuously published by protecting individuals in groups. The graph based on the previous w − 1 is released, missing some graph alterations. The advantage of the proposed method is to save many features of the network while protecting privacy efficiently and effectively.

Preface

The 5th International Conference on Eco Engineering Development (ICEED 2021) brings academicians, practicians, and governmental policy makers to discuss the emerging topic of “Innovative Eco Engineering for Sustainable Development towards Society 5.0”. This conference was organized and hosted by Bina Nusantara University, Jakarta, Indonesia. Due to the COVID-19 pandemic protocol, the conference was held virtually via Zoom meeting on 17-18th November 2021. The conference topics address the most recent developments in Eco Engineering and Sustainable Development by combining plenary and parallel sessions to stimulate technical and scientific interaction in the topics of:• Sustainable Infrastructure and Transportation, Energy, Water, and Renewable Energy• Sustainable Industry and Green Manufacturing• Sustainable Architecture• Integrated of Smart Computing and Communication• Sustainable Food TechnologyThe physical limitations brought by COVID-19 pandemic did not fade the spirit for disseminating current knowledge in the Eco Engineering and Sustainable Development. It is described by the enthusiasm of authors who submitted a total of 326 manuscripts to ICEED 2021. Among them, there are about 200 papers that were accepted for parallel presentation and 63 paper is accepted for IOP Publication. During the virtual conference, in the parallel presentations, each presenters had ten minutes for presentation time, continued with five minutes of question and answer session. Furthermore, in order to maintain good time management, presenters need to submit a presentation video (ten minutes) to the conference committee beforehand to ensure the parallel sections ran smoothly.

Development of Innovative Operational Flexibility Measurement Model for Smart Systems in Industry 4.0 Paradigm

The exponential growth of cutting-edge technologies continuously pushing the manufacturing industry into the paradigm of smart manufacturing. Smart manufacturing provides the pace of highly competitive market demand and customized intensive production. The goal of smart manufacturing technologies embedded systems in the Industry 4.0 paradigm and lean production is to enhance the flexibility in all tears of the enterprise. It is a big challenge, to measure the flexibility of smart systems for decision-making and adaptation of the new manufacturing technologies. The conceptual architecture of smart manufacturing systems has been proposed to solve the problem. Operational flexibility has been measured using a mathematical model for smart manufacturing in the Open Platform Communication Unified Architecture enabled Cyber-Physical Production System at shop floor level and validated. The results obtained from experimentation depict the operational flexibility, maximum capacity, and breakeven point of the manufacturing system have been improved by using smart manufacturing technologies. The proposed model improves the product manufacturing using Smart Computer Numeric Control Machining, Smart Autonomous Robotic Machining, Smart Additive Manufacturing and Smart Hybrid Additive &#x0026; Subtractive Manufacturing up to 30.4&#x0025;, 53.6&#x0025;, 55&#x0025; and 65&#x0025; respectively. It will also help the decision-makers to overcome the challenges of transformation from conventional to smart manufacturing industry 4.0 paradigm.

An adaptive multi-hop routing with IoT abstraction for minimizing delay-node capacity trade-offs in mobile ad-hoc network

&lt;span&gt;Delay and node capacity are incompatible mobile ad hoc constraints because of the network's versatility and self-disciplined design. &lt;/span&gt;&lt;span&gt;It is a challenging problem to maximize the trade-off between the above mobility correlation factors. &lt;/span&gt;&lt;span&gt;This manuscript proposes an adaptive multi-hop routing (A.M.R.) for mobile ad-hoc network (MANET) to minimize the trade-off by integrating the internet of things (IoT). IoT nodes' smart computing and offloading abilities are extended to ad-hoc nodes to improve routing and transmission. Dor MANET nodes in route exploration, neighbor selection, and data transmission, the beneficial features of IoT include enhanced decision making. The traditional routing protocols use IoT at the time of the neighbor discovery process in updating the routing table and localization. &lt;/span&gt;&lt;span&gt;The harmonizing technologies with their extended support improve the performance of MANETs has been estimated. The proposed method achieves better throughput (14.16 Mbps), delay (0.118), packet drop (126), and overhead (36 packets) when compared to existing methods.&lt;/span&gt;

A smart computer mouse with biometric sensors for unobtrusive office work-related stress monitoring.

Health disorders related to the prolonged exposure to stress are very common among office workers. The need for an automated and unobtrusive method of detecting and monitoring occupational stress is imperative and intensifies in the current conditions, where the pandemic COVID-19 causes changes in the working norms globally. In this study, we present a smart computer mouse with biometric sensors integrated in such a way that its structure and functionality remain unaffected. Photoplethysmography (PPG) signal is collected from user's thumb by a PPG sensor placed on the side wall of the mouse, while galvanic skin response (GSR) is measured from the palm through two electrodes placed on the top surface of the mouse. Biosignals are processed by a microcontroller and can be transferred wirelessly over Wi-Fi connection. Both the sensors and the microcontroller have been placed inside the mouse, enabling its plug and play use, without any additional equipment. The proposed module has been developed as part of a system that infers about the stress levels of office workers, based on their interactions with the computer and its peripheral devices.

Custom Built of Smart Computing Platform for Supporting Optimization Methods and Artificial Intelligence Research

This paper describes a prototype of a computing platform dedicated to artificial intelligence explorations. The platform, dubbed as PakCarik, is essentially a high throughput computing platform with GPU (graphics processing units) acceleration. PakCarik is an Indonesian acronym for Platform Komputasi Cerdas Ramah Industri Kreatif, which can be translated as “Creative Industry friendly Intelligence Computing Platform”. This platform aims to provide complete development and production environment for AI-based projects, especially to those that rely on machine learning and multiobjective optimization paradigms. The method for constructing PakCarik was based on a computer hardware assembling technique that uses commercial off-the-shelf hardware and was tested on several AI-related application scenarios. The testing methods in this experiment include: high-performance lapack (HPL) benchmarking, message passing interface (MPI) benchmarking, and TensorFlow (TF) benchmarking. From the experiment, the authors can observe that PakCarik's performance is quite similar to the commonly used cloud computing services such as Google Compute Engine and Amazon EC2, even though falls a bit behind the dedicated AI platform such as Nvidia DGX-1 used in the benchmarking experiment. Its maximum computing performance was measured at 326 Gflops. The authors conclude that PakCarik is ready to be deployed in real-world applications and it can be made even more powerful by adding more GPU cards in it.

The use of internet and technological devices in children with disability from the perspective of parents

BACKGROUND: Information and communication technologies can make a significant difference in the lives of individuals with disabilities. Hardware and software services adapted for the type of disability may enable the individuals with disabilities to make use of information and communication technologies. OBJECTIVE: The aim of the study was to examine the use of internet and technological devices in children with disability from the perspective of parents. METHODS: This was a cross-sectional and descriptive study. The sample group was comprised of parents who accepted to take part in the study and who have children continuing their education at a special education and rehabilitation center. Family and Child Introductory Information Form, Use of Internet and Technological Devices Information Form were used for data acquisition. RESULTS AND CONCLUSIONS: It was determined that 97.5% of the children have smart phones, 45% have tablets and 34% have computers and that smart phones, tablets and computers are used for playing games by 64%, 27% and 10% of the children respectively. The present study has shown that children with disability are able use technological devices by themselves for the purposes of following social media, playing games, watching films/cartoons, listening to music, doing homework, chatting and taking photographs.

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective.

The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on “Data Science” including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

A novel intelligent round robin CPU scheduling algorithm

In the present era of smart computing and digital transformation multiprogramming multitasking, multi-user operating environment is playing an important role where rapid arrival, termination and switching of multiple processes is crucial for performance. CPU Utilization is one of the key performance parameter and CPU scheduling crucial for CPU utilization. On the way of improving performance, substantial research and efforts are being done by community researchers and operating system processing is becoming fast day by day. Major performance evaluations criteria like waiting time, response time turnaround time number of context switching are sophisticatedly enhanced to achieve better processing. Round-Robin (RR) scheduling is a well-known important scheduling algorithm where quantum time slot pointedly affect performance. In case of very large quantum time it works like First come first serve or if it is too small then RR works like a process sharing algorithm which increases the system overheads in view of context switching. Thus setting up an effective quantum time is a major challenge for researchers. In this work a clustering inspired novel intelligent approach has been proposed to significantly enhance the round robin algorithm performance. An intelligent sub ready queue by using standard deviation and optimal threshold is created. Dynamic quantum is generated for each cluster and performance is evaluated on sample data in terms of waiting time, response time, turnaround time and context switching. The results of the proposed intelligent approach are compared with other existing algorithms and are found better.

Website Vulnerabilities Attacks and Negative Impacts

In today's modern age, everyone from the individual to an ad company has the support of technology, from the youngest child to the oldest, likewise as from the unemployed to the lender, using all the technology. In a smart phone or computer, the globe seems to own passed the time. Not only household necessities like home groceries, clothes, utensils, furniture, equipment but also hotel booking, restaurant, hospital appointment, tourism, railway-bus-flight booking, stock exchange likewise as important banking industry are related to this. Now that everything is occurring online, most are expecting everything in their own backyard. Websites or applications are created for online work, further as websites are designed in HTML. HTML is that the simple design language so it code is shown all told browsers. This paper try and show how far hackers can make the most of this simplicity and the way much impact it's on user information.