Computer Sciences and Engineering

This article presents in detail the Kyungu Formula for the Inverse Laplace Transform, a new method based on successive derivatives of an auxiliary function. Unlike the classical Bromwich integral, the formula provides a direct and systematic approach to compute inverse Laplace transforms. Several examples are worked out, showing how the formula retrieves known closed forms and also generates new series expansions not usually found in classical tables. This contribution offers a fresh perspective on symbolic computation, applied mathematics, and engineering.

Smart meter is an advanced energy meter that measures consumption of electrical energy providing additional information compared to a conventional energy meter. Integration of smart meters into electricity grid involves implementation of a variety of techniques and software, depending on the features that the situation demands. Design of a smart meter depends on the requirements of the utility company as well as the customer. We discuss various features and technologies that can be integrated with a smart meter. In fact, deployment of smart meters needs proper selection and implementation of a communication network satisfying the security standards of smart grid communication. The various issues and challenges involved in design, deployment, utilization, and maintenance of the smart meter infrastructure. In addition, several applications and advantages of smart meter, in the view of future electricity market are discussed in detail. We explain the importance of introducing smart met...

What is Full Stack Development? Full Stack Web Development refers to the practice of working on both the Front-End (client-side) and Back-End (server-side) of a web application. A full stack developer is proficient in all layers of the "stack," ensuring a seamless connection and efficient functionality from the user interface down to the database. The Web Development Stack The "stack" is generally divided into three main layers: 1. Front-End (Client-Side): Everything the user sees and interacts with directly in their browser. This is the Presentation Layer.

Este livro de atas foi preparado com o máximo cuidado e atenção pelos editores, no entanto, não podemos garantir a exatidão, completude ou atualidade do conteúdo deste volume. Os textos, opiniões e análises expressas são da responsabilidade exclusiva dos seus autores e não refletem necessariamente a opinião dos editores. Além disso, o conteúdo destina-se apenas a fins de estudo, não devendo ser interpretado como aconselhamento profissional nem recomendação. Os editores não assumem qualquer responsabilidade por possíveis erros científicos, técnicos, ortográficos ou de qualquer outra natureza que possam estar presentes. Do mesmo modo, não endossamos necessariamente as ideias, conceitos ou teorias apresentadas pelos autores, pelo que os textos aqui incluídos devem ser considerados como exercícios académicos em diferentes estádios de desenvolvimento e de rigor. Este libro de actas ha sido preparado con el máximo cuidado y atención por los editores; sin embargo, no podemos garantizar la exactitud, exhaustividad o actualidad de los contenidos de este volumen. Los textos, opiniones y análisis expresados son responsabilidad exclusiva de sus autores y no reflejan necesariamente los puntos de vista de los editores. Además, el contenido está destinado únicamente a fines de estudio y no debe interpretarse como asesoramiento o recomendación profesional. Los editores no aceptan responsabilidad alguna por los errores científicos, técnicos, ortográficos o de otro tipo que puedan estar presentes. Asimismo, no respaldamos necesariamente las ideas, conceptos o teorías expuestos por los autores, por lo que los textos aquí incluidos deben considerarse ejercicios académicos en diferentes fases de desarrollo y rigor. This proceedings book has been prepared with the utmost care and attention by the editors, however, we cannot guarantee the accuracy, completeness or timeliness of the contents of this volume. The texts, opinions and analyses expressed are the sole responsibility of their authors and do not necessarily reflect the views of the editors. Furthermore, the content is intended for study purposes only and should not be construed as professional advice or recommendation. The editors accept no responsibility for any scientific, technical, spelling or other errors that may be present. Likewise, we do not necessarily endorse the ideas, concepts or theories presented by

In today’s world, as limited quantity of the resources available, it's necessity to utilize that resources in such way that it gives maximum of them. The major problem for the gear transmission is that the manufacturing of drugs is complex process which consumes longer and takes considerably precision and manufacturing cost is high .[1,2,4,7,8] The another major problem is that the transmission having gear cause the jamming thanks to the backless error and produces more noise compared to other drives thanks to pitch mismatch.[3] This elbow mechanism is additionally referred to as Gearless transmission , L-pin mechanism or Orbital mechanism . This elbow mechanism is straightforward in construction and may be easily made with minor precision. This mechanism is mainly utilized in replacement of bevel gears where the motion is to be transmitted at 900. So, generally elbow mechanism angle between rod is taken 900. [1,2,4,5,6,7,8,11,12,14] This mechanism also can be wont to transmit p...

The adoption of face feature recognition technique in smart elect voting system is a crucial advancement in the election process. The system suggested enhances the productivity, convenience and security of voters. Through the use of biometric algorithms, only authentic voters can cast their vote. When a voter registers, his or her face is captured and kept in a computerized database in encrypted form. Voters on election day simply go through the kiosks. Their faces are captured in real time and compared in real time with data in storage. The voice assistant helps in opening up and running through the VoIP interfacing choice system; confirming choices through voice instructions makes it easier to expedite the election which quickens the vote-taking process. Physically challenged individuals are sure to be aided by this facility as well as a larger voters' constituency who do not understand the computer system. Additionally, the smart vote-casting system has been developed towards flexibility and scalability. The system accommodates a massive voter turnout and conducts different types of elections at a national and local level and simultaneously or separately. The overall system is designed with security as its core focus the modern techniques of encryption protect highly sensitive information like voter records. The face recognition software has proactive measures put in place such as routine evaluations aimed towards previously established cybersecurity issues that are prone to exploitation so overhaul updates can be administered frequently guarantee bolstered defense against new cyber threats. Overall sophisticated methods are used to encrypt information thus putting relentless effort into defending voter data.

Impersonation in the context of examination, is a situation where a candidate sits in an examination for another candidate pretending to the real candidate. In many institutions in Nigeria, to mitigate this act, students are expected to present a means of identification before entering the examination hall. However, this approach is not sufficient to determine the eligibility of a student for an examination as these means of identification can easily be falsified. This paper therefore, develops a face recognition web service model for student identity verification using Deep Neural Network (DNN) and Support Vector Machine (SVM). The aim is to mitigate examination impersonation by simple face scan using mobile phone and also to make such a model accessible and re-usable for seamless integration with any kind of student identity verification project.

Machines and humans differ fundamentally: humans have consciousness, emotions, common sense, and adaptability, while machines excel at processing data, performing repetitive tasks, and demonstrating consistent, logical behavior. While machines are superior in computational speed and scale, they lack human traits like intuition, social understanding, and creativity. Combining the strengths of both humans and machines offers the potential for synergies, enabling humans to focus on creative and relational aspects of tasks while machines handle complex data processing and calculations.
Traditional design typically consists of a master-servant relationship between humans and machines where the human directly controls what the machine will do and when it will do it through an interface.
The current archetypical path encompasses moving from informational displays, where the human directly controls the machine based on information displayed, to automation where the human still directs the machine that then caries out the request using predefined set of instructions

The rising food prices in recent years leads to the cascading effect on the entire Indian economy. India cannot dream about two digit growth rate unless the revitalization of the agriculture sector. The shackled rural economy will be freed and its engine needs to expedite by convergence of technology under changing environment. Indian population is expected increase in the coming years meanwhile India needs to keep up its food production. The land area available to agriculture is not expected to increase rather it will decrease due to rapid expansion of habitation. Hence there is need to increase productivity of agriculture and yields per hectare. Water shortage results in less than sufficient irrigation will be another major challenge to combat in the years to come. Affordable modern irrigation techniques will result per drop more crop. Indian farmer needs realistic crop protection technology which will reduce per hectare usage of agrochemicals resulting low production cost and improving yields. Mitigating the digital divide for supporting planting decision to selling their produce at the wholesale market will be the game changer in agriculture. This article provides a detailed review of emerging trends in farm automation using agriculture robots and Internet of Things. The topic includes natural resource variability, variability management and potential of technologies in modernizing agriculture.

The rising food prices in recent years leads to the cascading effect on the entire Indian economy. India cannot dream about two digit growth rate unless the revitalization of the agriculture sector. The shackled rural economy will be freed and its engine needs to expedite by convergence of technology under changing environment. Indian population is expected increase in the coming years meanwhile India needs to keep up its food production. The land area available to agriculture is not expected to increase rather it will decrease due to rapid expansion of habitation. Hence there is need to increase productivity of agriculture and yields per hectare. Water shortage results in less than sufficient irrigation will be another major challenge to combat in the years to come. Affordable modern irrigation techniques will result per drop more crop. Indian farmer needs realistic crop protection technology which will reduce per hectare usage of agrochemicals resulting low production cost and improving yields. Mitigating the digital divide for supporting planting decision to selling their produce at the wholesale market will be the game changer in agriculture. This article provides a detailed review of emerging trends in farm automation using agriculture robots and Internet of Things. The topic includes natural resource variability, variability management and potential of technologies in modernizing agriculture.

This paper describes a case study for the causes of cracks and ruptures that emerged in the houses of the City of Shari, AlQassim region. The structures of this city built using the load wall bearings system. Loads of bearing masonry buildings usually have walls arranged at a fairly uniform spacing to carry gravity loads to the foundation without addition of columns. This study is divided to two parts; the first part discusses the field geotechnical investigation of the construction site. The second part presents the results of a parametric study for the effect of soil settlement as well as the positions and sizes of the wall openings on the existence of the wall cracks. The results showed that, the construction of houses almost at or very near to the ground surface with no rigid tie-beams under the walls which could help in resisting the differential settlement affects their safety. The opening definitely reduces the stiffness of the wall and subsequently reduces the failure load and increases deflection at the same load level. The finite difference-based code FLAC and the finite element-based code, ANSYS were used in the analysis.

In this paper, an array of 7*7 novel compact metamaterial unit cells is characterized and employed for the gain enhancement of a microstrip antenna operating at 7.1 GHz. This 7.1 GHz operating frequency is the C-band frequency which is use in the long distance for the satellite communication. The unique property of the metamaterial gathers the wave radiated from the antenna and collimates it towards the normal direction when used as a superstrate array. The proposed unit cell is obtained by suitably adding extra metal strips to the conventional mirrored S like structures. The unit cell analysis and parameter extraction using periodic boundary condition confirms the near zero index behaviour as well as significant reduction (almost 52%) in the unit cell footprint. The proposed design provides a gain enhancement of 9.4dB using a single layer superstrate, when suspended above a microstrip patch at 7.1 GHz. Simulation and measurement results confirm that Near-Zero Index Metamaterial (NZIM) lens significantly improves the gain (by more than 1.8 dBi) . A parameter-retrieval method using the S parameters is used to calculate the curves for the complex permittivity and permeability of the metamaterial based on its unit element. The S parameters are extracted using HFSS TM , which is a Finite-Element-Method (FEM)-based full-wave simulator. , Near Zero Index Metamaterials (NZIM) have the ability to control the direction of radiation.

The present work if it proposes to identify the children's emotional and physical reactions from 1 to 5 years. The population objective consisted of 56 children interned at Oliveira's Hospital Pediatric Monsignor Pedro Rocha, in Crato, Ceará. The data were obtained starting from a form especially built for the research and of the observation. The results demonstrated that the children's group that they had companions during the hospitalization presented smaller frequency of physical reactions, such as: cry, vomits, diarrhea, palpitation, lack of appetite, insomnia, enuresis night, and emotional such as: indifference, fear, apathy, aggressiveness and irritability, that the children's group, without companions, during the internment. The children's companions were shown cooperative with the team of health. We concluded that she should demand from the public political rulers that they privilege the child, especially in what he concerns the right of staying with a relative when in situation of internment hospitalar.

The Expense Tracker is an innovative digital application designed to help users manage and monitor their daily expenditures efficiently. It serves as a comprehensive tool for tracking spending habits by organizing expenses into categories and offering detailed summaries. Users can review their financial activities across different timeframes, such as daily, weekly, or monthly. The app also incorporates a budgeting feature to promote disciplined spending by providing actionable insights. At the end of each month, users can access visual reports, including graphs and charts, to analyse their financial patterns. Additional features include personalized expense categories and a complete transaction history, enabling users to maintain better financial control.

Primary brain tumor detection and classification are critical factors in ensuring effective treatment and, ultimately, improving patient well-being. This paper describes a novel method for detecting and classifying brain tumors with the help of magnetic resonance imaging (MRI) and computed tomography (CT) images. The suggested method combines harmony search optimization (HSO) and Convolution Neural Networks (CNN) based on deep learning techniques, yielding an impressive accuracy rate of 99.13% for both detection and classification tasks. Furthermore, the system has exceptional specificity (99.2243%) and sensitivity (99.245%), highlighting its precision in distinguishing true negatives and true positives. This incorporation of advanced methodologies not only improves diagnostic accuracy but also reduces reliance on radiologists’ expertise. The experimental results support the proposed approach’s effectiveness, demonstrating its ability to detect and classify brain tumors from medical images. This breakthrough is a noteworthy step forward in the fields of medical image analysis and brain tumor diagnosis, paving the way for better patient care and treatment outcomes.

The aim of this paper is to develop home automation system based on IOT using Wi-Fi based microcontroller. As scope of technology is widening every day, we are making our tech advance in mobile, robotics, Machine Learning, then why an exception for our home. Today houses are gradually transferring from ordinary/human’s input-based appliances to smart/IOT enabled appliances to be controlled remotely. At Present, existing home automation systems use technology that is limited to only that device. So, in a nutshell, we are making our devices IOT enabled not our homes.

The COVID-19 pandemic has led to an increase in digitization. With the strict social and physical distancing measures in place, new routines require accessing the internet for most online services which have led to the explosive growth of data. As a consequence, data mining technologies are used for the extraction of useful information from a huge compilation of such digital data. Thus, the desire to mine data from varied sources to discover behaviors and patterns among entities such as customers, diseases, and environmental conditions is on the rise which can be accomplished by association rule mining. However, such pattern discovery by association rule mining also discloses the personal information of an individual or organization. Thus, the challenge of association rule mining is privacy preservation wherein confidentiality of sensitive rules should be maintained while releasing the database of third parties. Privacy-preserving association rule mining is the process of modifying the original database to hide the sensitive rules for preserving privacy. Thus, the paper emphasizes multiple objectives like minimizing the side effects of hiding sensitive rules. i.e. reduce the number of ghost rules, lost rules, and hiding failure along with the increase in utility of the data.

Air pollution is the “world’s largest environmental health threat”[1], causing 7 million deaths[1] worldwide every year. Its major constituents are PM2.5, PM10 and the harmful green house gases S02, N02, C0 and other effluents from vehicles and factories affecting not only humans but also other living organisms both on land and sea. The only effective solution to this global issue is to implement machine learning algorithms to predict the AQI (Air Quality Index ) that can make the people aware of the condition of the air of a certain region such that certain actions could be issued by the government for the improvement of the air quality in the future. The prime objective behind this project is to predict the AQI based on the concentration of PM2.5, PM10,S02, N02, C0 as well as weather conditions like temperature, pressure and humidity[2].Hence the data set is combined from various web sources like cpcb.nic.in and uci repository in order to bring accuracy in the prediction and to ju...

A proposta deste material é fornecer dicas que possam apoiar as atividades como tutor(a) nos cursos de especialização promovidos pela Universidade Aberta do Sistema Único de Saúde na Universidade Federal de Pernambuco (UNA-SUS UFPE) e em outros cursos online. Também são apresentadas respostas para as perguntas que surgem com mais frequência no universo da tutoria e sugestões de como agir em situações específicas que podem surgir com os trabalhadores e trabalhadoras-estudantes que estão sob responsabilidade do(a) tutor(a).

This article examines the concept of Progressive Web Applications (PWA) as an innovative approach to development that combines the advantages of websites and native applications. It explores the technological foundation of PWA, including key components such as Service Workers, Web App Manifest, and HTTPS, which ensure high performance, offline access, and security. The study investigates the impact of PWA on improving user experience, cost efficiency in development, and their potential in various industries, including e-commerce and media. Special attention is given to the prospects of PWA application in the context of digital transformation and the further advancement of web technologies.

Automated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to grading by human raters. Conventional AES typically relies on handcrafted features, whereas recent studies have proposed AES models based on deep neural networks (DNNs) to obviate the need for feature engineering. Furthermore, hybrid methods that integrate handcrafted features in a DNN-AES model have been recently developed and have achieved state-of-the-art accuracy. One of the most popular hybrid methods is formulated as a DNN-AES model with an additional recurrent neural network (RNN) that processes a sequence of handcrafted sentencelevel features. However, this method has the following problems: 1) It cannot incorporate effective essay-level features developed in previous AES research. 2) It greatly increases the numbers of model parameters and tuning parameters, increasing the difficulty of model training. 3) It has an additional RNN to process sentence-level features, enabling extension to various DNN-AES models complex. To resolve these problems, we propose a new hybrid method that integrates handcrafted essay-level features into a DNN-AES model. Specifically, our method concatenates handcrafted essay-level features to a distributed essay representation vector, which is obtained from an intermediate layer of a DNN-AES model. Our method is a simple DNN-AES extension, but significantly improves scoring accuracy.

Social distancing is one kind of preventative measure to reduce the spread of COVID-19. COVID-19 transmits mainly from one person to another during close contact for a prolonged period. Different types of preventive measures like thermal screening, social distancing, hand sanitization, office sanitization, building sanitization etc. are taken by an organization for smooth functioning of the organization. Implementation of social distancing in the organization is really a challenging task. Some work is done by the group of people and some work may be done by the individual. In some cases symptom of COVID-19 is shown but it in some cases its symptoms are not shown. Recent studies indicate that people who are infected but do not have symptoms likely also play a role in the spread of COVID-19. Social distancing helps the limit opportunities to come in contact with infected person and contaminated surface in an organization. The challenging task is taking decision in implementing social ...

In this paper, we offer a novel underwater picture enhancing model based on hybrid algorithms. We describe a color correction system designed to compensate for the disparities between superior and inferior color channels, using specifically constructed attenuation matrices to restore accurate color representation. Furthermore, we use dual sub image histogram equalization and a contrastlimited histogram equalization approach with Rayleigh distribution to improve both global and local contrast in the color-corrected image, resulting in increased visibility and detail. We combine the advantages of global and local contrast enhancement using a multi-scale fusion method. Furthermore, our system incorporates a novel approach to underwater image restoration, which addresses weaknesses in traditional methods by enhancing background light estimation and automatic white balance. Evaluation through contrast experiments on various underwater images demonstrates the superior performance of our enhanced images in both subjective and objective assessments.

This project is all about building an autonomous obstacle avoiding robot making use ofmmWave sensors for its navigation. TIDEP-0094 or IWR1642BOOST evaluation board embedded with IWR1642 chipand XDS110 MCU is used to perform the required operation. An obstacle avoidance is an intelligent robot which can automatically sense and overcome unexpected obstacles on its path. It contains a micro-controller to process the data and sensors to detect obstacles on it's path. This project proposes an autonomous robotic vehicle which has an intelligence integrated in it such that it can direct itself whenever an obstacle comes in its path of movement. This project is based on the IWR1642BOOST evaluation board. The mmWave sensor (IWR1642) is used to detect any obstacle near it. It sends the data to the MCU XDS110 where it is processed and the robot is made to move in the alternate direction using motors interfaced through a motor driver (LM293D).

This aids the visually impaired in avoiding potential hazards. To help the sight challenged with their travels, we offer a smart glass. In this manner, many persons who are visually impaired may go about their day without worrying about encountering any obstacles. Traveling from one location to another is very challenging for those with visual impairments in the modern world. Our approach is on giving them with personalized vocal support to overcome whatever challenges they may have. They may reach out to the faraway individual for advice when things go wrong. As a consumer product, it aids the vision handicapped in getting about independently. A built-in sensor emits ultrasonic waves in the direction the user is walking, scanning a radius of up to 10 meters. A vibration sensor, which shakes and makes a beep when it senses an obstacle in the person's path, is also useful for this purpose. The Global System for Mobile Communication (GSM) is a means of contact that will allow people to send a text message to loved ones in the event of an emergency. The blind individual can go about and go about their daily life without any assistance from others. A pair of glasses with an integrated obstacle detecting module in the middle, together with a central processing unit, an output device (in the form of a buzzer component), and a power source make up this apparatus. The central processing unit is coupled with the obstacle detecting module and the output device. The CPU requires electricity, which is provided by the power supply. The ultrasonic sensors are managed by the control units, which analyse the data they collect about the barrier in the man's path and then relay the results to him through a buzzer.

The advancements surrounding information and communication technologies have become ubiquitous so much so that governments are now compelled to use them to reduce cost and increase their efficiency. E-government entails the application of information and communication technologies to deliver government services, increase interaction between the government and citizens, and improve the efficiency of the government. This paper details the various aspects of e-government implementation, ICT4D policies, and case studies from the United States and Kenya. Kenya is fast catching up with the rest of the world, having benchmarked the local e-government system with systems in Malaysia, Singapore, the UK, and South Africa among other nations. Limitations that the local implementation faces are detailed with the future of e-government being pitted as fruitful despite myriad infrastructural and training deficiencies.

O artigo investiga como ocorre o processo de ensino e aprendizagem dos sujeitos na Educação a Distância (EaD) e as influências da comunicação e da mediação estabelecidas entre professor-tutor e aluno. Apresenta uma reflexão acerca da produção docente e a relação entre o professor-tutor e o aluno, a fim de compreender como ocorrem os processos de comunicação e aprendizado no ambiente virtual. Discute os obstáculos encontrados neste meio para superar os desafios cotidianos. Analisa a prática do professor-tutor e quais são os saberes necessários à sua atuação mediada por tecnologias da informação e comunicação. Utiliza a metodologia de abordagem qualitativa para analisar dados que foram coletados por meio de um questionário com questões estruturadas abertas e fechadas, disponibilizado por meio do Google Formulários enviado pelo WhatsApp e Messenger para alunos de diversos cursos em EaD de uma Instituição de Ensino Superior. O estudo está fundamentado em Bacich (2015), Barros e Choti (2...

Now a day the internet users are rapidly increased day by day. As technology advances, people are depending more and more on internet-connected smart devices and these kinds of services to store their data and information on the cloud. Data security for both the IoT environment and cloud platform has become crucial. It consists of interconnected smart devices that send, store, and gather data from their environment using embedded technology. Sensors, processors, and communication devices are all part of embedded technology, but it also has some security issues with the information that is held there. According to the scenario, we decided that we design and develop a system that will help to secure our data and enhance security and resolve these issues. The authors' proposed system design and development provides data privacy, authenticity, and integrity for information held in cloud and IoT environments.

The undertaking title is "E-VOTING APPLICATION USING VOTER AUTHENTICATION ".The issue of casting a ballot is as yet basic as far as wellbeing and security. This paper manages the plan and improvement of an online democratic framework utilizing finger impression and Aadhaar card to furnish an elite with high security to the democratic framework. Likewise, we use web innovation to make the casting a ballot framework more pragmatic .The proposed Online Voting System permits the electors to filter their finger impression ,which is then coordinated with an as of now save harm with in an information base that is recovered from Aadhaar card information base of the public authority. The democratic framework is overseen in an easier manner as every one of the clients must login by Aadhaar card number and One Time secret key and snap on his/her good possibility to make the choice. This will build the democratic rate in India and diminishes the expense of casting a ballot interaction. By utilizing biometric unique mark it gives sufficient security which diminishes he bogus votes

The development of information technology is currently growing very rapidly, including the impact on the hardware used. This can be exemplified in the use of hard drives that are starting to switch to SSDs. The process of selecting an SSD product to be used cannot be separated from the sources of information found on the internet. Through the internet, every user can provide reviews, both positive and negative reviews. With the many reviews regarding the review of the Samsung 850 Evo SSD on the NewEgg Store, the author uses it to be processed into information, which will have new knowledge. Based on that, the author makes research, in the form of opinion classification by analyzing sentiment through a text mining approach. In this study, two classification models were used, namely Decision Tree and Support Vector Machine. The results of this study are in the form of a comparison of the 2 models used based on the accuracy and AUC values. Based on research, the Support Vector Machine ...

The mining association algorithm well known as A-priori is one of the most popular data mining algorithms, The mining association algorithm requires two parameters support and confidence to derive rules, The A-priori algorithm scans the database multiple passes to discover the rules, In this research we proposed an algorithm to reduce the multiple passes to just one pass to derive rules with high confidence, which gives the same results and also reduces the time and the effort, the algorithm gives analytical ability for understanding the results because it focusing on small itemsets.

The internet of things (IoT), is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction (Gismalla Abdullah, 2017). The internet of things helps people live and work smarter as well as gain complete control over their lives. A passenger car is a road motor vehicle, intended for the carriage of passengers and designed to seat no more than nine persons. The problems occur in passenger car when there is no device that monitor the quality of air in the car’s cabins. Air Quality Detector is an automatic system that being developed to send the value of Carbon Dioxide (CO2) level in car to LCD display and Blynk App. The existing low-end car do not have any system that can notify the passengers regarding the CO2 level in the car. As a solution, this innovation able to detect exact condition of carbon dioxide leve...

The integration of computer-based technologies interacting with industrial machines or home appliances through an interconnected network, for teleoperation, workflow control, switching to autonomous mode, or collecting data automatically using a variety of sensors, is known as Internet of Things (IoT). When applied inside an industrial context, it is possible to immediately benefit from the analytics obtained, contributing to process optimization, machine health, the safety of workers and asset management. IoT can assist real-time platforms in remotely monitoring and operating a complex production system with minimal intervention of humans. Hence it can be beneficial for hazardous industries, such as mining, by increasing the safety of personnel and equipment while reducing operation costs. An ideal smart automated mine could potentially be achievable by gradually taking advantage of IoT. Currently, different sensors are used in mine-related activities, such as geophones in exploration and blast control, piezometers in dewatering and toxic gas detectors in working frontlines. However, a fully integrated automated system is challenging in practice due to infrastructural limitations in communication, data management and storage. Moreover, the tendency of mining companies to continue with traditional methods instead of relying on untested novel techniques decelerates this progress. In this study, the adaptability of the mining industry to IoT systems and its current development is reviewed. Significant challenges of this progress are investigated and recommendations to develop a comprehensive model suited for different mining sections such as exploration, operation and safety considering flexible technologies such as Wireless Sensor Networks and the introduction of Global Data Management.

in the current years, speech recognition has emerged as an important research area. To carry out further research on automatic speech recognition, a comprehensive review of existing work in this domain stands useful and constructive for the researchers. This paper has presented a recent literature review on speech recognition considering various existing speech corpora, speech features and different models or classifiers used in speech recognition. Different speech databases have been compared in terms of the number of speakers, type of speakers such as native or acted, age and gender of speakers and speech recording environment. Various techniques for speech signal acquisition and pre-processing of the speech signals are also addressed in this work.

We can see a person standing in front of our house, whose duty is to read the energy meter. The person checks the meter board, takes the readings and the bill is received at the customer?s place within two or three days. This procedure from taking the meter readings to generating the electric bills and delivering the electric bills to each customer?s house involves a lot of time as well as money. Sometimes there may be issues like extra bill amount or notifications to the customers even if the bill is paid. To overcome this drawback we have come up with an idea of developing an IOT based smart energy meter which will reduce the errors in bill generation and also save the time of the workers of the electricity board. In this system, the electric meter board will be connected to Arduino board which will take the readings from the meter board with the help of sensor and will give the notifications or to update the readings on the web based system which will be connected through the Wi-...

Nowadays, the traffic over the networks is changing because of new protocols, devices and applications. Therefore, it is necessary to analyze the impact over services and resources. Traffic Classification of network is a very important prerequisite for tasks such as traffic engineering and provisioning quality of service. In this paper, we analyze the variable packet size of the traffic in an university campus network through the collected data using a novel sniffer that ensures the user data privacy. We separate the collected data by type of traffic, protocols and applications. Finally, we estimate the traffic model that represents this traffic by means of a Poisson process and compute its associated numerical parameters.

Nowadays, Natural Language Processing has received the widespread attention from the natural sciences, and sentimental analysis is one of the most widely used NLP applications. In the age of big data, how to find the required information accurately and quickly has become the hotspot of current research. Based on the movie reviews of two movies from the same series, this paper studies the sentimental trend of movies reviews, in order to help the audience obtain a reference for movie choices. Term frequency-Inverse Document Frequency (TF-IDF) algorithm is applied to evaluate the importance of words in the reviews, and TextBlob sentiment analysis library of Python software is used to grade the sentiment scores of the two films. Finally, the sentiment score graph is drawn, which provides a strong support for the further identification of the movie characteristics of two films from the same series. What’s more, Support vector machines (SVM) model is utilized to do the classification of t...

The handling, analysis, and access of data by enterprises has been completely transformed by cloud computing, which provides many benefits. But it also poses serious security risks, especially when it comes to data protection, confidentiality, integrity, availability, and location. Strong security measures are crucial to addressing these issues and guaranteeing the dependability of cloud services. The RSA (Rivest-Shamir-Adleman) algorithm is one useful tool for improving data security in cloud computing settings. By using prime factorization complexity for both encryption and decryption, the popular asymmetric cryptographic technique RSA provides secure communication on erratic networks. A vital component of cryptography, RSA was created in 1977 and improves digital privacy, integrity, and authenticity by doing away with the requirement for shared secret keys. Its adaptability may be seen in a number of digital applications, such as internet connection security protocols and email security. Cryptographic libraries like OpenSSL and Bouncy Castle, which provide key generation, encryption, decryption, and digital signature verification features, are necessary for the implementation of RSA encryption in cloud computing. Working together, researchers, cloud providers, and cybersecurity specialists can create RSAbased security solutions that are specifically designed for cloud environments. Data security is further strengthened by major cloud providers like Microsoft Azure and Amazon Web Services including RSA encryption capabilities. Encouraging cloud computing providers to handle, transfer, and store data securely is the main goal of RSA integration. RSA encryption improves total data security and dependability in cloud environments by guaranteeing data confidentiality, integrity, and availability. For RSA implementation to be optimized and regulatory standards compliance to be ensured, more research and development is necessary due to persistent issues including scalability and effective key management.

O presente paper traz a abordagem da educação a distância (EAD), embasada na utilização da Inteligência artificial (IA) que está presente na sociedade atual , sendo aplicada em diversas áreas. O objetivo deste estudo é identificar e evidenciar a relação entre a integração da inteligência artificial na educação a distância, onde são desenvolvidos ambientes virtuais para os estudantes e recursos com a integração da inteligência artificial (IA). No campo da educação, a tecnologia está transformando o processo de ensino e aprendizagem, o que fica evidente nas abordagens de ensino a distância, onde são desenvolvidos ambientes virtuais para os estudantes e recursos com a inteligência artificial (IA) estão disponíveis. A metodologia utilizada nesse paper é um estudo exploratório e descritivo, utilizando uma abordagem qualitativa e a pesquisa bibliométrica como principal ferramenta teórica para discutir a contribuição da Inteligência Artificial (IA) na educação a distância (EAD). Ele se baseia em artigos sobre Ensino a Distância (EaD), foram consultados vários autores que estudaram, investigaram e escreveram sobre os assuntos abordados no paper.

Financial fraud detection remains a critical challenge due to the increasing complexity of fraudulent activities and stringent data privacy regulations. Traditional fraud detection methods, including machine learning and deep learning approaches, suffer from limitations such as high false positive rates, data security risks, and scalability issues. To address these challenges, this paper proposes a Cloud-Enabled Federated Learning (FL) framework integrated with Graph Neural Networks (GNNs) for privacy-preserving financial fraud detection. The framework enables collaborative learning across multiple financial institutions while ensuring data confidentiality by leveraging federated learning. GNNs are employed to model transactional relationships, effectively capturing complex fraud patterns in a graph-based representation. Additionally, adaptive aggregation mechanisms enhance communication efficiency in the cloud environment. The proposed framework is evaluated using the Bank Account Fraud Dataset Suite (NeurIPS 2022), demonstrating superior performance. Experimental results show that the model achieves 99.0% accuracy, 98.5% precision, 97.2% recall, and 97.8% F1-score, significantly outperforming existing FL-based (95.5% accuracy) and traditional ML (88.7% accuracy) models. Furthermore, the AUC-ROC score of 99.1% highlights the model's robustness in fraud detection. The proposed approach ensures high detection accuracy, improved scalability, and reduced communication overhead (35MB) while preserving data privacy. This research establishes a scalable and efficient fraud detection framework, making it a viable solution for real-world financial applications.

Financial fraud detection remains a critical challenge due to the increasing complexity of fraudulent activities and stringent data privacy regulations. Traditional fraud detection methods, including machine learning and deep learning approaches, suffer from limitations such as high false positive rates, data security risks, and scalability issues. To address these challenges, this paper proposes a Cloud-Enabled Federated Learning (FL) framework integrated with Graph Neural Networks (GNNs) for privacy-preserving financial fraud detection. The framework enables collaborative learning across multiple financial institutions while ensuring data confidentiality by leveraging federated learning. GNNs are employed to model transactional relationships, effectively capturing complex fraud patterns in a graph-based representation. Additionally, adaptive aggregation mechanisms enhance communication efficiency in the cloud environment. The proposed framework is evaluated using the Bank Account Fraud Dataset Suite (NeurIPS 2022), demonstrating superior performance. Experimental results show that the model achieves 99.0% accuracy, 98.5% precision, 97.2% recall, and 97.8% F1-score, significantly outperforming existing FL-based (95.5% accuracy) and traditional ML (88.7% accuracy) models. Furthermore, the AUC-ROC score of 99.1% highlights the model's robustness in fraud detection. The proposed approach ensures high detection accuracy, improved scalability, and reduced communication overhead (35MB) while preserving data privacy. This research establishes a scalable and efficient fraud detection framework, making it a viable solution for real-world financial applications.

One of the causes of motor vehicle accidents in Sri Lanka is driver inattention or drowsiness. In the field of intelligent transportation systems, continuous research and development are conducted to address this contemporary issue. Many approaches, such as driver assistance and drowsiness detection systems, have been proposed to overcome this fatality. The purpose of this research was to implement a product that can maximise road safety while improving the transport sector's efficiency and reliability of the logistics chain to reinforce the country's economic growth. In this paper, the correlation between the preprocessed vehicular parameters and visual features are used to analyse the driver state and make predictions of the driver's perfomance. The proposed system uses computer vision and fuzzy logic inference implemented on the singleboard computer Raspberry Pi to detect facial features and to determine the driver's drowsiness state, an ELM327 is used to read the vehicle parameters from the Electronic Control Unit (ECU) and motion sensors were used to obtain the steering angle. The data acquired is stored in a cloud platform using REST API. The database also contains driver details. The system uses a fingerprint scanner to identify the driver. An actuator was installed in the vehicle to alert the driver when the system detects inattentiveness. Overall the proposed project provided satisfying experimental results. It can be used as a solution to improve road safety and a supporting tool for the logistics sector to monitor vehicles and driver performance.

The safe and effective administration of data in cloud storage systems poses major issues in the current digital environment. Deduplicable Proof of Storage (DPOS) is a viable solution that successfully addresses these issues by utilizing sophisticated encryption algorithms and symmetric keys. Symmetric key encryption techniques are used by DPOS to encrypt data prior to storage, guaranteeing secrecy and promoting effective data deduplication. DPOS enhances storage efficiency by optimizing storage resources by detecting and removing redundant data copies. DPOS also makes proof of storage easier, allowing users to confirm data accuracy without laborious decryption processes. The significance of data integrity in contemporary digital contexts is emphasized in this study, which also examines the technical challenges of implementing an integrity auditing protocol within Sec-DPoS. The approach includes critical steps including challenge, reaction, and verification, all of which are necessary to guarantee the auditing process' efficacy. Moreover, assessing Sec-DPoS effectiveness using performance measures offers important information about its scalability and efficacy in comparison to other systems. A thorough architecture diagram provides a visual depiction of the integrity auditing procedure within Sec-DPoS by highlighting the interactions between various components. In summary, this study offers a methodical framework for putting integrity auditing techniques into practice, guaranteeing the dependability and security of data in cloud storage settings.

Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their license numbers. ANPR can be used in the detection of stolen vehicles. The detection of stolen vehicles can be done in an efficient manner by using the ANPR systems located in the highways. This method presents a recognition method in which the vehicle plate image is obtained by the digital cameras and the image is processed to get the number plate information. An image of a vehicle is captured and processed using image pre-processing algorithm. In this context, the number plate area is localized using edge detection method and the characters acquired are segmented using segmentation method. The segmented characters are passed to Optical character reader (OCR) which gives exact characters of the number plate and are stored in database. The image pre-processing is done using Gaussian Blur method, conversion of RGB (Red, Green, Blue) to grayscale, Edge detection using sobel followed by contour detection and license plate is extracted. OCR is run on the Region of Interest (ROI) given by the contour analysis. The OCR provides the vehicle’s num-ber obtained from the number plate and is stored in the database which is used for surveillance and tracking vehicle’s location. The software used in the process is Python version 3 and different python libraries like Tesseract OCR, OpenCV.

Recently, most of the applications will not need precise support information for frequent pattern. They approximate the frequency of every frequent pattern with a guaranteed maximal error bound. But, these algorithms generally have some problems and challenges while processing large-scale data. Therefore, the parallel approach has emerged as an important research topic. This paper implements the algorithm ModifiedRPset in a parallel manner while creating NCFP-tree. It follows the concepts such as considering closed patterns, finding C(X)s for closed patterns and applying greedy set cover algorithm which are same as in the previous algorithms MinRPset and ModifiedRPset. The experiment results show that the proposed algorithm gives better execution time for finding a small number of representative patterns to best approximate all other frequent patterns comparing to the algorithm ModifiedRPset.

Grupo 2.2. Docência na Educação a Distância: Práticas e estratégias pedagógicas dos diferentes agentes O presente artigovisa apresentar uma análise da webconferência como objeto de aprendizagem, enfocando sua utilização enquanto recurso pedagógico. A pesquisa foi realizada no âmbito do Curso de Licenciatura em Artes Visuais, modalidade à distância, da UFES, na disciplina de LIBRAS, no decorrer do módulo VII. Nos momentos virtuais, foram observados todos os alunos participantes, porém, nos momentos presenciais, buscou-se um acompanhamento mais detalhado dos alunos matriculados no Polo UAB de Itapemirim/ES. Foram utilizados os registros do ambiente virtual de aprendizagem -AVA e, à luz de conceitos teóricos, buscouse compreender algumas aproximações e divergências, bem como as contribuições que a webconferência, utilizada enquanto objeto de aprendizagem, tem trazido ao desenvolvimento do processo ensino aprendizagem do Curso em questão.

Machine learning is a field of computer science
which gives computers an ability to learn without being
explicitly programmed. Machine learning is used in a variety
of computational tasks where designing and programming
explicit algorithms with good performance is not easy.Big data
are now rapidly expanding in all science and engineering
domains. The potentials of these increased volumes of data are
obviously very significant to every aspect of our lives. To aid
us in decision making and future predictions requires new
ways of thinking and new learning techniques to address the
various challenges.Traditional analytical approaches are
insufficient to analyze big data because they are highly
scalable and unstructured data captured in real time. Machine
learning (ML), addresses this challenge, by enabling a system
to automatically learn patterns from data that can be leveraged
in future predictions.This paper reviews some machine
learning techniques for big data highlighting their
uses/applications, strengths and weaknesses in learning data
patterns. The techniques reviewed include Bayesian network,
association rules, naïve bayes, decision trees, nearest neighbor
and super vector machines (SVM).