Accurate prediction of cancer can play a crucial role in its treatment. The procedure of cancer detection is incumbent upon the doctor, which at times can be subjected to human error and therefore leading to erroneous decisions. Using machine learning techniques for the same can prove to be beneficial. Many classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are proven to produce good classification accuracies. The following study models data sets for breast, liver, ovarian and prostate cancer using the aforementioned algorithms and compares them. The study covers data from condition of organs, which is called standard data and from gene expression data as well. This research has shown that SVM classifier can obtain better performance for classification in comparison to the ANN classifier.

A cyber physical system (CPS) is a complex system that integrates computation, communication, and physical processes. Digital manufacturing is a method of using computers and related technologies to control an entire production process. Industry 4.0 can make manufacturing more efficient, flexible, and sustainable through communication and intelligence; therefore, it can increase the competitiveness. Key technologies such as the Internet of Things, cloud computing, machine-to-machine (M2M) communications, 3D printing, and Big Data have great impacts on Industry 4.0. Big Data analytics is very important for cyber-physical systems (CPSs), digital manufacturing, and Industry 4.0. This paper introduces technology progresses in CPS, digital manufacturing, and Industry 4.0. Some challenges and future research topics in these areas are also presented.

Leakage of gas is a major issue in the industrial sector, residential buildings, and gas-powered vehicles, one of the preventive methods to stop accidents associated with gas leakage is to install gas leakage detection devices. The focus of this work is to propose a device that can detect gas leakage and alert the owners to avert problems due to gas leakages. The system is based on a microcontroller that employs a gas sensor as well as a GSM module, an LCD display, and a buzzer. The system was designed for gas leakage monitoring and alerts with SMS via an Arduino microcontroller with a buzzer and an MQ2 gas sensor. The circuit contains a Microcontroller MQ2 gas sensor, buzzer, LCD display, and GSM module, when the sensor detects gas leakage it transmit the information to the Microcontroller while the microcontroller makes a decision and then forwarded a warning message to the user as SMS to a mobile phone for decision to be taken accordingly. The output of this research will be significant in averting problems associated with gas leakages now and in future. 

Quality of electricity with continuity is the reliability of the power system which is inversely proportional with the duration of power supply interruption. It depends on some expected or unexpected faults/failures on the systems, speed of protecting systems, preventive maintenance, and motivation of technical staffs. The detailed study of the distribution system is more crucial as its reliability is the concern of utility’s fame, service, customers’ satisfactions and reflects to the overall revenue. The relevant articles from the various sources has been collected and analyzed different reliability indices with their significance. Also, to realize the methodology related with reliability analysis, a comparative study among its different components has been carried out and the best techniques for maintaining system reliability are suggested.

As one of the countries situated in the Pacific Ring of Fire, the Philippines suffers from an inexhaustible number of natural disasters every year. One of the most destructible ones is the occurrence of earthquakes. Because of the high damage that earthquakes incur, along with their inevitability and unpredictability, developing effective methods of earthquake damage mitigation as well as disaster preparedness is imperative to lessen the negative impacts it is capable of producing in communities. One efficient way of doing this is by implementing an earthquake early warning (EEW) system that is capable of sending message alerts to receivers to warn them in the event of a hazardous earthquake. With this objective, this study centers on creating an earthquake detector with SMS messaging to function as an EEW system with an added advantage of being low-cost to make it more accessible to the public. Using electronic components based on an Arduino Mega 2560 and a Global System for Mobile Communications (GSM) module, the earthquake detector and its alert message system were created. A series of tests in different locations across Butuan City was then performed to assess the device’s accuracy in measuring different Intensity levels when subjected to surface vibrations. Comparative analysis showed that its recorded values. Corresponded with the values obtained from accelerometer-based mobile applications. In conclusion, the study was deemed functional in its ability to detect low and high surface vibrations, which proves that it is successful in detecting earthquake tremors and vibrations in the event of an earthquake.

This project is focused on developing a Fully Automatic Hydroponics system which helps in monitoring and controlling temperature, Humidity, pH and EC in Hydroponics. Hydroponics is a method of growing crops without soil. Plants are grown in rows or on trellises, just like in a traditional garden, but they have their roots in water rather than in dirt. Although, there are different ways in which hydroponics can be implemented, there is no individual system which can measure and control pH and EC level of nutrient solution along with its surrounding temperature and humidity automatically. We use PIC16F877A microcontroller and four pumps, three of which are used to pump water, nutrient solution, pH solution and the fourth pump is used to control the humidity. A fan is used to control the temperature which increases its speed as the temperature increases. The pumps are turned on depending on the EC and pH values obtained from the electrodes. A passive LCD display is used to display variations in the values. Different Analysis like water usage, plant growth in comparison with regular farming method and hydroponics is successfully completed which results in hydroponics system is significant method in comparison with soiled cultivation method in terms of yield and water usage. This project is expected to produce high yield crops by taking minimal space, makes work easier for farmers in growing of plants, and also consumes less amount of water when compared to traditional method resulting in conservation of water.

Air pollution is responsible for an estimated 5.5 million deaths in 2013 which costed the global economy approximately US$225 billion in lost labor income. To address the problems caused by air pollution, this study aims to develop a low-cost and portable air quality monitoring system that detects the levels of CO, PM2.5, PM10, temperature, and humidity. Using Internet of Things (IoT), the data that the system gathers can be accessed through the internet. Moreover, the system assesses the obtained data through a comparative analysis with the AQI. The Iterative Design Loop method was used in the development of the air quality monitoring system. Furthermore, the sensors were programmed using the Arduino Integrated Development Environment (IDE). Using the Welch’s t-test, it was found that the obtained data of the system is not significantly different to that of the standard air quality monitoring systems. To achieve more accurate data from the developed system, the raw data of the developed and standard system were calibrated through an equation from the trendline. Through the use of Acer CloudProfessor, the study successfully developed an air quality monitoring system that can be accessed through the internet.

The evolving information technology abridges the hardship in the daily life of consumers all over the world, hence the application of this knowledge in the irrigation field is necessary nowadays. The exponential growth of demand in food is due to the ever-evolving population of the world, thus it becomes necessary to expand the present area of cultivation. Considering the present situation of weather change due to global warming as a result of industrial activities, farming via irrigation is the reliable process of food production. Water remains the only source for survival for crop production, thus optimal management and proper use of water become pertinent with the ever-increasing land for irrigation. Arduino based automatic plant irrigation control system; provides a simple approach to automated irrigation. This work makes use of the GSM module for the notification of the user about the situation in the farm, this project aims to design and implement an automatic plant irrigation control system using Arduino and GSM module. In this proposed system, there are two main parts hardware and software units. Mechanical units which are the hardware unit comprises of instrumentation systems and watering irrigation systems. The equipment system is based on microcontroller, flow meter, moisture sensor, LCD, and GSM module. The software part comprises of C++ code, this is to enable the linkage between various modules. The main control of this system is the microcontroller unit that serves as the brain for coordinating control for various modules of the system, it synchronizes and operates the watering system and notifies the user about the condition of the field and watering section via GSM module. Implementation of this project will significantly help in a water-saving of about 30 – 50% as compared to the conventional watering system like the sprinkler, improve growth and discourage weeds because water will only be served to the needed area, simple method and timer-based system for automatic watering can be incorporated for efficiency.

Geopolymers are inorganic aluminosilicate polymers that solidify into ceramic-like substances at tempera-tures close to ambient. The elements in silicate oxide (SiO2) and aluminum oxide (Al2O3) are essential for the hardening of geopolymers because they combine with other elements to create N-A-S-H formation, which gives the material its distinctive strength. Geopolymers based on industrial wastes are increasingly being used to stabilize soft soils. Fly ash, GGBS, metakaolin, glass powders, and others are a few of the industrial wastes that aid in synthesizing geopolymers. Several experimental studies were carried out to determine the mechanical strength, durability, and microstructure im-provement of soft soils stabilized with geopolymers. Some of the experiments include X-ray diffraction (XRD), scan-ning electron microscopy (SEM), unconfined compression testing (UCS), and durability testing. The main objective of this review was to assess the different types of binders, binder ratios, alkali activator types, alkali activator concentra-tions, and other parameters used in synthesizing geopolymers. The binder's proportion varies between 5% and 30% of the soil's dry weight. Researchers commonly use sodium silicate (Na2SiO3) and sodium hydroxide (NaOH) solution for the alkali activator. Since the unconfined compression test is one of the quickest and least expensive ways to determine shear strength, most researchers were used to measure stabilized soils' mechanical strength. This paper highlights the most frequently used industrial wastes used to synthesize geopolymers. The review enables researchers to acquire es-sential and complementary inputs for future research.

Face recognition (FR), the process of identifying people through facial images, has numerous practical applications in the area of biometrics, information security, access control, law enforcement, smart cards and surveillance system. Convolutional Neural Networks (CovNets), a type of deep networks has been proved to be successful for FR. For real-time systems, some preprocessing steps like sampling needs to be done before using to CovNets. But then also complete images (all the pixel values) are passed as input to CovNets and all the steps (feature selection, feature extraction, training) are performed by the network. This is the reason that implementing CovNets are sometimes complex and time consuming. CovNets are at the nascent stage and the accuracies obtained are very high, so they have a long way to go. The paper proposes a new way of using a deep neural network (another type of deep network) for face recognition. In this approach, instead of providing raw pixel values as input, only the extracted facial features are provided. This lowers the complexity of while providing the accuracy of 97.05% on Yale faces dataset.

Accurate prediction of cancer can play a crucial role in its treatment. The procedure of cancer detection is incumbent upon the doctor, which at times can be subjected to human error and therefore leading to erroneous decisions. Using machine learning techniques for the same can prove to be beneficial. Many classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are proven to produce good classification accuracies. The following study models data sets for breast, liver, ovarian and prostate cancer using the aforementioned algorithms and compares them. The study covers data from condition of organs, which is called standard data and from gene expression data as well. This research has shown that SVM classifier can obtain better performance for classification in comparison to the ANN classifier.

Quality of electricity with continuity is the reliability of the power system which is inversely proportional with the duration of power supply interruption. It depends on some expected or unexpected faults/failures on the systems, speed of protecting systems, preventive maintenance, and motivation of technical staffs. The detailed study of the distribution system is more crucial as its reliability is the concern of utility’s fame, service, customers’ satisfactions and reflects to the overall revenue. The relevant articles from the various sources has been collected and analyzed different reliability indices with their significance. Also, to realize the methodology related with reliability analysis, a comparative study among its different components has been carried out and the best techniques for maintaining system reliability are suggested.

Controlling drum level is a major and crucial control objective in thermal power plant steam boilers. The drum level as a controlled variable is highly characterized by complex non-linear process dynamics as well as measurement noise and long-time delays. Developing a data-driven process model is particularly advantageous as it could be built from ongoing operational data. Such a model could be used to assist existing controllers by providing predictions regarding the drum level. The aim of this paper is to develop such a model and to propose a control architecture that can be easily integrated into existing control hardware. For that purpose, different neural networks are used, Multilayer Perceptron (MLP), Nonlinear Autoregressive Exogenous (NARX), and Long Short Term (LSTM) neural networks. LSTM and MLP were able to capture the dynamics of the process, but LSTM showed superior performance. The results demonstrate that the use of traditional machine learning criteria to evaluate a process model is not necessarily adequate. Using the model in an open-loop and a closed-loop simulation is more suitable to test its ability to capture the dynamics of the process. A novel architecture that integrates the process model within an existing closed-loop controller is proposed. The architecture uses adaptive weights to ensure that a good model is given more influence than a bad model on the controller’s output.

The final stage, which is the building paint or the adopted design, is where most real estate developers and constructors struggle. Where extensive painting is required, which takes a lot of time, effort, and accuracy from the firm doing the work. Additionally, it might be challenging to decide on the precise color grades for the design and calculate the right amount of paint to use for the job. Where these activities are extremely expensive, and the complex implementation is accompanied by worries and skepticism. These are the motivations behind the development of painting machines that blend colors. Artificial intelligence is used in the machine's design to make it efficient and quick at what it does. High accuracy is needed when selecting the proper colors, and this machine is distinguished by its ability to select the proper color tone. The color sensor (TCS34725 RGB) determines the relevance and accuracy of the desired color by comparison with the system database with the assistance of the light sensor (STM32), which measures the degree of illumination of the chosen place. By combining basic colors, this technique saves the customer the hassle of looking at specialized stores for the level of color they require. By giving the system the codes assigned to each color, it may also blend colors. The system also has the feature of controlling the machine remotely via smart phone application by enabling bluetooth and wifi features.

During the past years, it is observed from the literature that, identification of the brain tumor identification in human being is gaining popularity. Diagnosing any disease without manual interaction with great accuracy makes computer science research more demanding, therefore, the present work is related to identify the tumor clots in the affected patients. For this purpose, a well-known Safdarganj Hospital, New Delhi, India is consulted and 2165 Magnetic Resonance Images (MRI) of a single patient are collected through scanning, and interpolation technique of numerical method used to identify the accurate position of the brain tumor. A system model is developed and implemented by the use of Python programming language and MATLAB for the identification of affected areas in the form of a contour of a patient. The desired accuracy and specificity are evaluated using the computed results and also presented in the form of graphs.

Cases of theft and robbery of computers, CCTV equipment, and LCD projector have become more frequent in schools. In addition, fire hazards are great threat to educational institutions where expensive learning materials are kept. Such incidents could be lessened and avoided if schools are equipped with appropriate security systems capable of monitoring and informing people about the coming possible danger. Thus, the development of Fire and Motion Early Warning Device (FMEWD) is timely and relevant. FMEWD consists of a website and interconnected devices and sensors intended to provide an efficient and effective warning system for preventing incidents relating to fire, smoke, and intrusion within an office. Upon detection, the system automatically sends an email and SMS to registered users. This study used the Agile Development Model which allows features to be delivered quickly and more frequently with higher levels of predictability. Evidently, the integration of different technologies conceptualized by the researcher addresses the pressing security concerns faced by educational institutions like NEUST.

This project is focused on developing a Fully Automatic Hydroponics system which helps in monitoring and controlling temperature, Humidity, pH and EC in Hydroponics. Hydroponics is a method of growing crops without soil. Plants are grown in rows or on trellises, just like in a traditional garden, but they have their roots in water rather than in dirt. Although, there are different ways in which hydroponics can be implemented, there is no individual system which can measure and control pH and EC level of nutrient solution along with its surrounding temperature and humidity automatically. We use PIC16F877A microcontroller and four pumps, three of which are used to pump water, nutrient solution, pH solution and the fourth pump is used to control the humidity. A fan is used to control the temperature which increases its speed as the temperature increases. The pumps are turned on depending on the EC and pH values obtained from the electrodes. A passive LCD display is used to display variations in the values. Different Analysis like water usage, plant growth in comparison with regular farming method and hydroponics is successfully completed which results in hydroponics system is significant method in comparison with soiled cultivation method in terms of yield and water usage. This project is expected to produce high yield crops by taking minimal space, makes work easier for farmers in growing of plants, and also consumes less amount of water when compared to traditional method resulting in conservation of water.

Geopolymers are inorganic aluminosilicate polymers that solidify into ceramic-like substances at tempera-tures close to ambient. The elements in silicate oxide (SiO2) and aluminum oxide (Al2O3) are essential for the hardening of geopolymers because they combine with other elements to create N-A-S-H formation, which gives the material its distinctive strength. Geopolymers based on industrial wastes are increasingly being used to stabilize soft soils. Fly ash, GGBS, metakaolin, glass powders, and others are a few of the industrial wastes that aid in synthesizing geopolymers. Several experimental studies were carried out to determine the mechanical strength, durability, and microstructure im-provement of soft soils stabilized with geopolymers. Some of the experiments include X-ray diffraction (XRD), scan-ning electron microscopy (SEM), unconfined compression testing (UCS), and durability testing. The main objective of this review was to assess the different types of binders, binder ratios, alkali activator types, alkali activator concentra-tions, and other parameters used in synthesizing geopolymers. The binder's proportion varies between 5% and 30% of the soil's dry weight. Researchers commonly use sodium silicate (Na2SiO3) and sodium hydroxide (NaOH) solution for the alkali activator. Since the unconfined compression test is one of the quickest and least expensive ways to determine shear strength, most researchers were used to measure stabilized soils' mechanical strength. This paper highlights the most frequently used industrial wastes used to synthesize geopolymers. The review enables researchers to acquire es-sential and complementary inputs for future research.

Dementia is the world's most deadly disease. A degenerative disorder that affects the thinking, memory, and communication abilities of the human brain. According to World Health Organization, more than 40 million people worldwide suffer from this illness. One of the most common methods for analyzing the human brain, including detecting dementia, is using MRI (Magnetic resonance imaging) data, which provides insight into the inner working of the human body. Using MRI images a deep Convolution neural network was designed to detect dementia, we are utilizing image processing to help doctors detect diseases and make decisions on observation, in an earlier stage of the disease. In this paper, we are going to get to the bottom of the DenseNet-169 model, to detect Dementia. There are approximately 6000 brain MRI images in the database for which the DenseNet-169 model has been used for classification purposes. It is a Convolution Neural Network (CNN) model that classifies Non-Dementia, Mild Dementia, Severe Dementia, and Moderate Dementia. The denseNet-169 model helps us determine Dementia disease. And also present the 97% accuracy for clarification of disease is present in the patient body. we are conducted this survey for providing effective disease prediction model for physicians to conclude that the disease stage is accurate and provide proper treatment for that.  

In this work, we formulated a deterministic mathematical continuous time model for the detection and elimination of a non-life threatening disease (head lice) by considering a fixed (constant) population size during the epidemic period. The formulated mathematical model was normalized for easy analysis, the model’s properties were obtained, as well as the disease free equilibrium point, the local stability and the basic reproduction number. We adopted MATLAB programing language to carry out the numerical simulation of the nonlinear ordinary differential equation, as well as simulation of different model state variables and effects of different model parameters on the state variables over time. Our result shows that early detection and treatment will lead to termination of the disease.