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.

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.

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.

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. 

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.

In the modern era agriculture development is the highly contribute field of food security. Data Science is one of the top analysis experimental methods for forecasting and mapping synchronize. In our study, we experiment with three major parameters (Rainfall, Relative Humidity and Temperature) that can be affected crop production rate as well as area-based mapping. To complete the procedure, the cluster groping and prediction system has created a machine learning BOT combined analysis system. Bangladesh and its 13 areas with 46 years of data have visualized with proper analysis and build up a 2D map of each separate production area. Multi Linear Regression (MLR) and KMean Clustering is the main key point algorithm for the production analysis. Experiment analyzing, we can see that some elements of our environment are closely associated with the productivity of the crop. An untactful environmental change on parameters (Rainfall, Humidity, and Temperature) reduces agricultural productivity by 32-38%. Developed model accuracy 91.25% forecasting methodological analysis for production mapping and prediction. Extreme population food security has ensured ICT and Agriculture combine BOT & EVPM method is essential for the scientific world. This study will allow farmers to choose the proper crop in the right environmental condition, which will play a key role in strengthening the economy of the country.

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.

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.

In the world ergonomics is involved everywhere, where is work there is a risk factor. Musculoskeletal disorder (MSDs) is a major risk factor in human life because it affects bones, joints, muscles, and connective tissues of whole human body parts such as the neck, shoulder, arms, wrists, hips, legs, thigh, knee, ankles, etc. so mainly our study focus on musculoskeletal disorders. This study there has used questionnaires in four factors those are socio-demographic, psychological, occupational, and biomechanical. In these factors number of questions were included in the data has been collected. In addition, there was the Nordic section in questions from that we analyzed the pain in different parts of the human body. The study concentrated on the business, education, industry, and healthcare sectors in Hyderabad, Kotri, and Jamshoro. University students and teachers, retail salespeople, manufacturing industry workers, nurses, doctors, nursing assistants, and other health professionals comprised the sample group. The questionnaires were fully completed by 50% of the respondents, resulting in a sample of 116 workers. The majority of the participants were private employees with one to fifteen years of experience in teaching or caring. In this study data has been analyzed through Co-relation between four factors with the Nordic section and ANOVA test through excel and it gives the value of p is also less than 0.05 so we cannot reject the null hypothesis. Over this study it has been analyzed that population is evolving in problems and there should be the proper implementation of ergonomics and safety rules. Test gives the values are not significant and null hypothesis should not reject and it should be improving.

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.

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.

We propose a stage structure predator-prey model with a partially dependent predator and prey conservation. It is taken that the environment has been divided into two disjoint regions, namely, unreserved and reserved areas, where a predator is not allowed to enter the latter. The first model describes four species: prey refuge (prey in the reserved zone), prey in the unreserved zone, mature and immature predators. The predator is partially dependent on the prey in the unprotected area. The existence of ecological equilibria and their local and global stability is investigated. By using the Lyapunov theorem, sufficient conditions on the global stability of the equilibriums are obtained. Some numerical simulations show the viability of our results. The results show that the reserved area has a stabilizing impact on the stage structure predator-prey model.

In the modern era agriculture development is the highly contribute field of food security. Data Science is one of the top analysis experimental methods for forecasting and mapping synchronize. In our study, we experiment with three major parameters (Rainfall, Relative Humidity and Temperature) that can be affected crop production rate as well as area-based mapping. To complete the procedure, the cluster groping and prediction system has created a machine learning BOT combined analysis system. Bangladesh and its 13 areas with 46 years of data have visualized with proper analysis and build up a 2D map of each separate production area. Multi Linear Regression (MLR) and KMean Clustering is the main key point algorithm for the production analysis. Experiment analyzing, we can see that some elements of our environment are closely associated with the productivity of the crop. An untactful environmental change on parameters (Rainfall, Humidity, and Temperature) reduces agricultural productivity by 32-38%. Developed model accuracy 91.25% forecasting methodological analysis for production mapping and prediction. Extreme population food security has ensured ICT and Agriculture combine BOT & EVPM method is essential for the scientific world. This study will allow farmers to choose the proper crop in the right environmental condition, which will play a key role in strengthening the economy of the country.

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.

High-performance concrete is a specialized series of concrete designed to provide superior mechanical and physical properties that cannot be achieved through the conventional design. Using high-performance concrete in the construction field can reduce the dead weight, provide a longer span, and increase the service life. In this paper, a full experimental program is conducted to study the effect of glass and carbon fibers on the flexural behavior of reinforced concrete (RC) beams. A total of 15 RC beam specimens are prepared and divided into four main groups according to their added fiber materials, which are (S- Steel fiber), and E (E- Glass fiber), carbon fiber, and S-Glass with carbon fiber. All the reinforced fibers are used at 1.5% of the cement weight. The beams are reinforced using the fiber materials at the hinging zone then tested under concentrated static load placed at the mid-span. The results show that using high-performance fibers can improve the ultimate load capacity, ductility, and absorption energy of the RC beams.

In recent years, the retail market industry has taken a broad form to sell the products online and also to give the opportunity to customers to provide their valuable feedbacks, suggestions and recommendations. The opinion summarization and classification systems extract and identify a range of opinions about different online available products in a large text-based review set. This paper addresses and reviews the concepts of automatic identification of the sentiments expressed in the English text for Amazon and Flipkart products using Random Forest and K-Nearest Neighbor techniques. It presents a detailed comparative study of such existing sentiment analysis algorithms and methodologies on the basis of five key parameters. It results in evaluating their performance in terms of parameter usage and contributions. The paper also discusses their experimental results and challenges found. Therefore, this study shows the maximum usage of feature extraction, positive-negative sentiment, Amazon web source, mobile phone for a large set of reviews in the existing algorithms.

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.

Biometric systems have been used in a wide range of applications. In this paper, we have introduced an online signature verification system using deep neural network models. The proposed system is designed to be used in a production environment and has accuracies on par with the state-of-the-art signature verification methods. It authenticates much faster than most of the existing signature verification systems (less than 2 seconds). To achieve better accuracies and faster training times, a feature vector with 42 features, both static and dynamic, is obtained from the signature sample. This feature vector is fed into the user identification model, which predicts the identity of the user with about 99% accuracy and based on this prediction, the user authentication model predicts if the signature is genuine or forged for that recognized user, with about 98% accuracy. The best possible accuracy achieved by the proposed system for 40 users is 97.5% and EER about 2%. The dataset from the Signature Verification Competition 2004 (SVC2004) was used to assess the performance of the proposed system. The results show that the proposed system competes with and even outperforms existing methods.