Management of Renewable Sources of Energy: A Case on Rice Bran oil and Vegetable oils of Bangladesh Perspective

Renewable energy is a now burning issue for sustainable development. Moreover, it is also environmentally compatible. Bangladesh produces huge amount rice every year. From this, a significant amount of wastes are generated from rice. Rice bran is one of them. On the other hand oil seeds such as coconut, soybean, pulm and mustard are available in Bangladesh. In this view, rice bran oil and vegetable oils are considered for a case study for renewable sources of energy and alternative fuel for lighting purposes of Bangladesh.Key words: Rice bran oil; Vegetable oil; Renewable energy; Management; Banglades

Management Information Systems of the Jamuna Fertilizer Company Limited: A Case Study

Through this study, an endeavour has been taken to emphasize on the need of the management information systems (MIS) in Jamuna Fertilizer Company Limited (JFCL), especially to identify the potential areas wherein the MIS could be effectively used. Management information system (MIS) or computer information system (CIS) consists of five related components: hardware, software, people, procedure, and collections of data. The term information technology (IT) represents the various types of hardware and software used in information systems, including computer and networking equipments. There is some lacking in business solving networking system like MIS/CIS/IT in the JFCL. Main barriers are a lack of leadership, motivation and shortage of skill manpower to implement MIS in JFCL.Key words: MIS; JFCL; BCIC; Case Stud

Telemarketing outcome prediction using an Ensemblebased machine learning technique

Business organisations often use telemarketing, which is a form of direct marketing strategy to reach a wide range of customers within a short time. However, such marketing strategies need to target an appropriate subset of customers to offer them products/services instead of contacting everyone as people often get annoyed and disengaged when they receive pre-emptive communication. Machine learning techniques can aid in this scenario to select customers who are likely to positively respond to a telemarketing campaign. Business organisations can use their CRM-based customer information and embed machine learning techniques in the data analysis process to develop an automated decisionmaking system, which can recommend the set of customers to be communicated. A few works in the literature have used machine learning techniques to predict the outcome of telemarketing, however, the majority of them used a single classifier algorithm or used only a balanced dataset. To address this issue, this article proposes an ensemble-based machine learning technique to predict the outcome of telemarking, which works well even with an imbalanced dataset and achieves 90.29% accuracy

概日時計におけるインターロック逆位相振動子の設計原理

In system biology, mathematical models have long tradition are used to understand complex biological control processes/ systems, for example, circadian clock oscillatory mechanism. Circadian rhythms (~24 hour) is ubiquitous in almost the living species ranging from mammals to cyanobacteria shows the robustness of key oscillatory features such as the phase, period and amplitude against external and internal variations. These autonomous oscillations are formed by the complex interactions of the interactive molecules. A transcriptional-translational feedback loop is typically characterized as a common principle for this sustained oscillations. Recently studies, it has broadly been established that the robustness of biochemical oscillators, like the Drosophila circadian clocks, can be generated by interlocked transcriptional-translational feedback loops, where two negative feedback loops are coupled through mutual activations. The mechanisms by which such coupling protocols have survived out of many possible protocols remain to be revealed. To address this question, we investigated two distinct coupling protocols: activator-coupled oscillators (ACO) and repressor-coupled oscillators (RCO). We focused on the two coupling parameters: coupling dissociation constant and coupling time delay. Interestingly, the ACO was able to produce anti-phase or morning-evening cycles, whereas the RCO produced in-phase ones. Deterministic and stochastic analyses demonstrated that the anti-phase ACO provided greater fluctuations in amplitude not only with respect to changes in coupling parameters but also to random parameter perturbations than the in-phase RCO. Moreover, the ACO deteriorated the entrainability to the day-night master clock, whereas the RCO produced high entrainability. Considering that the real, interlocked feedback loops have evolved as the ACO, instead of the RCO, we first proposed a hypothesis that the morning-evening or anti-phase cycle is more essential for Drosophila than achieving the robustness and entrainability.九州工業大学博士学位論文 学位記番号:情工博甲第352号 学位授与年月日:令和2年9月25日1 BACKGROUND|2 THE DYNAMICS MODELS OF CIRCADIAN RHYTHMS|3 MODELING THE INTERLOCKED NEGATIVE FEEDBACK LOOPS|4 ROBUSTNESS OF THE INTERLOCKED CIRCADIAN OSCILLATORS|5 ENTRAINABILITY OF THE COUPLED OSCILLATORS|6 CONCLUSIONS AND FUTURE WORK九州工業大学令和2年

SATISFACTION OF RETAIL INVESTORS ON THE STRUCTURAL EFFICIENCY OF THE MARKET: EVIDENCE FROM A DEVELOPING COUNTRY CONTEXT

ABSTRAC

A Kano Model Based Linguistic Application for Customer Needs Analysis

Linguistic is the systematic study of language. Now quality doesn’t always mean the “tangible attribute” of a product or service. It may also be linguistic. Thus, linguistic has applied for product design through capturing the voice of Customers. Capturing of the voice of customers has been done in different way, like Quality Function Deployment (QFD), Kansei Engineering and Kano Model regarding product design. Kano Model has two dimensional linguistic approaches, which is more voice capturing capacity than other methods. Reverse attribute study is important for more reliable product design for next actions than other attributes of Kano model i.e. attractive, must‐be, one‐dimensional and indifferent. Thus, this paper is exclusively study for reverse attribute. For this purpose, a reverse attribute based linguistic approach, which is run in the computer system for product design regarding Kano model aspect using threshold numbers of real consumers opinions converted into probability through fuzzy concept as an input of Monte Carlo Simulation system determining virtual customers is described in this paper. 

A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things

The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE

A technique for parallel share-frequent sensor pattern mining from wireless sensor networks

WSNs generate huge amount of data in the form of streams and mining useful knowledge from these streams is a challenging task. Existing works generate sensor association rules using occurrence frequency of patterns with binary frequency (either absent or present) or support of a pattern as a criterion. However, considering the binary frequency or support of a pattern may not be a sufficient indicator for finding meaningful patterns from WSN data because it only reflects the number of epochs in the sensor data which contain that pattern. The share measure of sensorsets could discover useful knowledge about numerical values associated with sensor in a sensor database. Therefore, in this paper, we propose a new type of behavioral pattern called share-frequent sensor patterns by considering the non-binary frequency values of sensors in epochs. To discover share-frequent sensor patterns from sensor dataset, we propose a novel parallel technique. In this technique, we develop a novel tree structure, called parallel share-frequent sensor pattern tree (PShrFSP-tree) that is constructed at each local node independently, by capturing the database contents to generate the candidate patterns using a pattern growth technique with a single scan and then merges the locally generated candidate patterns at the final stage to generate global share-frequent sensor patterns. Comprehensive experimental results show that our proposed model is very efficient for mining share-frequent patterns from WSN data in terms of time and scalability

Cyberattacks detection in iot-based smart city applications using machine learning techniques

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain. © 2020 by the authors. Licensee MDPI, Basel, Switzerland