Search results for: Artificial intelligent

Authors: F. F. Howard, I. Yakubu, C. B. Boye, J. S. Y. Kuma

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Natural and human activities, such as mining operations, expose the natural soil to adverse environmental conditions, leading to contamination of soil, groundwater, and surface water, which has negative effects on humans, flora, and fauna. Bare or partly exposed soil is most liable to fluvial erosion. This paper enumerates various methods used to identify, map, and model fluvial erosion in a mining environment. Classical, Artificial Intelligence (AI), and GIS methods have been reviewed. One of the many classical methods used to estimate river erosion is the Revised Universal Soil Loss Equation (RUSLE) model. The RUSLE model is easy to use. Its reliance on empirical relationships that may not always be applicable to specific circumstances or locations is a flaw. Other classical models for estimating fluvial erosion are the Soil and Water Assessment Tool (SWAT) and the Universal Soil Loss Equation (USLE). These models offer a more complete understanding of the underlying physical processes and encompass a wider range of situations. Although more difficult to utilise, they depend on the availability and dependability of input data for correctness. AI can help deal with multivariate and complex difficulties and predict soil loss with higher accuracy than traditional methods, and also be used to build unique models for identifying degraded areas. AI techniques have become popular as an alternative predictor for degraded environments. However, this research proposed a hybrid of classical, AI, and GIS methods for efficient and effective modelling of fluvial erosion.

Keywords: Fluvial erosion, classical methods, Artificial Intelligence, Geographic Information System.

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Authors: Rajvir Kaur, Jeewani Anupama Ginige

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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: Artificial neural networks, breast cancer, cancer dataset, classifiers, cervical cancer, F-score, logistic regression, machine learning, precision, recall, support vector machine.

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Authors: Ali Reza Mehrabian, Caro Lucas

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In this paper a new control strategy based on Brain Emotional Learning (BEL) model has been introduced. A modified BEL model has been proposed to increase the degree of freedom, controlling capability, reliability and robustness, which can be implemented in real engineering systems. The performance of the proposed BEL controller has been illustrated by applying it on different nonlinear uncertain systems, showing very good adaptability and robustness, while maintaining stability.

Keywords: Learning control systems, emotional decision making, nonlinear systems, adaptive control.

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Authors: Hataitep Wongsuwarn, Djitt Laowattana

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This paper summaries basic principles and concepts of intelligent controls, implemented in humanoid robotics as well as recent algorithms being devised for advanced control of humanoid robots. Secondly, this paper presents a new approach neuro-fuzzy system. We have included some simulating results from our computational intelligence technique that will be applied to our humanoid robot. Subsequently, we determine a relationship between joint trajectories and located forces on robot-s foot through a proposed neuro-fuzzy technique.

Keywords: Biped Robot, Computational Intelligence, Static and Dynamic Walking, Gait Synthesis, Neuro-Fuzzy System.

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Authors: Selim Yannier, Asif Sabanovic, Ahmet Onat, Muhammet Bastan

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In this work, we suggested a new approach for the control of a mobile robot capable of being a building block of an intelligent agent. This approach includes obstacle avoidance and goal tracking implemented as two different sliding mode controllers. A geometry based behavior arbitration is proposed for fusing the two outputs. Proposed structure is tested on simulations and real robot. Results have confirmed the high performance of the method.

Keywords: Autonomous Mobile Robot, Behavior Based Control, Fast Local Obstacle Avoidance, Sliding Mode Control.

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Authors: Kevser Simsek, Rahime Gunay

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In this paper, we focused primarily on Istanbul data that is gathered by using intelligent transportation systems (ITS), and considered the developments in traffic information delivery and future applications that are being planned for implementation. Since traffic congestion is increasing and travel times are becoming less consistent and less predictable, traffic information delivery has become a critical issue. Considering the fuel consumption and wasted time in traffic, advanced traffic information systems are becoming increasingly valuable which enables travelers to plan their trips more accurately and easily.

Keywords: Data Fusion, Istanbul, ITS, Real Time Information, Traffic Information, Travel Time, Urban Mobility

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Authors: F. Rahnamay Roodposhti, M. Falah Shams, H. Kordlouie

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The aim of the article is extending and developing econometrics and network structure based methods which are able to distinguish price manipulation in Tehran stock exchange. The principal goal of the present study is to offer model for approximating price manipulation in Tehran stock exchange. In order to do so by applying separation method a sample consisting of 397 companies accepted at Tehran stock exchange were selected and information related to their price and volume of trades during years 2001 until 2009 were collected and then through performing runs test, skewness test and duration correlative test the selected companies were divided into 2 sets of manipulated and non manipulated companies. In the next stage by investigating cumulative return process and volume of trades in manipulated companies, the date of starting price manipulation was specified and in this way the logit model, artificial neural network, multiple discriminant analysis and by using information related to size of company, clarity of information, ratio of P/E and liquidity of stock one year prior price manipulation; a model for forecasting price manipulation of stocks of companies present in Tehran stock exchange were designed. At the end the power of forecasting models were studied by using data of test set. Whereas the power of forecasting logit model for test set was 92.1%, for artificial neural network was 94.1% and multi audit analysis model was 90.2%; therefore all of the 3 aforesaid models has high power to forecast price manipulation and there is no considerable difference among forecasting power of these 3 models.

Keywords: Price Manipulation, Liquidity, Size of Company, Floating Stock, Information Clarity

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Authors: Omid Mirmotahari, Yngvar Berg, Mats Hovin

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The problem discussed in this paper involves packing fresh fish fileet of the northern Cod into a standard square container. The fish is first cleaned and split and then collected on a belt ready to be stacked in a container. The aim of our work is to pack the fish into the container with constraints on the amount of overlap allowed for the fileets. The current focus is to design a packing cell that can be real-time and of practical use, while finding the optimal solution to the degree of overlap and minimise the unused space of the container.

Keywords: Facilities Planning and Management, Intelligent Systems, Manufacturing Systems, Operations Research, Production Planning and Control.

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Authors: Arash Taheri, Meysam Mohammadi-Amin, Seyed Hossein Moosavy

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Medical applications are among the most impactful areas of microrobotics. The ultimate goal of medical microrobots is to reach currently inaccessible areas of the human body and carry out a host of complex operations such as minimally invasive surgery (MIS), highly localized drug delivery, and screening for diseases at their very early stages. Miniature, safe and efficient propulsion systems hold the key to maturing this technology but they pose significant challenges. A new type of propulsion developed recently, uses multi-flagella architecture inspired by the motility mechanism of prokaryotic microorganisms. There is a lack of efficient methods for designing this type of propulsion system. The goal of this paper is to overcome the lack and this way, a numerical strategy is proposed to design multi-flagella propulsion systems. The strategy is based on the implementation of the regularized stokeslet and rotlet theory, RFT theory and new approach of “local corrected velocity". The effects of shape parameters and angular velocities of each flagellum on overall flow field and on the robot net forces and moments are considered. Then a multi-layer perceptron artificial neural network is designed and employed to adjust the angular velocities of the motors for propulsion control. The proposed method applied successfully on a sample configuration and useful demonstrative results is obtained.

Keywords: Artificial Neural Network, Biomimetic Microrobots, Flagellar Propulsion, Swimming Robots.

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Authors: Anjan Kumar Kakati, M. Chandrasekaran, Amitava Mandal, Amit Kumar Singh

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End milling process is one of the common metal cutting operations used for machining parts in manufacturing industry. It is usually performed at the final stage in manufacturing a product and surface roughness of the produced job plays an important role. In general, the surface roughness affects wear resistance, ductility, tensile, fatigue strength, etc., for machined parts and cannot be neglected in design. In the present work an experimental investigation of end milling of aluminium alloy with carbide tool is carried out and the effect of different cutting parameters on the response are studied with three-dimensional surface plots. An artificial neural network (ANN) is used to establish the relationship between the surface roughness and the input cutting parameters (i.e., spindle speed, feed, and depth of cut). The Matlab ANN toolbox works on feed forward back propagation algorithm is used for modeling purpose. 3-12-1 network structure having minimum average prediction error found as best network architecture for predicting surface roughness value. The network predicts surface roughness for unseen data and found that the result/prediction is better. For desired surface finish of the component to be produced there are many different combination of cutting parameters are available. The optimum cutting parameter for obtaining desired surface finish, to maximize tool life is predicted. The methodology is demonstrated, number of problems are solved and algorithm is coded in Matlab®.

Keywords: End milling, Surface roughness, Neural networks.

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Authors: Anuja Vyas, Aikel Indurkhya, Hari Krishna Garg

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Nearly 25 years have passed since the landmark publication of the Human Genome Project, yet scientists have only begun to scratch the surface of its potential benefits. To bridge this gap, a personalized genomic application has been envisioned as a transformative tool accessible to people worldwide. This innovative solution proposes an integrated framework combining blockchain technology, genome-specific applications, and data compression techniques, ensuring operations to be swift, secure, transparent, and space-efficient. The software harnesses advanced Artificial Intelligence and Machine Learning methodologies, such as neural networks, evaluation matrices, fuzzy logic, and expert systems, to analyze individual genomic data. It generates personalized reports by comparing a user's genome with a reference genome, highlighting significant differences. Blockchain technology, with its inherent security, encryption, and immutability features, is leveraged for robust data transport and storage. In addition, a 'Data Abbreviation' technique ensures that genetic data and reports occupy minimal space. This integrated approach promises to be a significant leap forward, potentially transforming human health and well-being on a global scale.

Keywords: Artificial intelligence in genomics, blockchain technology, data abbreviation, data compression, data security in genomics, data storage, expert systems, fuzzy logic, genome applications, genomic data analysis, human genome project, neural networks, personalized genomics.

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Authors: S. Areerachakul, N. Ployong, S. Na Songkla

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This research’s objective is to select the model with most accurate value by using Neural Network Technique as a way to filter potential students who enroll in IT course by Electronic learning at Suan Suanadha Rajabhat University. It is designed to help students selecting the appropriate courses by themselves. The result showed that the most accurate model was 100 Folds Cross-validation which had 73.58% points of accuracy.

Keywords: Artificial neural network, classification, students.

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Authors: Almutasim Billa A. Alanazi, Hal S. Tharp

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Electrical energy demands have increased due to population growth and the variety of new electrical load technologies. This increase demand has nearly doubled during peak hours. Consequently, that necessitates the construction of new power plant infrastructures, which is a costly approach due to the expense of construction building, future preservation like maintenance, and environmental impact. As an alternative approach, most electrical utilities increase the price of electrical usage during peak hours, encouraging consumers to use less electricity during peak periods under Time-Of-Use programs, which may not be universally suitable for all consumers. Furthermore, in some areas, the excessive demand and the lack of supply cause an electrical outage, posing considerable stress and challenges to electrical utilities and consumers. However, control systems, artificial intelligence (AI), and renewable energy (RE), when effectively integrated, provide new solutions to mitigate excessive demand during peak hours. This paper presents a power model that reduces the reliance on the power grid during peak hours by utilizing a hybrid solar system connected to a residential house with a power management controller, that prioritizes the power drives between Photovoltaic (PV) production, battery backup, and the utility electrical grid. As a result, dependence on utility grid was from 3% to 18% during peak hours, improving energy stability safely and efficiently for electrical utilities, consumers, and communities, providing a viable alternative to conventional approaches such as Time-Of-Use programs.

Keywords: Artificial intelligence, AI, control system, photovoltaic, PV, renewable energy.

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Authors: Chi-Fang Huang, Yun-Shiow Chen, Yun-Kung Chung

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In the past decade, artificial neural networks (ANNs) have been regarded as an instrument for problem-solving and decision-making; indeed, they have already done with a substantial efficiency and effectiveness improvement in industries and businesses. In this paper, the Back-Propagation neural Networks (BPNs) will be modulated to demonstrate the performance of the collaborative forecasting (CF) function of a Collaborative Planning, Forecasting and Replenishment (CPFR®) system. CPFR functions the balance between the sufficient product supply and the necessary customer demand in a Supply and Demand Chain (SDC). Several classical standard BPN will be grouped, collaborated and exploited for the easy implementation of the proposed modular ANN framework based on the topology of a SDC. Each individual BPN is applied as a modular tool to perform the task of forecasting SKUs (Stock-Keeping Units) levels that are managed and supervised at a POS (point of sale), a wholesaler, and a manufacturer in an SDC. The proposed modular BPN-based CF system will be exemplified and experimentally verified using lots of datasets of the simulated SDC. The experimental results showed that a complex CF problem can be divided into a group of simpler sub-problems based on the single independent trading partners distributed over SDC, and its SKU forecasting accuracy was satisfied when the system forecasted values compared to the original simulated SDC data. The primary task of implementing an autonomous CF involves the study of supervised ANN learning methodology which aims at making “knowledgeable" decision for the best SKU sales plan and stocks management.

Keywords: CPFR, artificial neural networks, global logistics, supply and demand chain.

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Authors: H. Dornhackl, K. Kadletz, R. Luh, P. Tavolato

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In this paper, we describe the use of formal methods to model malware behaviour. The modelling of harmful behaviour rests upon syntactic structures that represent malicious procedures inside malware. The malicious activities are modelled by a formal grammar, where API calls’ components are the terminals and the set of API calls used in combination to achieve a goal are designated non-terminals. The combination of different non-terminals in various ways and tiers make up the attack vectors that are used by harmful software. Based on these syntactic structures a parser can be generated which takes execution traces as input for pattern recognition.

Keywords: Malware behaviour, modelling, parsing, search, pattern matching.

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Authors: S. Sutor, R. Röhr, G. Pujolle, R. Reda

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This paper presents a highly efficient algorithm for detecting and tracking humans and objects in video surveillance sequences. Mean shift clustering is applied on backgrounddifferenced image sequences. For efficiency, all calculations are performed on integral images. Novel corresponding exponential integral kernels are introduced to allow the application of nonuniform kernels for clustering, which dramatically increases robustness without giving up the efficiency of the integral data structures. Experimental results demonstrating the power of this approach are presented.

Keywords: Clustering, Integral Images, Kernels, Person Detection, Person Tracking, Intelligent Video Surveillance.

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Authors: Amin Milani Fard, Mohsen Kahani, Reza Ghaemi, Hamid Tabatabaee

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In this paper we propose a multi-agent architecture for web information retrieval using fuzzy logic based result fusion mechanism. The model is designed in JADE framework and takes advantage of JXTA agent communication method to allow agent communication through firewalls and network address translators. This approach enables developers to build and deploy P2P applications through a unified medium to manage agent-based document retrieval from multiple sources.

Keywords: Information retrieval systems, list fusion methods, document score, multi-agent systems.

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Authors: Maryam Humayun, Zafar I. Malik, Shaukat Ali

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Computer animation is a widely adopted technique used to specify the movement of various objects on screen. The key issue of this technique is the specification of motion. Motion Control Methods are such methods which are used to specify the actions of objects. This paper discusses the various types of motion control methods with special focus on behavioral animation. A behavioral model is also proposed which takes into account the emotions and perceptions of an actor which in turn generate its behavior. This model makes use of an expert system to generate tasks for the actors which specify the actions to be performed in the virtual environment.

Keywords: Behavioral animation, emotion, expert system, perception.

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Authors: Ho-Yeon Kim, Dong-Min Kang, Jun-Ho Lee, Tai-Myoung Chung

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Recently, a vehicular ad-hoc networks(VANETs) for Intelligent Transport System(ITS) have become able safety and convenience services surpassing the simple services such as an electronic toll collection system. To provide the proper services, VANET needs infrastructure over the country infrastructure. Thus, we have to spend a huge sum of human resources. In this reason, several studies have been made on the usage of cellular networks instead of new protocols this study is to assess a performance evaluation of the cellular network for VANET. In this paper, the result of a for the suitability of cellular networks for VANET experiment, The LTE(Long Term Evolution) of cellular networks found to be most suitable among the others cellular networks

Keywords: Vehicle communication, VANET, Cellular network

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Authors: A. Faro, D. Giordano, C. Spampinato

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Traffic management in an urban area is highly facilitated by the knowledge of the traffic conditions in every street or highway involved in the vehicular mobility system. Aim of the paper is to propose a neuro-fuzzy approach able to compute the main parameters of a traffic system, i.e., car density, velocity and flow, by using the images collected by the web-cams located at the crossroads of the traffic network. The performances of this approach encourage its application when the traffic system is far from the saturation. A fuzzy model is also outlined to evaluate when it is suitable to use more accurate, even if more time consuming, algorithms for measuring traffic conditions near to saturation.

Keywords: Neuro-fuzzy networks, computer vision, Fuzzy systems, intelligent transportation system.

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Authors: N. Soussi, M. Bahaj

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The enormous amount of information stored on the web increases from one day to the next, exposing the web currently faced with the inevitable difficulties of research pertinent information that users really want. The problem today is not limited to expanding the size of the information highways, but to design a system for intelligent search. The vast majority of this information is stored in relational databases, which in turn represent a backend for managing RDF data of the semantic web. This problem has motivated us to write this paper in order to establish an effective approach to support semantic transformation algorithm for SPARQL queries to SQL queries, more precisely SPARQL SELECT queries; by adopting this method, the relational database can be questioned easily with SPARQL queries maintaining the same performance.

Keywords: RDF, Semantic Web, SPARQL, SPARQL Query Transformation, SQL.

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Authors: Xiaolei Hu, Enrico Ferrera, Riccardo Tomasi, Claudio Pastrone

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Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is easy yet effective to apply Load Forecasting at larger geographic scale, i.e. Smart Micro Grids, wherein the lower available grid flexibility makes accurate prediction more critical in Demand Response applications. This paper analyses the application of short-term load forecasting in a concrete scenario, proposed within the EU-funded GreenCom project, which collect load data from single loads and households belonging to a Smart Micro Grid. Three short-term load forecasting techniques, i.e. linear regression, artificial neural networks, and radial basis function network, are considered, compared, and evaluated through absolute forecast errors and training time. The influence of weather conditions in Load Forecasting is also evaluated. A new definition of Gain is introduced in this paper, which innovatively serves as an indicator of short-term prediction capabilities of time spam consistency. Two models, 24- and 1-hour-ahead forecasting, are built to comprehensively compare these three techniques.

Keywords: Short-term load forecasting, smart micro grid, linear regression, artificial neural networks, radial basis function network, Gain.

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Authors: Ashly Joseph, Jithu Paulose

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Large language models (LLMs) have revolutionized natural language processing capabilities, enabling applications such as chatbots, dialogue agents, image, and video generators. Nevertheless, their trainings on extensive datasets comprising personal information poses notable privacy and safety hazards. This study examines methods for addressing these challenges, specifically focusing on approaches to enhance the security of LLM outputs, safeguard user privacy, and adhere to data protection rules. We explore several methods including post-processing detection algorithms, content filtering, reinforcement learning from human and AI inputs, and the difficulties in maintaining a balance between model safety and performance. The study also emphasizes the dangers of unintentional data leakage, privacy issues related to user prompts, and the possibility of data breaches. We highlight the significance of corporate data governance rules and optimal methods for engaging with chatbots. In addition, we analyze the development of data protection frameworks, evaluate the adherence of LLMs to General Data Protection Regulation (GDPR), and examine privacy legislation in academic and business policies. We demonstrate the difficulties and remedies involved in preserving data privacy and security in the age of sophisticated artificial intelligence by employing case studies and real-life instances. This article seeks to educate stakeholders on practical strategies for improving the security and privacy of LLMs, while also assuring their responsible and ethical implementation.

Keywords: Data privacy, large language models, artificial intelligence, machine learning, cybersecurity, general data protection regulation, data safety.

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Authors: Alshalaa A. Shleeg, Issmail M. Ellabib

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Breast cancer is a major health burden worldwide being a major cause of death amongst women. In this paper, Fuzzy Inference Systems (FIS) are developed for the evaluation of breast cancer risk using Mamdani-type and Sugeno-type models. The paper outlines the basic difference between Mamdani-type FIS and Sugeno-type FIS. The results demonstrated the performance comparison of the two systems and the advantages of using Sugeno- type over Mamdani-type.

Keywords: Breast cancer diagnosis, Fuzzy Inference System (FIS), Fuzzy Logic, fuzzy intelligent technique.

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Authors: Dipankaj G Medhi, Juri Dakua

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Recommender Systems act as personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with no emphasis on the trustworthiness of the user. RS depends on information provided by different users to gather its knowledge. We believe, if a large group of users provide wrong information it will not be possible for the RS to arrive in an accurate conclusion. The system described in this paper introduce the concept of Testing the knowledge of user to filter out these “bad users". This paper emphasizes on the mechanism used to provide robust and effective recommendation.

Keywords: Collaborative Filtering, Content Based Filtering, Intelligent Agent, Level of Interest, Recommendation System.

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Authors: Brian A. Smith, Ronald W. McClendon, Gerrit Hoogenboom

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The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. Previous work established that the Ward-style artificial neural network (ANN) is a suitable tool for developing such models. The current research focused on developing ANN models with reduced average prediction error by increasing the number of distinct observations used in training, adding additional input terms that describe the date of an observation, increasing the duration of prior weather data included in each observation, and reexamining the number of hidden nodes used in the network. Models were created to predict air temperature at hourly intervals from one to 12 hours ahead. Each ANN model, consisting of a network architecture and set of associated parameters, was evaluated by instantiating and training 30 networks and calculating the mean absolute error (MAE) of the resulting networks for some set of input patterns. The inclusion of seasonal input terms, up to 24 hours of prior weather information, and a larger number of processing nodes were some of the improvements that reduced average prediction error compared to previous research across all horizons. For example, the four-hour MAE of 1.40°C was 0.20°C, or 12.5%, less than the previous model. Prediction MAEs eight and 12 hours ahead improved by 0.17°C and 0.16°C, respectively, improvements of 7.4% and 5.9% over the existing model at these horizons. Networks instantiating the same model but with different initial random weights often led to different prediction errors. These results strongly suggest that ANN model developers should consider instantiating and training multiple networks with different initial weights to establish preferred model parameters.

Keywords: Decision support systems, frost protection, fruit, time-series prediction, weather modeling

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Authors: Gaurav Upadhyay, Nand Kishore, Prashant Ranjan, V. S. Tripathi, Shivesh Tripathi

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A frequency reconfigurable multiband antenna for intelligent transportation system (ITS) applications is proposed in this paper. A PIN-diode is used for reconfigurability. Centre frequencies are 1.38, 1.98, 2.89, 3.86, and 4.34 GHz in “ON” state of Diode and 1.56, 2.16, 2.88, 3.91 and 4.45 GHz in “OFF” state. Achieved maximum bandwidth is 18%. The maximum gain of the proposed antenna is 2.7 dBi in “ON” state and 3.95 dBi in “OFF” state of the diode. The antenna is simulated, fabricated, and tested in the lab. Measured and simulated results are in good confirmation.

Keywords: ITS, multiband antenna, PIN-diode, reconfigurable.

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Authors: Naiqin Feng, Yuhui Qiu, Yingshan Zhang, Fang Wang

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A logic model for analyzing complex systems- stability is very useful to many areas of sciences. In the real world, we are enlightened from some natural phenomena such as “biosphere", “food chain", “ecological balance" etc. By research and practice, and taking advantage of the orthogonality and symmetry defined by the theory of multilateral matrices, we put forward a logic analysis model of stability of complex systems with three relations, and prove it by means of mathematics. This logic model is usually successful in analyzing stability of a complex system. The structure of the logic model is not only clear and simple, but also can be easily used to research and solve many stability problems of complex systems. As an application, some examples are given.

Keywords: Complex system, logic model, relation, stability.

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Authors: Pingping Lin, Xudong Luo

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Natural language often conveys emotions of speakers. Therefore, sentiment analysis on what people say is prevalent in the field of natural language process and has great application value in many practical problems. Thus, to help people understand its application value, in this paper, we survey various applications of sentiment analysis, including the ones in online business and offline business as well as other types of its applications. In particular, we give some application examples in intelligent customer service systems in China. Besides, we compare the applications of sentiment analysis on Twitter, Weibo, Taobao and Facebook, and discuss some challenges. Finally, we point out the challenges faced in the applications of sentiment analysis and the work that is worth being studied in the future.

Keywords: Natural language processing, sentiment analysis, application, online comments.

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Authors: Richard Joseph, Nerey Mvungi

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An electric power system includes a generating, a transmission, a distribution, and consumers subsystems. An electrical power network in Tanzania keeps growing larger by the day and become more complex so that, most utilities have long wished for real-time monitoring and remote control of electrical power system elements such as substations, intelligent devices, power lines, capacitor banks, feeder switches, fault analyzers and other physical facilities. In this paper, the concept of automation of management of power systems from generation level to end user levels was determined by using Power System Simulator for Engineering (PSS/E) version 30.3.2.

Keywords: Automation, Distribution subsystem, Generating subsystem, PSS/E, TANESCO, Transmission subsystem.

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