International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1227
Comparative Analysis of Data Mining Techniques for Predicting the Yield of Agricultural Crops
Статья научная
Predicting crop yields is one of the more difficult tasks in the agriculture sector. A fascinating area of research to estimate agricultural productivity has emerged from recent advancements in information technology for agriculture. Crop yield prediction is a technique for estimating crop production based on a variety of factors, including weather conditions and parameters such as temperature, rainfall, fertilizer, and pesticide use. In the world of agriculture, Data mining techniques are extremely popular. In order to predict the crop production for the following year, data mining techniques are employed and evaluated in the agricultural sector. In this paper, we carried out the comparison between Naive Bayes, K-nearest neighbor, Decision Tree, Random Forest, and K-Means clustering algorithms to predict crop yield in order to determine which method is most effective at doing so. The results show which algorithm is better suitable for this particular purpose by comparing these data mining algorithms for agricultural crop production and determining which algorithm is more successful for this outcome.
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Comparative Analysis of Data Mining Techniques to Predict Cardiovascular Disease
Статья научная
Cardiovascular disease is the leading cause of death. In recent days, most people are living with cardiovascular disease because of their unhealthy lifestyle and the most alarming issue is the majority of them do not get any symptoms in the early stage. This is why this disease is becoming more deadly. However, medical science has a large amount of data regarding cardiovascular disease, so this data can be used to apply data mining techniques to predict cardiovascular disease at the early stage to reduce its deadly effect. Here, five data mining classification techniques, such as: Naïve Bayes, K-Nearest Neighbors, Support Vector Machine, Random Forest and Decision Tree were implemented in the WEKA tool to get the best accuracy rate and a dataset of 12 attributes with more than 300 instances was used to apply all the data mining techniques to get the best accuracy rate. After doing this research people who are at the early stage of cardiovascular disease or probably going to be a victim can be identified more accurately.
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Статья научная
The intricate realm of time series prediction using stock market datasets from the NSE India is delved into by this research. The supremacy of LSTM architecture for forecasting in time series is initially affirmed, only for a paradigm shift to be encountered when exploring various LSTM variants across distinct sectors on the NSE (National Stock Exchange) of India. Prices of various stocks in five different sectors have been predicted using multiple LSTM model variants. Contrary to the assumption that a specific variant would excel in a particular sector, the Gated Recurrent Unit (GRU) emerged as the top performer, prompting a closer examination of its limitations and subsequent enhancement using technical indicators. The ultimate objective is to unveil the most effective model for predicting stock prices in the dynamic landscape of NSE India.
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Comparative Analysis of Three Improved Deep Learning Architectures for Music Genre Classification
Статья научная
Among the many music information retrieval (MIR) tasks, music genre classification is noteworthy. The categorization of music into different groups that came to existence through a complex interplay of cultures, musicians, and various market forces to characterize similarities between compositions and organize collections is known as a music genre. The past researchers extracted various hand-crafted features and developed classifiers based on them. But the major drawback of this approach was the requirement of field expertise. However, in recent times researchers, because of the remarkable classification accuracy of deep learning models, have used similar models for MIR tasks. Convolutional Neural Net- work (CNN), Recurrent Neural Network (RNN), and the hybrid model, Convolutional - Recurrent Neural Network (CRNN), are such prominently used deep learning models for music genre classification along with other MIR tasks and various architectures of these models have achieved state-of-the-art results. In this study, we review and discuss three such architectures of deep learning models, already used for music genre classification of music tracks of length of 29-30 seconds. In particular, we analyze improved CNN, RNN, and CRNN architectures named Bottom-up Broadcast Neural Network (BBNN) [1], Independent Recurrent Neural Network (IndRNN) [2] and CRNN in Time and Frequency dimensions (CRNN- TF) [3] respectively, almost all of the architectures achieved the highest classification accuracy among the variants of their base deep learning model. Hence, this study holds a comparative analysis of the three most impressive architectural variants of the main deep learning models that are prominently used to classify music genre and presents the three architecture, hence the models (CNN, RNN, and CRNN) in one study. We also propose two ways that can improve the performances of the RNN (IndRNN) and CRNN (CRNN-TF) architectures.
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Comparative Evaluation of Mobile Forensic Tools
Статья научная
Mobile technology, over the years, has improved tremendously in sophistication and functionality. Today, there are mobile phones, known as smartphones, that can perform virtually most functions associated with personal computers. This has translated to increase in the adoption of mobile technology. Consequently, there has been an increase in the number of attacks against and with the aid of this technology. Mobile phones will often contain data that are needed as evidence in a court of law. And, therefore, the need to be able to acquire and present this data in an admissible form cannot be overemphasized. This requires the right forensic tools. This is the focus of this study. We evaluated the ability of four forensic tools to extract data, with emphasis on deleted data, from Android phones. Our results show that AccessData FTK Imager and EnCase performed better than MOBILedit Forensic and Oxygen Forensic Suite at acquiring deleted data. The conclusion is that, finding a forensic tool or toolkit that is virtually applicable across all mobile device platforms and operating systems is currently infeasible.
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Статья научная
This research focuses on the basic concepts of continuum robot manipulator and control methodology. OCTARM Continuum robot manipulator is a 6 DOF serial robot manipulator. From the control point of view, robot manipulator divides into two main parts i.e. kinematics and dynamic parts. The dynamic parameters of this system are highly nonlinear. To control of this system nonlinear control methodology (computed torque controller and sliding mode controller) is introduced. Computed torque controller (CTC) is an influential nonlinear controller to certain systems which it is based on feedback linearization and computes the required arm torques using the nonlinear feedback control law. When all dynamic and physical parameters are known computed torque controller works superbly; practically a large amount of systems have uncertainties and sliding mode controller reduce this kind of limitation. Sliding mode controller (SMC) is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators, because this controller is a robust and stable. Comparative study between computed torque controller and sliding mode controller is introduced in this research.
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Comparative Study: Performance of MVC Frameworks on RDBMS
Статья научная
The regular utilization of web-based applications is crucial in our everyday life. The Model View Controller (MVC) architecture serves as a structured programming design that developers utilize to create user interfaces. This pattern is commonly applied by application software developers to construct web-based applications. The use of a MVC framework of PHP Scripting language is often essential for application software development. There is a significant argument regarding the most suitable PHP MVC such as Codeigniter & Laravel and Phalcon frameworks since not all frameworks cater to everyone's needs. It's a fact that not all MVC frameworks are created equal and different frameworks can be combined for specific scenarios. Selecting the appropriate MVC framework can pose a challenge at times. In this context, our paper focuses on conducting a comparative analysis of different PHP frameworks. The widely used PHP MVC frameworks are picked to compare the performance on basic Operation of Relational databases and different type of Application software to calculate execution time. In this experiment a large (Big Data) dataset was used. The Mean values of insert operation in MySQL database of Codeigniter, Laravel, Phalcon were 149.64, 149.99, 145.48 and PostgreSQL database`s 48.259, 49.39, 45.87 respectively. The Mean values of Update operation in MySQL database of Codeigniter, Laravel, Phalcon were 149.64, 158.39, 207.82 and PostgreSQL database`s 48.24, 49.39, 46.64 respectively. The Mean values of Select operation in MySQL database of Codeigniter, Laravel, Phalcon were 1.60, 3.23, 0.98 and PostgreSQL database`s 1.95, 4.57, 2.36 respectively. The Mean values of Delete operation in MySQL database of Codeigniter, Laravel, Phalcon were 150.27, 156.99, 149.63 and PostgreSQL database`s 42.95, 48.25, 42.07 respectively. The findings from our experiment can be advantageous for web application developers to choose proper MVC frameworks with their integrated development environment (IDE). This result will be helpful for small, medium & large-scale organization in choosing the appropriate PHP Framework.
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Comparative Weka Analysis of Clustering Algorithm's
Статья научная
Data mining is a procedure of mining or obtaining a pertinent volume of data or information making the data available for understanding and processing. Data analysis is a common method across various areas like computer science, biology, telecommunication industry and retail industry. Data mining encompass various algorithms viz. association rule mining, classification algorithm, clustering algorithms. This survey concentrates on clustering algorithms and their comparison using WEKA tool. Clustering is the splitting of a large dataset into clusters or groups following two criteria ie. High intra-class similarity and low inter-class similarity. Every cluster or group must contain one data item and every data item must be in one cluster. Clustering is an unsupervised technique that is fairly applicable on large datasets with a large number of attributes. It is a data modelling technique that gives a concise view of data. This survey tends to explain all the clustering algorithms and their variant analysis using WEKA tool on various datasets.
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Статья научная
Authors here tried to use the WEKA tool to evaluate the performance of various classifiers on a dataset to come out with the optimum classifier, for a particular application. A Classifier is an important part of any machine learning application. It is required to classify various classes and get to know whether the predicted class lies in the true class. There are various performance analysis measures to judge the efficiency of a classifier and there are many tools which provide oodles of classifiers. In the present investigation, Bayes Net, Naive Bayes and their combination have been implemented using WEKA. It has been concluded that the combination of Bayes Net and Naive Bayes provides the maximum classification efficiency out of these three classifiers. Such a hybridization approach will always motivate for combining different classifiers to get the best results.
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Comparative analysis of multiple sequence alignment tools
Статья научная
The perfect alignment between three or more sequences of Protein, RNA or DNA is a very difficult task in bioinformatics. There are many techniques for alignment multiple sequences. Many techniques maximize speed and do not concern with the accuracy of the resulting alignment. Likewise, many techniques maximize accuracy and do not concern with the speed. Reducing memory and execution time requirements and increasing the accuracy of multiple sequence alignment on large-scale datasets are the vital goal of any technique. The paper introduces the comparative analysis of the most well-known programs (CLUSTAL-OMEGA, MAFFT, BROBCONS, KALIGN, RETALIGN, and MUSCLE). For programs’ testing and evaluating, benchmark protein datasets are used. Both the execution time and alignment quality are two important metrics. The obtained results show that no single MSA tool can always achieve the best alignment for all datasets.
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Comparative descriptive analysis of breast cancer tissues using k-means and self-organizing map
Статья научная
Data mining is a descriptive and predictive data analytical technique that discovers meaningful and useful knowledge from dataset. Clustering is one of the descriptive analytic techniques of data mining that uses latent statistical information that exists among dataset to group them into meaningful and or useful groups. In clinical decision making, information from medical tests coupled with patients’ medical history is used to make recommendations, and predictions. However, these voluminous medical datasets analysis is always dependent of individual analyzer that might have in one way or the other introduced human error. In other to solve this problem, many automated analyses have been proposed by researchers using various machine learning techniques and various forms of dataset. In this paper, dataset from electrical impedance imaging of breast tissues are clustered using two unsupervised algorithms (k-means and self-organizing map). Result of the performances of these machine learning algorithms as implemented with R i368 version 3.4.2 shows a slight outperformance of K-means in terms of classification accuracy over self-organizing map for the considered dataset.
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Статья научная
Since various pseudo-random algorithms and sequences are used for cryptography of data or as initial values for starting a secure communication, how these algorithms are analyzed and selected is very important. In fact, given the growingly extensive types of pseudo-random sequences and block and stream cipher algorithms, selection of an appropriate algorithm needs an accurate and thorough investigation. Also, in order to generate a pseudo-random sequence and generalize it to a cryptographer algorithm, a comprehensive and regular framework is needed, so that we are enabled to evaluate the presented algorithm as quick as possible. The purpose of this study is to use a number of pseudo-random number generators as well as popular cryptography algorithms, analyze them in a standard framework and observe the results obtained in each stage. The investigations are like a match between different algorithms, such that in each stage, weak algorithms are eliminated using a standard method and successful algorithms enter the next stage so that the best algorithms are chosen in the final stage. The main purpose of this paper is to certify the approved algorithm.
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Comparing the Performances of Ensemble-classifiers to Detect Eye State
Статья научная
Brain signals required for the brain-computer interface are obtained through the electroencephalography (EEG) method. EEG data is used in the analysis of many problems such as epileptic seizure detection, bipolar mood disorder, attention deficit, and detection of the sleep state of the vehicle driver. It is very important to determine whether the eye is open or closed, which is a substantial organ for the determination of the cognitive state of the person. The aim of this paper is to present a stable and successful model for detecting the eye states that are opened or closed. In this context, the performances of several ensemble classifiers were examined on the Emotiv EEG Neuroheadset dataset, which has 14 features excluding the target variable, 14980 records that have 8225 eye states opened and 6755 eye states closed. In the experiments, firstly the min-max normalization process was applied to the dataset, and then the classification performances of these classifiers were evaluated via a 5-fold cross-validation technique. The performance of each model was measured using accuracy, sensitivity, and specificity metrics. The obtained results show that the Random Forest algorithm is an acceptable level with 92.61% value of accuracy, 94.31% value of sensitivity and 91.36% value of specificity for detecting the eye state.
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Comparison of Time Concept Modeling for Querying Temporal Information in OWL and RDF
Статья научная
Ontology is an important factor in the integration of heterogeneous semantic information. Description logic, as a formal language for expressing ontologies, does not include the necessary features to create a temporal dimension in the relationships among concepts. It is critical to introduce time concepts to model temporal data and relate them to other non-temporal data recorded in ontology. Current query languages in the semantic web are not able to respond to temporal questions; thus, another important issue is to have the appropriate methods for answering temporal questions. In this paper, temporal modeling methods in OWL and RDF are assessed and the temporal query languages for expressing queries in the semantic web are categorized and compared.
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Comparison of on Demand Routing Protocols
Статья научная
A routing protocol is used to facilitate communication in ad hoc network. The primary goal of such a routing protocol is to provide an efficient and reliable path between a pair of nodes. The routing protocols for ad hoc network can be categorized into three categories: table driven, on demand and hybrid routing. The table driven and hybrid routing strategies require periodic exchange of hello messages between nodes of the ad hoc network and thus have high processing and bandwidth requirements. On the other hand on demand routing strategy creates routes when required and hence is very much suitable for ad hoc network. This paper therefore examines the performance of three on demand routing protocols at application layer using QualNet-5.01 simulator.
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Статья научная
Informal education will be successful as an alternative for the community because not all people are able to receive formal education. This study uses a qualitative method with a systematic literature review (SLR) technique to look for learning community components in informal education to support learning in the culinary community in the new normal era of Covid-19. The author collects, studies, and analyzes reference sources according to the specified keywords. Found 53 papers from 2002 to 2021 with background authors from academia, industry, and the public sector with reference sources from journals, conferences, white papers, and research reports. Systematic literature review results obtained 6 components of learning community in informal education, namely content, forum, method, technology, figure/layout, and human/social resources. The six components as a reference and the author's first step in the next research through searching for the characteristics of the learning community in the culinary field, then making a learning model of the culinary community. Because of the importance of the learning community component in informal education to help community members share knowledge, solve problems, share common goals and interests among community members.
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Comprehensive Evaluation of Energy Transportation Corridor in China
Статья научная
In order to offer reference for the energy transportation channels in future, we evaluated the existing system with the information entropy theory. By calculating the base value for the scale of construction and the equilibrium degree with the data of annual investment in the fixed assets from 1986 to 2010, we measured the scale and its rationality, and analyzed the relation between transportation channels and the information entropy combined with the maximum entropy methods. Empirical results show that the partial information entropy Si still have certain rising space. We can improve the rationality of the energy transportation channels by improving the partial information entropy Si. And we should not let the rail transportation channels have excessive investment growth, and then secondly transfer some of the highway investment to the transportation channels of waterway or pipeline while finally advance the construction step of the pipeline transportation channels. This conclusion provides a quantitative basis for measuring the rationality of the construction scale of the energy transportation channels. The quantitative evaluation of the construction scale of energy transportation channels is a important supplement to the traditional research and provide evidence for further development. And we can obtain more suggestions on the development strategy emphasis in future.
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Статья научная
A Mobile Adhoc Network is a multi-hop self-configuring network without any fixed infrastructure. Due to mobility of nodes, dynamic topology and highly dynamic environment, designing and implementing stable routing in Mobile Ad-hoc Networking is a major challenge and a critical issue. This paper analyses the performance analysis of on demand routing protocol, Dynamic Source Routing (DSR), Adhoc on Demand Distance Vector Routing (AODV) and table driven protocol, Destination-Sequenced Distance Vectoring (DSDV) using a network simulator NS2. Different types of test scenario have designed with fixed number of nodes but varying mobility. Different performance metric values like, throughput, delay, normalized network load, end to end delay, dropped packets, packets delivery ratio have been observed. The experimental results have been analysed and recommendation based on the obtained results has been proposed about the significance of each protocol in different scenarios and situations. The simulation results show that both protocols are good in performance in their own categories. We believe that findings of this paper will help the researcher to find the best protocol under predefined condition with varied mobility. We believe that this research will help the researcher to identify and further investigate any particular metrics value of AODV, DSR and DSDV.
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Статья научная
The paper is presenting theoretical analytical model and computer simulation of electromagnetic transient process in a transformer. Transformer parameters in a selected transitional process have been analyzed. Theoretical model refers to an energetic transformer with concentrated parameters with consideration of parameters of mutual inductance M. Simulation was performed on a personal computer using the software program MATLAB SIMULINK. The computer simulation confirmed the possibility of transitional process analysis in transformer’s windings with concentrated parameters.
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Confidence Analysis of a Solo Sign-On Device for Distributed Computer Networks
Статья научная
Solo sign-on (SSO) is a new authentication mechanism that enables a legal user with a single credential to be authenticated by multiple service providers in a distributed computer network. Recently, a SSO scheme proposed and claimed its security by providing well organized security arguments. But their scheme is actually insecure as it fails to meet credential privacy and soundness of authentication. Specifically, we present two impersonation attacks i.e., credential recovering attack and impersonation attack without credentials. So we propose a more authentication scheme that overcomes these attacks and flaws by make use of efficient verifiable encryption of RSA signatures. We promote the formal study of the soundness of authentication as one open problem.
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