International Journal of Informatics and Communication Technology (IJ-ICT)
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Memetic algorithm for short messaging service spam filter using text normalization and semantic approach
Get PDFToday’s popularity of the short messages services (SMS) has created a propitious environment for spamming to thrive. Spams are unsolicited advertising, adult-themed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter. However, SMS limitations of 160-charcaters and 140-bytes size as well as its being rippled with slangs, emoticons and abbreviations further inhibits effective training of models to aid accurate classification. The study proposes Genetic Algorithm Trained Bayesian Network solution that seeks to normalize noisy feats, expand text via use of lexicographic and semantic dictionaries that uses word sense disambiguation technique to train the underlying learning heuristics. And in turn, effectively help to classify SMS in spam and legitimate classes. Hybrid model comprises of text preprocessing, feature selection as well as training and classification section. Study uses a hybrid Genetic Algorithm trained Bayesian model for which the GA is used for feature selection; while, the Bayesian algorithm is used as classifier
Computational solution of networks versus cluster grouping for social network contact recommender system
Get PDFGraphs have become the dominant life-form of many tasks as they advance a structural system to represent many tasks and their corresponding relationships. A powerful role of networks and graphs is to bridge local feats that exist in vertices or nodal agents as they blossom into patterns that helps explain how nodes and their corresponding edges impacts a complex effect that ripple via a graph. User cluster are formed as a result of interactions between entities – such that many users today, hardly categorize their contacts into groups such as “family”, “friends”, “colleagues”. The need to analyze such user social graph via implicit clusters, enables the dynamism in contact management. Study seeks to implement this dynamism via a comparative study of the deep neural network and friend suggest algorithm. We analyze a user’s implicit social graph and seek to automatically create custom contact groups using metrics that classify such contacts based on a user’s affinity to contacts. Experimental results demonstrate the importance of both the implicit group relationships and the interaction-based affinity in suggesting friends
Compact wide stopband microstrip lowpass filter using polygon patches and meandered lines
Get PDFIn this paper, a low pass filter based on T-Shaped resonator is presented. The T-Shaped resonator consists of meandered lines and rectangular patches. Also, the LC model and transfer function of the proposed resonator is presented. For suppression of spurious harmonics, a bandstop structure consists of hexangular patches and open stubs has been utilized. Finally, the wide stopband microstrip lowpass filter with cutoff frequency 2.72 GHz has been simulated, fabricated and measured. The LPF has good characteristics such as wide stopband and insertion loss lower than 0.18 dB in the passband region. The rejection level is less than -20 dB from 2.98 up to 21.3 GHz. The filter size is 10.5 mm×12.7 mm, or 0.131 λg× 0.158 λg, where λg is the guided wavelength. The measured and simulated results of the filter is in good agreement with each other, which show the merits of low insertion loss and wide stopband
Tailored flower pollination (TFP) algorithm for diminution of real power loss
Get PDFIn this paper, Tailored Flower Pollination (TFP) algorithm is proposed to solve the optimal reactive power problem. Comprising of the elements of chaos theory, Shuffled frog leaping search and Levy Flight, the performance of the flower pollination algorithm has been improved. Proposed TFP algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss
Inventory prediction and management in Nigeria using market basket analysis associative rule mining: memetic algorithm based approach
Get PDFA key challenge in businesses today is determining inventory level for each product (to be) sold to clients. A pre-knowledge will suppress inventory stock-up and help avert unnecessary demurrage. It will also avoid stock out and avert loss of clients to competition. Study aims to unveil customer’s behavior in purchasing goods and thus, predict a next time purchase as well as serve as decision support to determine the required amount of each goods inventory. Study is conducted for Delta Mall (Asaba and Warri branches) department store. We adapt the memetic algorithm on market basket dataset to examine buying behavior of customers, their preference and frequency at which goods are purchased in common (basket). Result shows some items placed in basket allow customers to purchase items of similar value, or best combined with the selected items due to shelf-placement via concept of feature drift. Model yields 21-rules for eight items obtained from data transaction mining dataset acquired from Delta Mall
Latest trends, challenges and solutions in security in the era of cloud computing and software defined networks
Get PDFThe emergence of cloud computing has changed perception of all regarding software delivery, development models and infrastructure. Cloud computing has a potential of providing elastic, easily manageable, powerful and cost effective solutions. The rapid transition to cloud computing has fueled concerns on the security issues. The migration of the user’s data and applications in a shared environment of a cloud, where there is a collocation of several users increases security related concerns. Several research efforts have been made in evaluating challenges related to security faced by the cloud computing environments, a number of solutions of such problems have also been proposed. Integrated security solutions should be devised to deal with the increasing security risks. In this paper, a detailed cloud computing survey, key services and concepts are being presented. This paper attempts to evaluate various security threats to cloud computing and a number of security solutions have also been discussed. Furthermore, a brief view of the cloud security regulatory bodies and compliance have also been presented. Despite the research efforts in cloud security field, there are still some open research problems and challenges which are discussed in this paper
Using business intelligence solutions for forecasting in marketing researches
Get PDFBusiness intelligence comprises of tools and applications that are leverages software and services to translate data into intelligent actions for strategic, tactical and operational decisions. The intelligent business solution facilitates and develops the service provided to the market researchers, saves time and effort needed to identify the customers predict demand and manage production more efficiently, ability to explore possibilities to increase revenue. The purpose of this paper is using business intelligence solutions for forecasting in Marketing Researches. The intelligence solutions are helping the market researchers to achieve efficiency, effectiveness, and differentiation
Recent trends in big data using hadoop
Get PDFBig data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbor algorithm is discreetly chosen among them and described along with an example.
Financial stock application using websocket in real time application
Get PDFReal time web application involves clients which is also known as a browser getting updates from the server as they happen. The finance world is moving fast, and human brains cannot sustain processing data at that speed, so algorithms are used with regards to the limit observed in old-style real-time communication which include long polling, polling server-sent events and comets, this paper uses WebSocket technology when dealing with real time applications. The Web Socket offers improved result as compared to the conventional approaches that are considered to be good solution of providing real time information and lessens overhead acquired while communicating over the internet and offers stateful, efficient communication among web servers and clients. When there is need to track companies worth/stock using a dashboard immediately and not some seconds ago, WebSocket can be used to stream data without delays
Classifiers ensemble and synthetic minority oversampling techniques for academic performance prediction
Get PDFThe increasing need for data driven decision making recently has resulted in the application of data mining in various fields including the educational sector which is referred to as educational data mining. The need for improving the performance of data mining models has also been identified as a gap for future researcher. In Nigeria, higher educational institutions collect various students’ data, but these data are rarely used in any decision or policy making to improve the academic performance of students. This research work, attempts to improve the performance of data mining models for predicting students’ academic performance using stacking classifiers ensemble and synthetic minority over-sampling techniques. The research was conducted by adopting and evaluating the performance of J48, IBK and SMO classifiers. The individual classifiers models, standard stacking classifier ensemble model and stacking classifiers ensemble model were trained and tested on 206 students’ data set from the faculty of science federal university Dutse. Students’ specific previous academic performance records at Unified Tertiary Matriculation Examination, Senior Secondary Certificate Examination and first year Cumulative Grade Point Average of students are used as data inputs in WEKA 3.9.1 data mining tool to predict students’ graduation classes of degrees at undergraduate level. The result shows that application of synthetic minority over-sampling technique for class balancing improves all the various models performance with the proposed modified stacking classifiers ensemble model outperforming the various classifiers models in both performance accuracy and RSME values making it the best model