Identifying Spam Patterns in SMS using Genetic Programming Approach

Nonlinear model identification of an experimental ball-and-tube system using a genetic programming approach

Activity recognition from smartphone sensor inputs is of great importance to enhance user experience. Our study aims to investigate the applicability of Genetic Programming (GP) approach on this complex real world problem. Traditional methods often require substantial human efforts to define good features. Moreover the optimal features for one type of activity may not be suitable for another. In comparison, our GP approach does not require such feature extraction process, hence, more suitable for complex activities where good features are difficult to be pre-defined. To facilitate this study we therefore propose a benchmark of activity data collected from various smartphone sensors, as currently there is no existing publicly available database for activity recognition. In this study, a GP-based approach is applied to nine types of activity recognition tasks by directly taking raw data instead of features. The effectiveness of this approach can be seen by the promising results. In addition our benchmark data provides a platform for other machine learning algorithms to evaluate their performance on activity recognition.

This paper presents a novel nonparametric efficiency analysis technique based on the Genetic Programming (GP) in order to measure efficiency of Iran electric power plants. GP model was used to predict the output of power plants with respect to input data. The method, we presented here, is capable of finding a best performance among power plant based on the set of input data, GP predicted results and real outputs. The advantage of using GP over traditional statistical methods is that in prediction with GP, the researcher doesn’t need to assume the data characteristic of the dependent variable or output and the independent variable or input. In this proposed methodology to calculate the efficiency scores, a novel algorithm was introduced which worked on the basis of predicted and real output values. To validate our model, the results of proposed algorithm for calculating efficiency rank of power plants were compared to traditional method. Real data was presented for illustrative our proposed methodology. Results showed that by utilizing the capability of input-output pattern recognition of GP, this method provides more realistic results and outperform in identification of efficient units than the conventional methods.

In recent times, the increment of mobile phone usage has resulted in a huge number of spam messages. Spammers continuously apply more and more new tricks that cause managing or preventing spam messages a challenging task. The aim of this study is to detect spam message to prevent different cybercrimes as spam messages have become a security threat nowadays. In this paper, studies on SMS spam problems to perform a better accuracy using several different techniques such as Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, Random Forest, Logistic Regression and some more are performed. The result indicated that Support Vector Machine achieved the highest accuracy of 99%, indicating it might be useful as an effective machine learning system for future research.

Nowadays, spam messages have been overflowing in many countries. They seriously violate personal rights, and may even harm the national security. The existing filtering techniques usually uses traditional text classifiers, which are more suitable to deal with normal long texts, therefore, it often faces some serious challenges, such as the sparse data problem and noise data in the SMS message. This research work proposes a message topic model (MTM) for SMS spam filtering. The MTM derives from the famous probability topic model. Although the MTM is based on probability topic model, it is different from the famous standard Latent Dirichlet Allocation (LDA) in the following aspects: (1) For the purpose of overcoming the sparsity problem in SMS message classification, first, the standard K-means algorithm is used to classify the training data into rough classes, then, aggregates all the spam messages of a class into a single document. (2) Symbol semantics is taken in account. Some preprocessing rules and background terms are considered to make the model more appropriate to fully represent SMS spam. Finally, we compare the MTM with the SVM and the standard LDA on the public SMS spam corpus. The experimental results show that the MTM is more effective for the task of SMS spam filtering.

The genetic programming approach is proposed to predict temper embrittlement of rotor steel(30Cr2MoV).Two independent data sets are obtained experimentally:training data and verifying data.Peak current density of reactivation, temperature of electrolyte,the general chemical composition parameter(J-factor),chemical composition of Cr and S, hardness and the grain size parameter of the material are used as independent variables,while fracture appearance transition temperature as dependent variable.On the basis of training data, the best model is obtained by genetic programming,and the accuracy of it is verified with the verifying data.The prediction error of the model is within the scatter of±20℃.The results suggest that,the prediction model obtained by genetic programming is feasible and effective.

SMS or Short Message Service is usually found on cell phones. SMS is divided into two categories, namely SMS spam and SMS non-spam (ham). Spam SMS is an SMS that is annoying to phone users because it tends to contain messages that are not important such as promos and scams. Meanwhile, non-spam SMS (ham) tend to contain important SMS, such as messages from previous users. In this study, the classification of spam SMS and non-spam SMS (ham) was carried out using the logistic regression method. The purpose of this study is to distinguish or classify between spam and non-spam SMS (ham). The dataset in this study amounted to 1143 data, there are two columns, namely the text column and the label column. The number for spam messages is 566 messages and the number for non-spam messages is 577. The proposed method gets a better accuracy of 95%.

The aim of this study was to develop three roundabout entry capacity models (RECMs) by employing evolutionary-based regression techniques such as genetic programming (GP), age-layered population structure genetic programming (ALPSGP) and grammatical evolution genetic programming (GEGP) in mixed traffic conditions. Necessary data were collected from 27 roundabouts located in eight states in India. The influence area for gap acceptance method was used to determine the critical gap. To assess the significance of the models and select the best, the modified rank index was applied. The results showed that the GEGP model performed better than the GP and ALPSGP models. The GEGP model is also applicable in practice because of its simplicity. Sensitivity analysis revealed that the critical gap is the prime variable in the development of RECMs. The findings of this study should be useful for traffic planners and designers in the capacity estimation of roundabouts in mixed traffic conditions in developing countries with traffic characteristics similar to those in India.

Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a population of primitive 3D operators (e.g., 3D-Gabor and wavelet). In this way, the scale and shift invariant features can be effectively extracted from both color and optical flow sequences. We intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the GP searching space to effectively accelerate the convergence of optimal solutions. In our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the GP fitness function. After the entire evolution procedure finishes, the best-so-far solution selected by GP is regarded as the (near-)optimal action descriptor obtained. The GP-evolving feature extraction method is evaluated on four popular action datasets, namely KTH, HMDB51, UCF YouTube, and Hollywood2. Experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learned.

In this article, an evolutionary algorithm known as Genetic Programming (GP) was used to design a parametric speech quality estimation model. Nowadays, GP is one of the machine learning techniques employed in a quality estimation process. In principle, the set of quality-affecting parameters was used as an input to the designed estimation model based on GP approach in order to estimate a quality of synthesized speech transmitted over IP channel (VoIP environment). The performance results obtained by the designed estimation model have confirmed the good properties of genetic programming, namely good accuracy and generalization ability; this makes it to be perspective approach to a quality estimation of this type of speech in the corresponding environment. The developed model can be helpful for network operators and service providers implementing it in planning phase or early-development stage of telecommunication services based on synthesized speech.

The proliferation of unsolicited short messages (SMS spam) poses persistent challenges to mobile communication security and user privacy. This study presents a systematic benchmarking and analytical investigation of classical machine learning approaches for SMS spam detection, focusing on the impact of text feature representation under imbalanced short-text conditions.In practical SMS filtering systems, minimizing false positives (i.e., incorrectly blocking legitimate messages) is a critical operational constraint. Therefore, beyond overall accuracy, precision and specificity are emphasized to ensure reliable preservation of legitimate communication. Using the SMSSpamCollection dataset (5574 messages: 747 spam and 4827 ham), seven feature representation techniques were evaluated in combination with six widely adopted classifiers, resulting in 42 configurations assessed under 10-fold cross-validation. The results demonstrate that feature representation plays a more critical role than classifier complexity. Character-level 3-grams combined with Logistic Regression achieved the best overall performance, reaching 98.55% accuracy, with 98.55% precision and 90.50% recall for the spam class (F1-score = 94.32%), and 0.9893 AUC. Linear SVM produced comparable results, highlighting the effectiveness of linear models when paired with expressive representations. Beyond reporting performance metrics, this study analyzes feature–classifier interaction patterns and clarifies practical trade-offs between precision, recall, and computational efficiency. The findings provide reproducible baselines and structured guidance for designing efficient SMS spam filtering systems.

Short Message Service is one of the most using services in mobile phones. In daily life, several spam messages, which could disturb mobile phone users, are received frequently. Unwanted messages could be delivered for advertisement, announcement of promotional events and/or only for disturbing people. In this study, 3-tier hybrid message filtering architecture has been introduced to protect the mobile users from spam messages. In the first two steps, incoming messages are classified based on the white /black lists and are identified as “legitimate SMS” and “spam SMS”. If the number of incoming message is not in those lists, the message is examined according to their meaning and morphological features. The success rate of k-Nearest Neighbor and Naive Bayes algorithms is about 96%. The proposed architecture has been implemented on Android platform.

<p>Symbolic regression (SR) is a function identification process, the task of which is to identify and express the relationship between the input and output variables in mathematical models. SR is named to emphasise its ability to find the structure and coefficients of the model simultaneously. Genetic Programming (GP) is an attractive and powerful technique for SR, since it does not require any predefined model and has a flexible representation. However, GP based SR generally has a poor generalisation ability which degrades its reliability and hampers its applications to science and real-world modeling. Therefore, this thesis aims to develop new GP approaches to SR that evolve/learn models exhibiting good generalisation ability. This thesis develops a novel feature selection method in GP for high-dimensional SR. Feature selection can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalisation ability. However, feature selection is seldom considered in GP for high-dimensional SR. The proposed new feature selection method utilises GP’s built-in feature selection ability and relies on permutation to detect the truly relevant features and discard irrelevant/noisy features. The results confirm the superiority of the proposed method over the other examined feature selection methods including random forests and decision trees on identifying the truly relevant features. Further analysis indicates that the models evolved by GP with the proposed feature selection method are more likely to contain only the truly relevant features and have better interpretability. To address the overfitting issue of GP when learning from a relatively small number of instances, this thesis proposes a new GP approach by incorporating structural risk minimisation (SRM), which is a framework to estimate the generalisation performance of models, into GP. The effectiveness of SRM highly depends on the accuracy of the Vapnik-Chervonenkis (VC) dimension measuring model complexity. This thesis significantly extends an experimental method (instead of theoretical estimation) to measure the VC-dimension of a mixture of linear and nonlinear regression models in GP for the first time. The experimental method has been conducted using uniform and non-uniform settings and provides reliable VC-dimension values. The results show that our methods have an impressively better generalisation gain and evolve more compact model, which have a much smaller behavioural difference from the target models than standard GP and GP with bootstrap, The proposed method using the optimised non-uniform setting further improves the one using the uniform setting. This thesis employs geometric semantic GP (GSGP) to tackle the unsatisfied generalisation performance of GP for SR when no overfitting occurs. It proposes three new angle-awareness driven geometric semantic operators (GSO) including selection, crossover and mutation to further explore the geometry of the semantic space to gain a greater generalisation improvement in GP for SR. The angle-awareness brings new geometric properties to these geometric operators, which are expected to provide a greater leverage for approximating the target semantics in each operation, and more importantly, to be resistant to overfitting. The results show that compared with two kinds of state-of-the-art GSOs, the proposed new GSOs not only drive the evolutionary process fitting the target semantics more efficiently but also significantly improve the generalisation performance. A further comparison on the evolved models shows that the new method generally produces simpler models with a much smaller size and containing important building blocks of the target models.</p>

On the use of the genetic programming for balanced load distribution in software-defined networks

Accurate predictions of sea-level are important for geodetic applications, navigation, coastal, industrial and tourist activities. In the current work, the Genetic Programming (GP) and artificial neural networks (ANNs) were applied to forecast half-daily and daily sea-level variations from 12 hours to 5 days ahead. The measurements at the Cocos (Keeling) Islands in the Indian Ocean were used for training and testing of the employed artificial intelligence techniques. A comparison was performed of the predictions from the GP model and the ANN simulations. Based on the comparison outcomes, it was found that the Genetic Programming approach can be successfully employed in forecasting of sea level variations.