An Intelligent Approach for Prediction of Life Style Conditions in Adolescent Girls by Analyzing Health Parameters

Monitoring health parameters has become a challenging task due to unpredictable diseases and related symptoms. Lifestyle is a crucial factor to decide to be healthy, in adolescent girls especially. This chapter presents a work in progress on prediction of lifestyle of adolescent girls based on problems like unhealthy routines of eating habits, sleep patterns, stress, etc. Therefore, an IT-enabled system is presented to assess current lifestyle of adolescent girls in an easy and faster way. A systematic survey is conducted with specially designed survey form by consulting medical practitioners and physical trainers. Twenty-one factors related to age, diet habits, exercise habits, sleeping habits, health history, etc. are included in the expert-guided form. One hundred fifty-five individual responses are collected and assessed manually by medical experts to annotate as healthy or unhealthy types. The healthy lifestyle prediction accuracy with support vector machine is 83.87% whereas it is 80.64% using logistic regression.

Monitoring of Kidney Functions, Electrolytes and Volemia Analyzed by Application Example of Artificial Intelligence in Patients with Heart Failure

Basal renal function is a predictor of response to diuretic therapy and marker of poor prognosis. Simultaneous changes in renal function, sodium, potassium values and their interdependence are key parameters in addition to volemia for the assessment of cardiorenal balance. In our paper, an analysis of volemia, electrolytes, and renal function in heart failure was performed using an algorithm based on the ANFIS (Adoptive Neural Fuzzy Inference System), an intelligent approach to renal and heart function monitoring. The study included 90 subjects who were divided into two groups: clinical (n-80) and control (n-10). The base is composed of parameters B-type natriuretic peptide (NT-proBNP), sodium (Na), potassium (K), ejection fraction (EF), EPI creatinine-cystatin C formula and ANFIS expert system combined in neural network and fuzzy logic network. The results showed that the overall trend of data verification in the network with NT-proBNP, Na and K that we formed is approximately 15%, with which subjects can be classified according to the severity of hypervolemia, electrolyte disturbance and renal function. NT-proBNP (pg/mL) had the most influence on the EPI creatinine-cystatin C formula. Serum sodium (Na) has the most influence on the ejection fraction (EF).

Intelligent Approach to Network Device Migration Planning towards Software-Defined IPv6 Networks

Internet and telecom service providers worldwide are facing financial sustainability issues in migrating their existing legacy IPv4 networking system due to backward compatibility issues with the latest generation networking paradigms viz. Internet protocol version 6 (IPv6) and software-defined networking (SDN). Bench marking of existing networking devices is required to identify their status whether the existing running devices are upgradable or need replacement to make them operable with SDN and IPv6 networking so that internet and telecom service providers can properly plan their network migration to optimize capital and operational expenditures for future sustainability. In this paper, we implement “adaptive neuro fuzzy inference system (ANFIS)”, a well-known intelligent approach for network device status identification to classify whether a network device is upgradable or requires replacement. Similarly, we establish a knowledge base (KB) system to store the information of device internetwork operating system (IoS)/firmware version, its SDN, and IPv6 support with end-of-life and end-of-support. For input to ANFIS, device performance metrics such as average CPU utilization, throughput, and memory capacity are retrieved and mapped with data from KB. We run the experiment with other well-known classification methods, for example, support vector machine (SVM), fine tree, and liner regression to compare performance results with ANFIS. The comparative results show that the ANFIS-based classification approach is more accurate and optimal than other methods. For service providers with a large number of network devices, this approach assists them to properly classify the device and make a decision for the smooth transitioning to SDN-enabled IPv6 networks.

MMPC-RF: A Deep Multimodal Feature-Level Fusion Architecture for Hybrid Spam E-mail Detection

Hybrid spam is an undesirable e-mail (electronic mail) that contains both image and text parts. It is more harmful and complex as compared to image-based and text-based spam e-mail. Thus, an efficient and intelligent approach is required to distinguish between spam and ham. To our knowledge, a small number of studies have been aimed at detecting hybrid spam e-mails. Most of these multimodal architectures adopted the decision-level fusion method, whereby the classification scores of each modality were concatenated and fed to another classification model to make a final decision. Unfortunately, this method not only demands many learning steps, but it also loses correlation in mixed feature space. In this paper, we propose a deep multimodal feature-level fusion architecture that concatenates two embedding vectors to have a strong representation of e-mails and increase the performance of the classification. The paragraph vector distributed bag of words (PV-DBOW) and the convolutional neural network (CNN) were used as feature extraction techniques for text and image parts, respectively, of the same e-mail. The extracted feature vectors were concatenated and fed to the random forest (RF) model to classify a hybrid e-mail as either spam or ham. The experiments were conducted on three hybrid datasets made using three publicly available corpora: Enron, Dredze, and TREC 2007. According to the obtained results, the proposed model provides a higher accuracy of 99.16% compared to recent state-of-the-art methods.

Method of Fuzzy Parametric Selection for Making a Reasonable Decision by an Intelligent Training Module

Selection of an optimal route within the intelligent approach provides for the possibility of applying soft models and computing in estimation of the trainee presence in the knowledge space. Among numerous ways of representation and processing of information of this type, the special place is held by those able to adapt to the maximal number of NO factors, characterizing the actual training situations, their measurable data and actual methods and ways of their processing that have ambiguities, uncertainties and incompleteness of the respective models and methods. In this paper, we suggest to extend the certain well-proven best practices in data analysis and transformation in the training environment information space to solving the actual training management problems. In addition, the paper demonstrates approaches to the use of Pareto-optimal approaches for fuzzy and underdetermined situations in actual training processes. Formally, this problem is solved with a fuzzy systemic graph. Variants of calculating procedures, allowing the use of the available apparatus of soft models and computing with the purpose to eliminate uncertainties when forming grounded decisions, are given. Methods and criteria of route options selection with regard of vaguely defined functional specification requirements have been developed pursuant to the study.