Early prediction of diseases especially dengue fever in the case of Malaysia, is very crucial to enable health authorities to develop response strategies and context preventive intervention programs such as awareness campaigns for the high risk population before an outbreak occurs. Some of the deficiencies in dengue epidemiology are insufficient awareness on the parameter as well as the combination among them. Most of the studies on dengue prediction use standalone models which face problem of finding the appropriate parameter since they need to apply try and error approach. The aim of this paper is to conduct experiments for determining the best network structure that has effective variable and fitting parameters in predicting the spread of the dengue outbreak. Four model structures were designed in order to attain optimum prediction performance. The best model structure was selected as predicting model to solve the time series prediction of dengue. The result showed that neighboring location of dengue cases was very effective in predicting the dengue outbreak and it is proven that the hybrid Genetic Algorithm and Neural Network (GANN) model significantly outperforms standalone models namely regression and Neural Network (NN).

Generally, E-nose mimics human olfactory sense to detect and distinguish an odor or gasses or volatile organic compound from a few objects such as food, chemicals, explosive etc. Thus, E-nose can be used to measure gas emitted from food due to its ability to measure gas and odor. Principally, the E-nose operates by using a number of sensors to response to the odorant molecules (aroma). Each sensor will respond to their specific gas respectively. These sensors are a major part of the electronic nose to detect gas or odor contained in a volatile component. Information about the gas detected by sensors will be recorded and transmitted to the signal processing unit to perform the analysis of volatile organic compound (VOC) pattern and stored in the database classification, in order to determine the type of odor. Classification is a way to distinguish a mixture odor/aroma obtained from gas sensors in an electric signal form. In this paper, we discussed briefly about electronic nose, it’s principle of work and classification method and in order to classify food freshness.

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the progression of cyber-physical systems in the wider maritime industry and port domains, along with their rising cybersecurity vulnerabilities.Existing and applicable industry and government standards and mandates associated with cybersecurity attempt to impose regulatory compliance and increase asset cybersecurity integrity with reduced emphasis however, in the existing OT (Operational Technology) components and systems. The use of security risk assessment tools and processes that are used in other industrial sectors, such as the Security Risk Assessment (SRA) and the Bow Tie Analysis methods, can support the evaluation of IT/OT infrastructure for cyber-physical security susceptibilities and then assign suitable reactive measures. The implementation of cybersecurity safeguards that arise through the implementation of the MITRE ATT&CK Threat Model can enhance the cybersecurity posture of those assets that support the logistics chain, assuming that they are intermittently adapted following evaluations for their effectiveness and suitability. Finally, the improvement of stakeholder communication and cyber-awareness along with the increase in cyber-physical security resiliency can further be aided by the effective convergence of the segregated cyber and physical security elements of waterside or landside-based IT/OT infrastructure.

Recently, we have proposed the Guided Particle Swarm Optimization (GPSO) algorithm as a novel approach in facial emotion recognition. GPSO was a modification to the Particle Swarm Optimization (PSO) algorithm, which is widely recognized as an efficient optimization algorithm with applicability in many areas. While the results we obtained from the real-time system that we developed based on the said algorithm were very good, the question that still remained was, how does this method compare with the more conventional classification approaches, such as neural network? With the aim of answering this question, we have now re-implemented our emotion recognition system using the Back Propagation Neural Network (BPNN). The BPNN used has 3 layers, consisting of the input layer of 20 neurons representing the x and y coordinates of same 10 Facial Points (FPs) used in our previous experiments; the output layer has 7 neurons representing the six basic emotions plus Neutral and a hidden layer of 20 neurons. The same data (video clips) of 20 subjects used in previous experiments were used, randomly partitioning the data in the ratio of 60:40 to train and test the network respectively. The results show that while the BPNN has its own merits in terms of speed of detection, the GPSO method performed better in accuracy of detection for all but one of the six basic emotions.