To explore the effects of different stimuli on human emotions, olfaction has been incorporated into stimulation materials that might trigger strong emotions. In this study, we designed a new stimulation experiment and established an odor–video physiological signal database (OVPD) of odor–video stimulation for emotion recognition. The database contained the electroencephalogram (EEG) data of ten subjects acquired when experimenting with video–odor stimuli and video-only stimuli. We selected 32 video clips and ten kinds of odors (i.e., orange, rose, alcohol, water, mint, vinegar, durian, foul, acetic, and myrtle) as elicitation materials. Each subject reported a self-assessment of arousal and valence levels after the experiment ended. By comparing the brain topographies associated with two types of stimuli, olfactory stimuli increased orbitofrontal cortex activity, which is consistent with the finding from the fMRI study. Simultaneously with three types of emotions, we presented the average classification accuracies of different features using the support vector machine (SVM) classifier. The optimal accuracies with or without olfactory stimuli were 99.03% and 97.92%, respectively. The superior results show that the olfactory stimulus can enhance emotional experiences.

OVPD: Odor-Video Elicited Physiological Signal Database for Emotion Recognition

The monitoring and replacement of lubricating oil has an important impact on mechanical equipment. In this study, based on the infrared spectroscopy monitoring method, an acid value index prediction model is established. The support vector machine regression method is used to quantitatively analyze the acid number of the oil sample, which verifies the stability and predictive ability of the quantitative prediction model, and we provide a theoretical basis and practical examples for the online monitoring of oil indicators. In addition, a support vector machine regression model is established by observing the changing law of the spectral absorption peak and selecting the dominant wavelength, and it is compared against the prediction algorithm of the long- and short-term memory network. By comparing the deviation relationship between the predicted value and the real chemical value, the feasibility of the infrared spectroscopy prediction model is verified. The experimental results show that the correlation coefficient between the predicted value of the model and the actual measured value reaches 0.98. This proves that the prediction effect of the prediction model based on the infrared spectrum data and the support vector machine regression method is better than that of the long- and short-term memory network trend prediction model, and the predicted results are reliable.

/pdf/acid-number-prediction-model-of-lubricating-oil-based-on-mid-1nyq51kd.pdf

Acid Number Prediction Model of Lubricating Oil Based on Mid-Infrared Spectroscopy

To explore the effects of different stimuli on human emotions, olfaction has been incorporated into stimulation materials that might trigger strong emotions. In this study, we designed a new stimulation experiment and established an odor&#x2013;video physiological signal database (OVPD) of odor&#x2013;video stimulation for emotion recognition. The database contained the electroencephalogram (EEG) data of ten subjects acquired when experimenting with video&#x2013;odor stimuli and video-only stimuli. We selected 32 video clips and ten kinds of odors (i.e., orange, rose, alcohol, water, mint, vinegar, durian, foul, acetic, and myrtle) as elicitation materials. Each subject reported a self-assessment of arousal and valence levels after the experiment ended. By comparing the brain topographies associated with two types of stimuli, olfactory stimuli increased orbitofrontal cortex activity, which is consistent with the finding from the fMRI study. Simultaneously with three types of emotions, we presented the average classification accuracies of different features using the support vector machine (SVM) classifier. The optimal accuracies with or without olfactory stimuli were 99.03&#x0025; and 97.92&#x0025;, respectively. The superior results show that the olfactory stimulus can enhance emotional experiences. 

The neural representation of a repeated stimulus is the standard against which a deviant stimulus is measured in the brain, giving rise to the well-known mismatch response. It has been suggested that individuals with dyslexia have poor implicit memory for recently repeated stimuli, such as the train of standards in an oddball paradigm. Here, we examined how the neural representation of a standard emerges over repetitions, asking whether there is less sensitivity to repetition and/or less accrual of “standardness” over successive repetitions in dyslexia. We recorded magnetoencephalography (MEG) as adults with and without dyslexia were passively exposed to speech syllables in a roving-oddball design. We performed time-resolved multivariate decoding of the MEG sensor data to identify the neural signature of standard vs. deviant trials, independent of stimulus differences. This “multivariate mismatch” was equally robust and had a similar time course in the two groups. In both groups, standards generated by as few as two repetitions were distinct from deviants, indicating normal sensitivity to repetition in dyslexia. However, only in the control group did standards become increasingly different from deviants with repetition. These results suggest that many of the mechanisms that give rise to neural adaptation as well as mismatch responses are intact in dyslexia, with the possible exception of a putatively predictive mechanism that successively integrates recent sensory information into feedforward processing.

/pdf/the-neural-representation-of-a-repeated-standard-stimulus-in-2sgzk9j6.pdf

The Neural Representation of a Repeated Standard Stimulus in Dyslexia

With growth of Internet and World Wide Web, the human dependency on websites and web applications has increased significantly in present days. Browsers and general web concepts are more familiar to most people to use than any other abstract computing interface. Users performing sensitive transactions online have paved a way for the attackers to spoof and tamper the transaction data. SQL Injection is one of the most frequent web attack methods used by attackers to steal confidential data from organisations. It is a code injection technique that allows hackers to inject malicious SQL statements into input fields, which is then executed by the underlying SQL database. Attackers can also use web application URLs for inserting malicious SQL queries. Although a significant amount of research has been done on SQLIA Detection and Prevention, SQL injection attacks are still prevalent and cannot be eliminated completely. In this paper, we have provided an overview of SQL Injection vulnerability, different forms of SQL Injection Attacks and threats posed by the attack. We propose a system to secure web applications using Hashing and Aho-Corasick pattern matching algorithm.

SQL Injection Detection and Prevention using Aho-Corasick Pattern Matching Algorithm

In this paper, we solely focus on the EEG dataset. Using SVM classifier with external library LibSVM (3.23), we have classified our EEG SEED dataset and have achieved tremendous improvement in the accuracy and performance. Moreover, we have listed and explained the different approaches followed to improvise the performance and accuracy of our dataset. Further, we have also compared various existing approaches performed on our dataset using various classifiers ELM, SVM with KNN, SVM with SEED dataset and SVM with DEAP dataset. By using library LibSVM (3.23), we increased the performance of each run by 4% finally resulting with 79.38% accuracy having tensor flow environment.

/pdf/eeg-based-emotion-recognition-using-svm-and-libsvm-3as5y3lonv.pdf

EEG based Emotion Recognition using SVM and LibSVM

Frequent pattern mining is the most researched field in data mining. This paper provides comparative study of fundamental algorithms and performance analysis with respect to both execution time and memory usage. It also provides brief overview of current trends in frequent pattern mining and it applications. There are two categories of frequent pattern mining the algorithm, namely Apriori algorithm and Tree structure algorithm. The Apriori based algorithm uses generate and test strategy approach to find frequent pattern by constructing candidate items and checking their counts and frequency from transactional databases. The Tree structure algorithm uses a text only approach. There is no need to generate candidate item sets. Many tree based structures have been proposed to represent the data for efficient pattern discovery including FP-Tree, CAT-Tree, CAN-Tree, CP-Tree, and etc. Most of the tree based structure allows efficient mining with single scan over the database. In this paper, we describe the formatting guidelines for IJCA Journal Submission. General Terms Your general terms must be any term which can be used for general classification of the submitted material such as Pattern Recognition, Security, Algorithms et. al.

/pdf/frequent-pattern-mining-algorithms-analysis-4ue4dq44gz.pdf

Frequent Pattern Mining Algorithms Analysis

Web application plays an important role in different fields like finance sector, business, shopping etc. There is no. of web application vulnerabilities such as SQL injection, Buffer overflow etc. Above these SQL Injection vulnerabilities are very harmful for web applications. In literature survey there are number of technique used to prevent SQLIA in application level, but not in database level. SQL injection attacks occur due to vulnerabilities in the design of queries where a malicious user can take advantage of input opportunities to insert code in the queries that modify the query-conditions resulting in unauthorized database access. In this paper we design an effective algorithm to prevent stored procedure from SQLIA in database level. Hirschberg‘s algorithm is used to prevent the stored procedure, which reduces both time as well as space complexity. We also analyse several aspects which have been discussed further.

https://www.ijcaonline.org/archives/volume154/number2/pathak-2016-ijca-912037.pdf

Devising Solution to SQL Injection Attacks

Rapid development in technology has driven our attention towards intelligent and smarter regime. Current lifestyle of people involves spending less time at home and more time at work, a quick responsive smart kitchenware can be designed to save time and money during shopping. This paper focuses on developing a smart fridge that will notify the user of which food items are present in the fridge via email. This smart sensing application will capture image of the items present in the fridge and recognize the list of the items using Amazon Web Services (AWS) Rekognition API. The AWS Polly will translate the list of the items into speech format, an audio .mp3 file, which is played by the Bluetooth speaker. Further, this image of the items in the fridge and the audio file is sent to the user via email. Thus, helping users to avoid food wastage and overspending on unnecessary items. This system is accessible anywhere and anytime by the user.

/pdf/next-generation-smart-fridge-system-using-iot-2wljcso71a.pdf

Next Generation Smart Fridge System using IoT

As you know, it is inevitable to cease the advancement in AI because of the quest for developing systems that learns from experience and comply with the human capabilities. The leading ground-breaking impacts offers both positive and negative aspirations and are addressed as part of this research. To meet with the evil effects of the AI systems, is now the need of the hour to direct the focus on creating safe AI systems. The safe system must satisfy with 2 majorsapplying the security measures and analysing the system. Moreover, system must fulfil the risk and safety valuesintelligence, goals and safety in order to minimise the destruction that may occur due AI technology. However, further research is needed on the actual implementation of the safe AI systems in order to proceed with this research.

Ananta Bhatt

Papers

EEG based Emotion Recognition using SVM and LibSVM

Frequent Pattern Mining Algorithms Analysis

Devising Solution to SQL Injection Attacks

Next Generation Smart Fridge System using IoT

Safe AI Systems