• Journal of Information Processing Systems
• /
• v.16 no.3
• /
• pp.541-556
• /
• 2020

Wireless networks have become integral to society as they provide mobility and scalability advantages. However, their disadvantage is that they cannot control the media, which makes them vulnerable to various types of attacks. One example of such attacks is the evil twin access point (AP) attack, in which an authorized AP is impersonated by mimicking its service set identifier (SSID) and media access control (MAC) address. Evil twin APs are a major source of deception in wireless networks, facilitating message forgery and eavesdropping. Hence, it is necessary to detect them rapidly. To this end, numerous methods using clock skew have been proposed for evil twin AP detection. However, clock skew is difficult to calculate precisely because wireless networks are vulnerable to noise. This paper proposes an evil twin AP detection method that uses a multiple-feature-based machine learning classification algorithm. The features used in the proposed method are clock skew, channel, received signal strength, and duration. The results of experiments conducted indicate that the proposed method has an evil twin AP detection accuracy of 100% using the random forest algorithm.

• International Journal of Knowledge Content Development & Technology
• /
• v.11 no.4
• /
• pp.35-51
• /
• 2021

This study measured the effects of fake news and propaganda on managing information on COVID-19 among the Nigerian citizenry. This study examined sources of information on COVID-19 available to the people, evaluated reasons behind spreading fake news, examined how fake news has affected the spread of COVID-19 pandemic in Nigeria, established the consequences of fake news on managing COVID-19 pandemic and as well identified ways to contain fake news at a time like this in Nigeria.It is a survey with a sample size of 375 participants selected using simple random technique. Instrument of data gathering was questionnaire widely distributed in the six geo-political zones of Nigeria using Survey monkey. Data was analysed using frequencies, counts and percentages, tables and charts. Findings revealed that people rely more on radio, television, and social media for information on COVID-19. Fake news is spread by people mostly for political reasons and intention to cause panic. In Nigeria, fake news has led to disbelief of the existence of the virus thereby leading to violation of precautionary measures among the citizenry and lack of trust in the government. Concerted effort on the part of the government is required to give public enlightenment on the danger of fake news. Also, directorate of anti-fake news should be established to censor and reprimand sources of fake news. People should always check source of information to confirm its credibility and be weary of sharing unconfirmed information especially on the social media.

• Smart Media Journal
• /
• v.11 no.8
• /
• pp.84-92
• /
• 2022

Precision livestock monitoring promises greater management efficiency for farmers and higher welfare standards for animals. Recent studies on video-based animal activity recognition and tracking have shown promising solutions for understanding animal behavior. To achieve that, surveillance cameras are installed diagonally above the barn in a typical cattle farm setup to monitor animals constantly. Under these circumstances, tracking individuals requires addressing challenges such as occlusion and visual appearance, which are the main reasons for track breakage and increased misidentification of animals. This paper presents a framework for multi-cattle tracking in closed barns with appearance and motion models. To overcome the above challenges, we modify the DeepSORT algorithm to achieve higher tracking accuracy by three contributions. First, we reduce the weight of appearance information. Second, we use an Ensemble Kalman Filter to predict the random motion information of cattle. Third, we propose a supplementary matching algorithm that compares the absolute cattle position in the barn to reassign lost tracks. The main idea of the matching algorithm assumes that the number of cattle is fixed in the barn, so the edge of the barn is where new trajectories are most likely to emerge. Experimental results are performed on our dataset collected on two cattle farms. Our algorithm achieves 70.37%, 77.39%, and 81.74% performance on HOTA, AssA, and IDF1, representing an improvement of 1.53%, 4.17%, and 0.96%, respectively, compared to the original method.

• Smart Media Journal
• /
• v.12 no.2
• /
• pp.36-41
• /
• 2023

With the advent of applicant tracking systems, online recruitment has become more popular, and recruitment fraud has become a serious problem. This research aims to develop a reliable model to detect recruitment fraud in online recruitment environments to reduce cost losses and enhance privacy. The main contribution of this paper is to provide an automated methodology that leverages insights gained from exploratory analysis of data to distinguish which job postings are fraudulent and which are legitimate. Using EMSCAD, a recruitment fraud dataset provided by Kaggle, we trained and evaluated various single-classifier and ensemble-classifier-based machine learning models, and found that the ensemble classifier, the random forest classifier, performed best with an accuracy of 98.67% and an F1 score of 0.81.

• The KIPS Transactions:PartB
• /
• v.11B no.6
• /
• pp.691-700
• /
• 2004

This paper proposes a user's mobility based cooperative proxy caching policy for effective resource management of continuous media objects in mobile environments. This policy is different from the existing proxy caching policies in terms of how to exploit users' mobility. In other words, existing caching policies work based on the information about objects by referring to user's requests within a specified domain whereas the proposed caching policy runs by utilizing a number of user's requests across several domains. So, the proposed policy is applicable to random requests in mobile environments Moreover, we also propose a replacement policy based on weights and playback time. To check the efficiency of the proposed caching policy, the proposed replacement policy is run with different size of caching unit object or segment. The result of performance analyze tells what a ratio of user's mobility is are major factors for the efficient operation of the cooperative caching.

• Fashion & Textile Research Journal
• /
• v.15 no.6
• /
• pp.971-976
• /
• 2013

This study provides insight into current social media influences and purchasing power of the young generation in that the size of both of these demographic groups will impact the apparel companies and retail market for the predictable future Denim apparel companies are aware of the discretionary spending power of the Y and Z Generations. The characteristics of current teens are so similar to college-age individuals in that they have grown up with digital technology and they prefer to communicate via social networking sites. Retailers have utilized these social media platforms in order to capture the attention of the generations. Traditionally marketing campaigns have differentiated between teens and the college-age population. However, the teens actually have larger spending power and more discretionary income. A survey consisted of 32 questions pertaining to Internet media influences, influence of people, and decision factors on decisionmaking related to purchasing selection. A random sampling of 163 females responded to a set of questionnaires. Teens, like college students desire to make their own decisions when they select and purchase denim jeans. Overall 40% of them wanted to make their own decisions when purchasing their jeans, however, a significant number are influenced by their friend's opinions (34%) and the opinions of family members (15%). However, celebrities (10%) had the least influence on their decisions. Teens, like colleges students make decisions based on the same decision factors: fit (63%), cost (23%), brand (10%) and color (2%). The most important factor in determining preference was "fit".

• Journal of Broadcast Engineering
• /
• v.17 no.3
• /
• pp.447-458
• /
• 2012

We present a method to recognize hand gestures using skeletal joint data obtained from Microsoft's Kinect sensor. We propose a combination feature of multi-angle histograms robust to orientation variations to represent the observation sequence of skeletons. The proposed feature efficiently represents the orientation variations of gestures that can be occurred according to person or environment by combining the multiple angle histograms with various angular-quantization levels. The gesture represented as combination of multi-angle histograms and random decision forest classifier improve the recognition performance. We conduct the experiments in hand gesture dataset obtained from a kinect sensor and show that our method outperforms the other methods by comparing the recognition performance.

• Journal of Broadcast Engineering
• /
• v.14 no.6
• /
• pp.703-720
• /
• 2009

This paper proposes a realistic multi-view scalable video coding scheme designed for user's interest in 3D content services and the usage in the future computing environment. Future video coding schemes should support realistic services that make users feel the 3-D presence through stereoscopic or multi-view videos, as well as to accomplish the so-called one-source multi-use services in order to comprehensively support diverse transmission environments and terminals. Unlike the most of video coding methods which only support two-dimensional display, the proposed coding scheme in this paper is the method which can support such realistic services. This paper designs and also implements the proposed coding scheme through integrating Multi-view Video Coding scheme and Scalable Video Coding scheme, then shows its possibility of realization of 3D services by the simulation. The simulation results show the proposed structure remarkably improves the performance of random access with almost the same coding efficiency.

• Imaging Science in Dentistry
• /
• v.53 no.4
• /
• pp.291-302
• /
• 2023

Purpose: Early detection of carotid stenosis can reduce cardiovascular risk. In this study, the maximum-carotid intima-media thickness (CIMT), the mean-CIMT, and the presence of plaque were examined in healthy young Thai adults. Additionally, correlations between CIMT and cardiovascular risk factors were assessed. Materials and Methods: Left and right carotid arteries of 302 participants(15-45 years old) were scanned, with CIMT measured at the far walls of the common carotid artery, carotid bulb, and internal carotid artery. Demographics and risk factors were assessed using a questionnaire. Ten random participants were re-scanned after 4 weeks. Results: The study included 123 (40.70%) male and 179 (59.30%) female participants. The max-CIMT, mean-CIMT, and plaque thickness were 0.400±0.100, 0.403±0.095 and 1.520±0.814 mm, respectively. Male participants had significantly higher CIMT values for nearly all locations and age groups. The right-sided CIMT values were higher for all locations. The carotid bulb had the greatest CIMT values(0.437±0.178 mm), followed by the common (0.403±0.095 mm) and internal(0.361±0.099 mm) carotid arteries. Plaque was present in 18 locations (1.00%), affecting 15 participants (4.97%). These plaques were found in the right carotid bulb (n=9; 0.50%), left carotid bulb (n=7; 0.39%), and right internal carotid artery (n=2; 0.11%). Adjusted multivariable regression revealed significant positive associations between CIMT and male, increased age and "other" occupation (P<0.05). Conclusion: Both max-CIMT and mean-CIMT were approximately 0.4 mm. Plaque was observed in 4.97% of patients, with an average thickness of 1.5 mm. The most influential risk factors for increased CIMT were sex, age, and occupation.

• International Journal of Computer Science & Network Security
• /
• v.24 no.2
• /
• pp.79-88
• /
• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.