Optical burst switching (OBS) network is a new generation optical communication technology. In an OBS network, an edge node first sends a control packet, called burst header packet (BHP) which reserves the necessary resources for the upcoming data burst (DB). Once the reservation is complete, the DB starts travelling to its destination through the reserved path. A notable attack on OBS network is BHP flooding attack where an edge node sends BHPs to reserve resources, but never actually sends the associated DB. As a result the reserved resources are wasted and when this happen in sufficiently large scale, a denial of service (DoS) may take place. In this study, we propose a semi-supervised machine learning approach using k-means algorithm

Thin films of tin sulfide (SnS) were prepared by thermal evaporation technique on glass substrates, with thickness in the range of 100, 200 and 300nm and their physical properties were studied with appropriate techniques. The phase of the synthesized thin films was confirmed by X-ray diffraction analysis. Further, the crystallite size was calculated by Scherer formula and found to increase from 58 to 79 nm with increase of thickness. The obtained results were discussed in view of testing the suitability of SnS film as an absorber for the fabrication of low-cost and non toxic solar cell. For thickness, t=300nm, the films showed orthorhombic OR phase with a strong (111) preferred orientation. The films deposited with thickness < 200nm deviate

Cadmium Oxide thin films were deposited on glass substrate by spray pyrolysis technique at different temperatures (300,350,400, 500)oC. The optical properties of the films were studied in this work. The optical band-gap was determined from absorption spectra, it was found that the optical band-gap was within the range of (2.5-2.56)eV also width of localized states and another optical properties.

Chalcogenide glasses SeTe have been prepared from the high purity constituent elements .Thin films of SeTe compound have been deposited by thermal evaporation onto glass substrates for different values of film thickness . The effect of varying thickness on the value of the optical gap is reported . The resultant films were in amorphous nature . The transmittance spectra was measured for that films in the wavelength range (400-1100) nm . The energy gap for such films was determined .

GaN thin films were deposited by thermal evaporation onto
glass substrates at substrate temperature of 403 K and a thickness of
385 nm . GaN films have amorphous structure as shown in X-ray
diffraction pattern . From absorbance data within the range ( 200-
900 ) nm direct optical energy gap was calculated . Also the others
optical parameters like transmittance T, reflectance R , refractive
index n , extinction coefficient k , real dielectric constant 1 Î , and
imaginary dielectric constant 2 Î were determined . GaN films
have good absorbance and minimum transmittance in the region of
the visible light .

Key-frame selection plays an important role in facial expression recognition systems. It helps in selecting the most representative frames that capture the different poses of the face. The effect of the number of selected keyframes has been studied in this paper to find its impact on the final accuracy of the emotion recognition system. Dynamic and static information is employed to select the most effective key-frames of the facial video with a short response time. Firstly, the absolute difference between the successive frames is used to reduce the number of frames and select the candidate ones which then contribute to the clustering process. The static-based information of the reduced sets of frames is then given to the fuzzy C-Means algor

In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen

Face recognition and identity verification are now critical components of current security and verification technology. The main objective of this review is to identify the most important deep learning techniques that have contributed to the improvement in the accuracy and reliability of facial recognition systems, as well as highlighting existing problems and potential future research areas. An extensive literature review was conducted with the assistance of leading scientific databases such as IEEE Xplore, ScienceDirect, and SpringerLink and covered studies from the period 2015 to 2024. The studies of interest were related to the application of deep neural networks, i.e., CNN, Siamese, and Transformer-based models, in face recogni