In this study, an easy, low-cost, green, and environmentally
friendlier reagents have been used to prepare CdS QDs, in chemical
reaction method by mixed different ratio of CdO and sulfur in
paraffin liquid as solvent and oleic acid as the reacting media in
different concentration to get the optimum condition of the reaction
to formation CdS QDs. The results give an indication that the
behavior is at small concentration of 4ml of the oleic acid is best
concentration which give CdS QDs of small about to 9.23 nm with
nano fiber configuration.

CdS Nps (inorganic material) was prepared by the chemical reaction method. ZnPc (organic material) and CdS thin films were prepared separately to compare their characterization with the hybrid BHJ blend ZnPc/CdS thin film (2ml:2ml) by spin coating technique. The structural and morphological properties of the prepared thin films have been measured by XRD and AFM respectively. The XRD pattern for both as-deposited and annealed CdS nanoparticle films shows a hexagonal structure. The crystallite size behavior of as deposited ZnPc films and annealing at 423K for 1h, 2h and 3h was approximately similar to the behavior of heat treated blended films. The grain size was measured by AFM, which decreaseds with annealing temperature for ZnPc films,

By using vacuum evaporation, thin films of the (CdS)0.75-(PbS)0.25 alloy have been deposited to form a nanocrystalline composite. Investigations were made into the morphology, electrical, optical and I-V characteristics of (CdS)0.75-(PbS)0.25 films asdeposited and after annealing at various temperatures. According to AFM measurements, the values of grain sizes rise as annealing temperatures rise, showing that the films' crystallinity has been increased through heat treatment. In addition, heat treatment results in an increase in surface roughness values, suggesting rougher films that could be employed in more applications. The prepared films have direct energy band gaps, and these band gaps increase with the increase in the degrees

This paper presents a method to classify colored textural images of skin tissues. Since medical images havehighly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractaldimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested hereas an alternative and more effective tool. At the same time multiple color channels are used to get more descriptivefeatures.Two multifractal based set of features are suggested here. The first set measures the local roughness property, whilethe second set measure the local contrast property.A combination of all the extracted features from the three colormodels gives a highest classification accuracy with 99.4

The basic solution to overcome difficult issues related to huge size of digital images is to recruited image compression techniques to reduce images size for efficient storage and fast transmission. In this paper, a new scheme of pixel base technique is proposed for grayscale image compression that implicitly utilize hybrid techniques of spatial modelling base technique of minimum residual along with transformed technique of Discrete Wavelet Transform (DWT) that also impels mixed between lossless and lossy techniques to ensure highly performance in terms of compression ratio and quality. The proposed technique has been applied on a set of standard test images and the results obtained are significantly encourage compared with Joint P

Importance of Arabic language stemming algorithm is not less than that of other languages stemming in Information Retrieval (IR) field. Lots of algorithms for finding the Arabic root are available and they are mainly categorized under two approaches which are light (stem)-based approach and root-based approach. The latter approach is somehow better than the first approach. A new root-based stemmer is proposed and its performance is compared with Khoja stemmer which is the most efficient root-based stemmers. The accuracy ratio of the proposed stemmer is (99.7) with a difference (1.9) with Khoja stemmer.

Clustering algorithms have recently gained attention in the related literature since
they can help current intrusion detection systems in several aspects. This paper
proposes genetic algorithm (GA) based clustering, serving to distinguish patterns
incoming from network traffic packets into normal and attack. Two GA based
clustering models for solving intrusion detection problem are introduced. The first
model coined as handles numeric features of the network packet, whereas
the second one coined as concerns all features of the network packet.
Moreover, a new mutation operator directed for binary and symbolic features is
proposed. The basic concept of proposed mutation operator depends on the most
frequent value

The agent-based modeling is currently utilized extensively to analyze complex systems. It supported such growth, because it was able to convey distinct levels of interaction in a complex detailed environment. Meanwhile, agent-based models incline to be progressively complex. Thus, powerful modeling and simulation techniques are needed to address this rise in complexity. In recent years, a number of platforms for developing agent-based models have been developed. Actually, in most of the agents, often discrete representation of the environment, and one level of interaction are presented, where two or three are regarded hardly in various agent-based models. The key issue is that modellers work in these areas is not assisted by simulation plat

Advances in digital technology and the World Wide Web has led to the increase of digital documents that are used for various purposes such as publishing and digital library. This phenomenon raises awareness for the requirement of effective techniques that can help during the search and retrieval of text. One of the most needed tasks is clustering, which categorizes documents automatically into meaningful groups. Clustering is an important task in data mining and machine learning. The accuracy of clustering depends tightly on the selection of the text representation method. Traditional methods of text representation model documents as bags of words using term-frequency index document frequency (TFIDF). This method ignores the relationship an

Fuzzy Based Clustering for Grayscale Image Steganalysis