A single-phase algorithm for mining high utility itemsets using compressed tree structures

Mining high utility itemsets (HUIs) from transaction databases considers such factors as the unit profit and quantity of purchased items. Two-phase tree-based algorithms transform a database into compressed tree structures and generate candidate patterns through a recursive pattern-growth procedure. This procedure requires a lot of memory and time to construct conditional pattern trees. To address this issue, this study employs two compressed tree structures, namely, Utility Count Tree and String Utility Tree, to enumerate valid patterns and thus promote fast utility computation. Furthermore, the study presents an algorithm called single-phase utility computation (SPUC) that leverages these two tree structures to mine HUIs in a single phase by incorporating novel pruning strategies. Experiments conducted on both real and synthetic datasets demonstrate the superior performance of SPUC compared with IHUP, UP-Growth, and UP-Growth+ algorithms.     

A Light Weight Secure Communication Scheme for Wireless Sensor Networks

Wireless Personal Communications - The majority of security systems for wireless sensor networks are based on symmetric encryption. The main open issue for these approaches concerns the...     

An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2‐LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search‐based LBG (CS‐LBG), Firefly‐based LBG (FF‐LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2‐LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS‐LBG, FA‐LBG, and JPEG2000 methods. The experimental values revealed that the L2‐LBG process yielded effective compression performance with a better‐quality reconstructed image.     

Poly(1,3,4-oxadiazoles). Effect of structure on polymer properties

Poly(1,3,4-oxadiazole-2,5-diylvinylene) and poly(1,4-phenylene-1,3,4-oxadiazole-2,5-diylvinylene) were prepared by polymerizing different quantities of terephthalic acid (T), maleic acid (M), and hydrazine sulfate (HS) in the presence of fuming sulfuric acid. Homopolymers of M and T and various copolymers of M:T were prepared. The polymers were characterized by viscosity, IR, UV, and elemental analysis. Their solubility in different solvents was investigated. The relative thermal stability of the polymers was evaluated by TGA and DTA.     

Enhanced Photocatalytic and Antibacterial Activities of ZnSe Nanoparticles

Water and microbial contamination is a serious issues to aquatic system and human health. The metal selenide has a tremendous technique to degrade the dyes and bacteria.The present work reports the synthesis of ZnSe nanoparticles in a simple co-precipitation method. The synthesized samples were analyzed by structural, optical, morphological, catalytic and biological activity. The size and bandgap by annealing temperature tuning which are confirm by X-ray Diffraction and UV–Visible spectrometer. The quasi-spherical shapes were confirmed by Scanning Electron Microscope and Transmission Electron Microscope.The photo excited electrons have trapped the metals and promoting the degradation system. The release of ions to the surface was acknowledged by Photo Luminescence spectroscopy. The photocatalytic dye degradation of the Methyl Orange showed that the enhanced activity in high temperature. The photocatalytic dye degradation activity suggested that the temperature change the production of free radicals and ROS formation. The hydroxyl radicals were slewing the dye molecules and bacteria. The obtained results giving the information of ZnSe nanoparticles are one of the fascinating research areas in the current research world. Because of its large application in different field it acts as a promoting catalytic and biological application.

Graphical Abstract

     

Suppression of the α → β-nickel hydroxide transformation in concentrated alkali: Role of dissolved cations

The presence of dissolved cations such as Al and Zn in alkaline electrolyte (6 M KOH) suppresses the -nickel hydroxide transformation. The uptake of Al (10 mol%) and Zn (30 mol%) exhibited by the active material likely stabilizes the -phase. Dissolved Al is deleterious to the performance of the nickel hydroxide electrode, whereas, dissolved Zn enhances the specific discharge capacity of nickel hydroxide by approximately 25% showing that the mode of metal uptake is different in the two cases.     

Thermal analysis of poly(1,3,4-oxadiazole-2,5 diyl-1,2-ethenediyl) and poly(1,4-phenylene-1,3,4-oxadiazole-2,5 diyl-1,2 ethendiyl)

Poly(1,3,4-oxadiazole-2,5-diyl-1,2-ethendiyl) and poly(1,4-phenylene-1,3,4-oxadiazole-2,5 diyl-1,2-ethenediyl) have been prepared by condensation polymerization using fuming sulfuric acid and different quantities of terephthalic acid (T), fumaric acid (F), and hydrazine sulfate (HS). Homopolymers of F and T and various copolymers of F:T have been prepared. The polymer structure was investigated by IR and visible-range spectra and elemental analysis. The existence of poly(1,3,4-oxadiazole-diylphenylene) and poly(hydrazoterephthaloyl) structures was revealed by these studies. These polymers were thermally stable, and most of them did not show a weight loss below 350°C. The relative thermal stabilities of the various polymers have been evaluated by “integral procedural decomposition temperature” and activation energy measurements.     

Faceless identification based on temporal strips

This paper first presents a novel approach for modelling facial features, Local Directional Texture (LDT), which exploits the unique directional information in image textures for the problem of face recognition. A variant of LDT with privacy-preserving temporal strips (TS) is then considered to achieve faceless recognition with a higher degree of privacy while maintaining high accuracy. The TS uses two strips of pixel blocks from the temporal planes, XT and YT, for face recognition. By removing the reliance on spatial context (i.e., XY plane) for this task, the proposed method withholds facial appearance information from public view, where only one-dimensional temporal information that varies across time are extracted for recognition. Thus, privacy is assured, yet without impeding the facial recognition task which is vital for many security applications such as street surveillance and perimeter access control. To validate the reliability of the proposed method, experiments were carried out using the Honda/UCSD, CK+, CAS(ME)² and CASME II databases. The proposed method achieved a recognition rate of 98.26% in the standard video-based face recognition database, Honda/UCSD. It also offers a 81.92% reduction in the dimension length required for storing the extracted features, in contrast to the conventional LBP-TOP.

     

Comparison of TiAIN,AICrN, and AICrN/TiAIN coatings for cutting-tool applications

Monolayer and bilayer coatings of TiAlN, AlCrN, and AlCrN/TiAlN were deposited onto tungsten carbide inserts using the plasma enhanced physical vapor deposition process. The microstructures of the coatings were characterized using scanning electron microscopy （SEM） and atomic force microscopy （AFM）. The SEM micrographs revealed that the AlrN and AlCrN/TiAlN coatings were uniform and highly dense and contained only a limited number of microvoids. The TiAIN coating was non-uniform and highly porous and contained more micro droplets. The hardness and scratch resistance of the specimens were measured using a nanoindentation tester and scratch tester, respectively. Different phases formed in the coatings were analyzed by X-ray diffraction （XRD）. The AlCrN/TiAlN coating exhibited a higher hardness （32.75 GPa）, a higher Young＇s modulus （561.97 GPa）, and superior scratch resistance （LcN = 46 N） compared to conventional coatings such as TiAlN, A1CrN, and TiN.     

A neuro-genetic based short-term forecasting framework for network intrusion prediction system

Information systems are one of the most rapidly changing and vulnerable systems, where security is a major issue. The number of security-breaking attempts originating inside organizations is increasing steadily. Attacks made in this way, usually done by ＂authorized＂ users of the system, cannot be immediately traced. Because the idea of filtering the traffic at the entrance door, by using firewalls and the like, is not completely successful, the use of intrusion detection systems should be considered to increase the defense capacity of an information system. An intrusion detection system （IDS） is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current IDS depends on the system operators in working out the tuning solution and in integrating it into the detection model. Furthermore, an extensive effort is required to tackle the newly evolving attacks and a deep study is necessary to categorize it into the respective classes. To reduce this dependence, an automatically evolving anomaly IDS using neuro-genetic algorithm is presented. The proposed system automatically tunes the detection model on the fly according to the feedback provided by the system operator when false predictions are encountered. The system has been evaluated using the Knowledge Discovery in Databases Conference （KDD 2009） intrusion detection dataset. Genetic paradigm is employed to choose the predominant features, which reveal the occurrence of intrusions. The neuro-genetic IDS （NGIDS） involves calculation of weightage value for each of the categorical attributes so that data of uniform representation can be processed by the neuro-genetic algorithm. In this system unauthorized invasion of a user are identified and newer types of attacks are sensed and classified respectively by the neuro-genetic algorithm. The experimental results obtained in this work show that the system achieves improvement in terms of misclassification cost when compared with conventional IDS. The results of the experiments show that this system can be deployed based on a real network or database environment for effective prediction of both normal attacks and new attacks.