Association Rule Mining Frequent-Pattern-Based Intrusion Detection in Network

In the network security system, intrusion detection plays a significant role. The network security system detects the malicious actions in the network and also conforms the availability, integrity and confidentiality of data information resources. Intrusion identification system can easily detect the false positive alerts. If large number of false positive alerts are created then it makes intrusion detection system as difficult to differentiate the false positive alerts from genuine attacks. Many research works have been done. The issues in the existing algorithms are more memory space and need more time to execute the transactions of records. This paper proposes a novel framework of network security Intrusion Detection System (IDS) using Modified Frequent Pattern (MFP-Tree) via K-means algorithm. The accuracy rate of Modified Frequent Pattern Tree (MFPT)-K means method in finding the various attacks are Normal 94.89%, for DoS based attack 98.34%, for User to Root (U2R) attacks got 96.73%, Remote to Local (R2L) got 95.89% and Probe attack got 92.67% and is optimal when it is compared with other existing algorithms of K-Means and APRIORI.     

Efficient half-precision floating point multiplier targeting color space conversion

Multimedia Tools and Applications - Color Space Conversion (CSC) in image processing applications, demands computationally simple floating point multipliers consuming less area and power. This...     

Effect of microwave sintering on the properties of copper oxide doped Y-TZP ceramics

The effect of various amounts of copper oxide (CuO) up to 1?wt% on the densification behaviour and mechanical properties of 3?mol% yttria-tetragonal zirconia polycrystal (Y-TZP) were studied by using microwave (MW) sintering method. The MW sintering was performed at temperatures between 1100?°C and 1400?°C, with a heating rate of 30?°C/min. and holding time of 5?min. The beneficial effect of MW in enhancing densification was also compared for the undoped and 0.2?wt% CuO-doped Y-TZP when subjected to conventional sintering (CS) method. The results showed that significant enhancement in the relative density and Vickers hardness were observed for the undoped Y-TZP when MW-sintered between 1100?°C and 1250?°C. It was revealed that the 0.2?wt% CuO-doped Y-TZP and MW sintered at 1250–1300?°C could attain ≥?99.8% of theoretical density, Vickers hardness of about 14.4?GPa, fracture toughness of 7.8 MPam^1/2 and exhibited fine equiaxed tetragonal grain size of below 0.25?µm. In contrast, the addition of 1?wt% CuO was detrimental and the samples exhibited about 50% monoclinic phase upon sintering coupled with poor bulk density and mechanical properties. The study also revealed that the addition of 0.2?wt% CuO and subjected to conventional sintering produced similar densification as that obtained for microwave sintering, thus indicating that the dopant played a more significant role than the sintering method.     

Synthesis and properties of bio-waste-based hydroxyapatite via hydrothermal process

In this study, a facile hydrothermal method is applied to produce hydroxyapatite (HA) particles using egg shells as calcium precursors and fruit waste extracts (banana peel) as biomolecular templates at 150 °C for a reaction time period of 12 hours (h) and 24 hours. The sintering of the green samples of hydroxyapatite were conducted at 1250 °C in air for 2 h. The results showed that pectin extracted from banana peel extracts assisted in regulating crystal growth to obtain homogeneous hydroxyapatite powder, with higher purity observed for 24 h hydrothermal reaction time. Fourier-transform infrared spectroscopy (FTIR) spectra revealed the presences of phosphate (PO₄³⁻) and hydroxyl (OH⁻) groups in the powders. A relative density of 89.6 % was achieved for sintered hydroxyapatite compacts produced via hydrothermal method for 24 h. The sintered body was characterized by having high Vickers hardness of 5.35 GPa and good fracture toughness of 1.23 MPa√m, suitable for biomedical application.     

Formation of novel thermo-responsive hybrid vesicles: influence of molar ratio of lipids and heating

Thermo-responsive triblock copolymer, [poly(2-isopropyl-2-oxazoline)-b-poly(dimethylsiloxane)-b-poly(2-isopropyl-2-oxazoline] (IDI) was synthesized, for the first time, by ring opening polymerization and it can self-assemble in to polymersome in aqueous solution. We demonstrate a simple route to develop the novel hybrid vesicles, from the IDI and 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), by systematically regulating the molar ratio of DMPC. Effect of heating on the hybrid vesicular solution has also been examined. Differential scanning calorimetry (DSC), dynamic light scattering and fluorescence spectroscopy were used to investigate the role of molar ratio of DMPC and heating time of the mixture on the formation of hybrid vesicles. From DSC measurements, we interestingly found that heating the mixture of IDI and DMPC enhanced the insertion of IDI into the membrane of liposomes. The present findings confirm the potential utility of hybrid vesicular systems for the design of functional hybrid materials and could be used for controlled drug delivery applications.     

Semivolatile organic compounds in French schools: Partitioning between the gas phase,airborne particles and settled dust

The indoor environmental quality in classrooms can largely affect children's daily exposure to indoor chemicals in schools. To date, there has not been a comprehensive study of the concentrations of semivolatile organic compounds (SVOCs) in French schools. Therefore, the French Observatory for Indoor Air Quality (OQAI) performed a field study of SVOCs in 308 nurseries and elementary schools between June 2013 and June 2017. The concentrations of 52 SVOCs, including phthalates, polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), synthetic musks, and pesticides, were measured in air and settled dust (40 SVOCs in both air and dust, 12 in either air or dust). The results showed that phthalates had the highest concentrations among the SVOCs in both the air and dust. Other SVOCs, including tributyl phosphate, fluorene, phenanthrene, gamma-hexachlorocyclohexane (gamma-HCH, lindane), galaxolide, and tonalide, also showed high concentrations in both the air and dust. Theoretical equations were developed to estimate the SVOC partitioning between the air and settled dust from either the octanol/air partition coefficient or the boiling point of the SVOCs. The regression constants of the equations were determined using the data set of the present study for phthalates and PAHs.     

Enhancement of test data compression with multistage encoding

In this paper, we present two multistage compression techniques to reduce the test data volume in scan test applications. We have proposed two encoding schemes namely alternating frequency-directed equal-run-length (AFDER) coding and run-length based Huffman coding (RLHC). These encoding schemes together with the nine-coded compression technique enhance the test data compression ratio. In the first stage, the pre-generated test cubes with unspecified bits are encoded using the nine-coded compression scheme. Later, the proposed encoding schemes exploit the properties of compressed data to enhance the test data compression. This multistage compression is effective especially when the percentage of do not cares in a test set is very high. We also present the simple decoder architecture to decode the original data. The experimental results obtained from ISCAS'89 benchmark circuits confirm the average compression ratio of 74.2% and 77.5% with the proposed 9C-AFDER and 9C-RLHC schemes respectively.     

Low-power selective pattern compression for scan-based test applications

The ever-increasing test data volume and test power consumption are the two major issues in testing of digital integrated circuits. This paper presents an efficient technique to reduce test data volume and test power simultaneously. The pre-generated test sets are divided into two groups based on the number of unspecified bits in each test set. Test compression procedure is applied only to the group of test sets which contain more unspecified bits and the power reduction technique is applied to the remaining test sets. In the proposed approach, the unspecified bits in the pre-generated test sets are selectively mapped with 0s or 1s based on their effectiveness in reducing the test data volume and power consumptions. We also present a simple decoder architecture for on-chip decompression. Experimental results on ISCAS’89 benchmark circuits demonstrate the effectiveness of the proposed technique compared with other test-independent compression techniques.     

Machine learning and statistical models for predicting indoor air quality

Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario‐based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed. Thirty‐seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM_2.5 and PM₁₀) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees.     

A Stackelberg game theoretical approach for demand response in smart grid

Smart grids enable greater customer participation by which the customers are encouraged to reshape their daily energy consumption pattern for the financial