A New Efficient Method for the Detection of Intrusion in 5G and beyond Networks using ML

60-65The 5G networks are very important to support complex application by connecting different types of machines and devices, which provide the platform for different spoofing attacks. Traditional physical layer and cryptography authentication methods are facing problems in dynamic complex environment, including less reliability, security overhead also problem in predefined authentication system, giving protection and learn about time-varying attributes. In this paper, intrusion detection framework has been designed using various machine learning methods with the help of physical layer attributes and to provide more efficient system to increase the security. Machine learning methods for the intelligent intrusion detection are introduced, especially for supervised and non-supervised methods. Our machine learning based intelligent intrusion detection technique for the 5G and beyond networks is evaluated in terms of recall, precision, accuracy and f-value are validated for unpredictable dynamics and unknown conditions of networks

INT-Hi-C reveals distinct chromatin architecture in endosperm and leaf tissues of Arabidopsis

Higher-order chromatin structure undergoes striking changes in response to various developmental and environmental signals, causing distinct cell types to adopt specific chromatin organization. High throughput chromatin conformation capture (Hi-C) allows studying higher-order chromatin structure; however, this technique requires substantial amounts of starting material, which has limited the establishment of cell type-specific higher-order chromatin structure in plants. To overcome this limitation, we established a protocol that is applicable to a limited amount of nuclei by combining the INTACT (isolation of nuclei tagged in specific cell types) method and Hi-C (INT-Hi-C). Using this INT-Hi-C protocol, we generated Hi-C data from INTACT purified endosperm and leaf nuclei. Our INT-Hi-C data from leaf accurately reiterated chromatin interaction patterns derived from conventional leaf Hi-C data. We found that the higher-order chromatin organization of mixed leaf tissues and endosperm differs and that DNA methylation and repressive histone marks positively correlate with the chromatin compaction level. We furthermore found that self-looped interacting genes have increased expression in leaves and endosperm and that interacting intergenic regions negatively impact on gene expression in the endosperm. Last, we identified several imprinted genes involved in long-range and trans interactions exclusively in endosperm. Our study provides evidence that the endosperm adopts a distinct higher-order chromatin structure that differs from other cell types in plants and that chromatin interactions influence transcriptional activity

Genetic divergence in brinjal (Solanum melongena L.)

A study of genetic divergence in 40 brinjal (Solanum melongena L.) genotypes for various characters to study the diversity based on qualitative and quantitative characters. Significant variations were observed among the brinjal lines for all the parameters under study. Based on D2 values, the accessions were grouped into seven clusters. Average intra- and inter-cluster D2 values among 40 genotypes revealed that cluster II showed a minimum intra-cluster value of 3.793, indicating that the genotypes within this cluster were similar, while the cluster I showed maximum intra-cluster D2 value (4.681) revealing the existence of diverse genotypes in these clusters. The inter-cluster D2 values ranged from 4.657 to 7.174. The minimum inter-cluster D2 value was observed between cluster III and IV (4.657), indicating the close relationship among the genotypes included in these clusters. The maximum inter-cluster value was observed between cluster V and II (7.174), indicating that the genotypes included in these clusters had maximum divergence. Hence, hybridization between the genotypes included in these different clusters may give high heterotic responses and thus better segrigants are greatly suggested for selection and improvement of brinjal crop with good consumer preference and high fruit yield

Effect of spacing, fertilizers and varieties on growth and yield parameters of okra (Abelmoschus esculantus (L.) Moench)

The experiment was conducted during spring summer seasons of 2013 and 2014 conducted at Research Farm of Vegetable Science, CCS HAU, in summer season. There were three spacing, three fertilizer levels and two varieties in split-split plot design with three replications. Growth parameters of okra crop were significantly affectedby spacing, fertilizer and varieties. Highest plants were observed in wider spacing with fertilizer application of 187.5 kg N + 75kg P2O5 + 60 kg K2O per hectare in variety HBT-49-1. However, numbers of branches were highest in variety Hisar Unnat. Yield attributes like first fruiting node, intermodal length, fruit length and diameter etc. were highest in variety HBT-49-1 resulting in highest fruit yield (q/ha) in spacing 30 cm x 10 cm with the application of 187.5 kg N+75 kg P2O5 + 60 kg K2O per hectare. The seed yield attributes and yield was significantly affected by spacing, fertilizer and varieties. Finally, spacing 30 cm × 10 cm resulted in higher growth parameters, yield attributes and yield with the application of 187.5 kg N +75 kg P2O5 + 60 K2O in variety HBT-49-1 of okra

A New Improved Approach for Feature Generation and Selection in Multi-Relational Statistical Modelling using ML

1095-1100Multi-relational classification is highly challengeable task in data mining, because so much data in our world is organised in multiple relations. The challenge comes from the huge collection of search spaces and high calculation cost arises in the selection of feature due to excessive complexity in the various relations. The state-of-the-art approach is based on clusters and inductive logical programming to retrieve important features and derived hypothesis. However, those techniques are very slow and unable to create enough data and information to produce efficient classifiers. In the given paper, we proposed a fast and effective method for the feature selection using multi-relational classification. Moreover we introduced the natural join and SVM based feature selection in multi-relation statistical learning. The performance of our model on various datasets indicates that our model is efficient, reliable and highly accurate

A New Approach for Movie Recommender System using K-means Clustering and PCA

Recommendation systems are refining mechanism to envisagethe ratings for itemsand users, to recommend likes mainly from the big data. Our proposed recommendationsystem gives a mechanism to users to classify with the same interest. This recommendersystem becomes core to recommend the e-commerce and various websites applications basedon similar likes. This central idea of our work is to develop movie recommender system withthe help of clustering using K-means clustering technique and data pre-processing usingPrincipal Component Analysis (PCA). In this proposed work, new recommendationtechnique has been presented using K-means clustering, PCA and sampling with the help ofMovieLens dataset. Our proposed method and its subsequent results have been discussed andcollation with other existing methods using evaluation metrics like Dunn Index, averagesimilarity and computational time has been also explained and prove that our technique isbest among other techniques. The results achieve from the MovieLens dataset is able to provehigh efficiency and accuracy of our proposed work. Our proposed method is able to achievethe MAE of .67, which is better than other methods

A New Approach for Movie Recommender System using K-means Clustering and PCA

159-165Recommendation systems are refining mechanism to envisage the ratings for items and users, to recommend likes mainly from the big data. Our proposed recommendation system gives a mechanism to users to classify with the same interest. This recommender system becomes core to recommend the e-commerce and various websites applications based on similar likes. This central idea of our work is to develop movie recommender system with the help of clustering using K-means clustering technique and data pre-processing using Principal Component Analysis (PCA). In this proposed work, new recommendation technique has been presented using K-means clustering, PCA and sampling with the help of MovieLens dataset. Our proposed method and its subsequent results have been discussed and collation with other existing methods using evaluation metrics like Dunn Index, average similarity and computational time has been also explained and prove that our technique is best among other techniques. The results achieve from the MovieLens dataset is able to prove high efficiency and accuracy of our proposed work. Our proposed method is able to achieve the MAE of 0.67, which is better than other methods

Application of Multi Criteria Decision Making tools in Selection of Concrete Mix

Nowadays decision making plays a major role in deciding the execution of any task. For this purpose, two widely known key tools are available to us. These include AHP as well as TOPSIS, both falls under MCDM tools. These techniques are now also brought in the field of civil engineering. Both techniques are used to analyze the results with the help of comparative data studies related to different concrete mixes. The study includes the analysis of results of compressive strength, split tensile strength and flexure strength and then validate the results obtained with AHP and TOPSIS techniques. These techniques will help in identification of best to worst concrete mix

Application of Multi Criteria Decision Making tools in Selection of Concrete Mix

304-309Now a day decision making plays a major role in deciding the execution of any task. Two key tools are available to serve the purpose of decision-making. These include AHP as well as TOPSIS, both falls under Multi Criteria Decision Making (MCDM) tools. These techniques are now also brought in the field of civil engineering. MCDM techniques are used in various applications of civil engineering. This paper presents comparison of AHP and TOPSIS for making final decisions for the best concrete mix with fibres of steel and basalt available with different proportions. The comparison is made on the tests of split tensile strength, compressive strength and flexure results. Results of the experiment are used to validate results of AHP and TOPSIS. Optimum hybrid mixes for mechanical properties is M-S0.5-B0 at 28 days

Correction to : Epigenetic signatures associated with imprinted paternally expressed genes in the Arabidopsis endosperm

Aquesta és una correcció a l'article 10.1186/s13059-019-1652-