Generalization of Picture Fuzzy Matrix

In this paper, we introduce the concept of k-regular Picture Fuzzy Matrix (PiFuM) as a generalization of regular matrix and investigate some basic properties of a k-Regular Picture Fuzzy Matrix (k-RPiFuM). Moreover we analyze the characterization of a matrix for which the regularity index and the index are identical. Furthermore the relation between regular, k-regular and regularity of powers of picture fuzzy matrices are discussed.Publisher's Versio

A Review on Skin Disease Classification and Detection Using Deep Learning Techniques

Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches

Rank Maximal Matchings -- Structure and Algorithms

Let G = (A U P, E) be a bipartite graph where A denotes a set of agents, P denotes a set of posts and ranks on the edges denote preferences of the agents over posts. A matching M in G is rank-maximal if it matches the maximum number of applicants to their top-rank post, subject to this, the maximum number of applicants to their second rank post and so on. In this paper, we develop a switching graph characterization of rank-maximal matchings, which is a useful tool that encodes all rank-maximal matchings in an instance. The characterization leads to simple and efficient algorithms for several interesting problems. In particular, we give an efficient algorithm to compute the set of rank-maximal pairs in an instance. We show that the problem of counting the number of rank-maximal matchings is #P-Complete and also give an FPRAS for the problem. Finally, we consider the problem of deciding whether a rank-maximal matching is popular among all the rank-maximal matchings in a given instance, and give an efficient algorithm for the problem

टूटीकोरिन जिले की मोलस्कन संपदाएं

टूटीकोरिन जिले की मोलस्कन संपदाए

Success stories of model sea-cage farm Sippikulam fishervillage, Thoothukudi district

Success stories of model sea-cage farm Sippikulam fishervillage, Thoothukudi distric

Marine biotoxins and its detection

The incidences of intoxication due to the consumption of marine foods have been increasing in recent years. This is due to the presence of biotoxins in foods of marine origin. The biotoxins will be accumulated in the marine foods due to the consumption of toxic biota of marine origin. When this contaminated food is taken by the humans or animals, those toxins will be transferred to them causing intoxication and lethality. Among these intoxications, most of them are caused by the harmful algal blooms (HAB). In order to avoid the harmful effects from marine biotoxins, it is necessary to have the proper knowledge. In this manuscript, the different types of biotoxins, source of intoxication, characteristics of toxins, detection and control measures are discussed in detail. Key words: Harmful algal blooms, harmful algal blooms (HAB), ciguatara fish poisoning (CFP), paralytic shellfish poisoning (PSP), diarrhetic shellfish poisoning (DSP) blooming, detection

Protein profiling for phylogenetic relationship in snakehead species

Protein banding pattern of eight snakeheads – Channa species viz., Channa striatus, Channa marulius, Channa punctatus, Channa diplogramme, Channa bleheri, Channa gachua, Channa stewartii and Channa aurantimaculata collected from different regions of India were used to study the phylogenetic relationship among them. The banding pattern from muscle protein indicated a unique profile for each species and the electrophoregrams showed similarities among the species studied. In the SDS-PAGE, a maximum of 12 protein bands were obtained for C. gachua followed by 11 for C. diplogramme and 10 for C. marulius whereas less number of bands were recorded for the remaining species. Molecular weight of the protein bands varied from 16 kDa - 232 kDa. UPGMA (Unweighted Pair Group Method with Arithmetic Mean) dendrogram revealed that the phylogenetic relationship was very close among C. aurantimaculata and C. bleheri and also between C. gachua and C. stewarti

Approximate Solutions To Constrained Risk-Sensitive Markov Decision Processes

This paper considers the problem of finding near-optimal Markovian randomized (MR) policies for finite-state-action, infinite-horizon, constrained risk-sensitive Markov decision processes (CRSMDPs). Constraints are in the form of standard expected discounted cost functions as well as expected risk-sensitive discounted cost functions over finite and infinite horizons. The main contribution is to show that the problem possesses a solution if it is feasible, and to provide two methods for finding an approximate solution in the form of an ultimately stationary (US) MR policy. The latter is achieved through two approximating finite-horizon CRSMDPs which are constructed from the original CRSMDP by time-truncating the original objective and constraint cost functions, and suitably perturbing the constraint upper bounds. The first approximation gives a US policy which is $\epsilon$-optimal and feasible for the original problem, while the second approximation gives a near-optimal US policy whose violation of the original constraints is bounded above by a specified $\epsilon$. A key step in the proofs is an appropriate choice of a metric that makes the set of infinite-horizon MR policies and the feasible regions of the three CRSMDPs compact, and the objective and constraint functions continuous. A linear-programming-based formulation for solving the approximating finite-horizon CRSMDPs is also given.Comment: 38 page

Copepod Abundance and Diversity from Offshore Region of Tuticorin, South East Coast of India

A detailed study had been carried out on species abundance, biomass and composition of copepod in four different offshore stations namely, Station I: Vembar, II: Keelavaipar, III: Punnaikayal and IV: Thiruchendhur in Gulf of Mannar region from October 2011 to April 2012. A total of 56 copepod species belongs to 20 families under 4 orders have been encountered during the period. The percentage composition of different groups of copepod species was composed of Calanoida (35 numbers) 62.5%, Cyclopoida (4 numbers) 7.14%, Harpacticoida (8 numbers) 14.3% and Poecilostomatoida (9 numbers) 16.1%. The percentage of biomass composition of different groups of copepods during the study was in the order of Calanoida 38.99%, Harpacticoida 32.56%, Cyclopoida 15.22% and Poecilostomatoida 13.23%. In the case of species composition, Euterpina acutifrons (28.61%) was the most abundant species followed by Acrocalanus gracilis (17.68%), Corycaeus crassiusculus (12.33%), Oithona brevicornis (12.03%) and Temora turbinata (4.25%) were the other dominant species in observation. The copepod density in different stations were in the range of 8600–39900, 3900–64600, 3800–24800 and 5000–22500 numbers m-3 at station I, II, II and IV respectively. The lowest biomass of copepod was observed at station III and highest biomass was found at station II. The copepod species richness ranged from 0.48 to 2.72 and species diversity was in the range of 0.87 to 1.98 in the study areas. Species evenness was varied from 0.24 – 0.51 during the observation period