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Nowadays, predicting the success of a new movie is a crucial task. In this work, the hybrid approach considers the movie features as well as sentiment expressed in the movie review to predict the success rate of a movie. Multiple movie features such as title, director, star cast, and writer. Are considered for prediction. The related raw data is collected from the internet movie database (IMDb) website and after pre-processing, the collected data is used to generate the supervised machine learning model. Different supervised learning models are compared and the one with the best results is used further. The mean squared error, root mean squared error and r2 score of the models generated are comparable with existing models. Further, sentiment analysis of the movie-related tweets is performed. The accuracy of best sentiment analysis model is 88.47%. Finally, the two models are combined to give the success prediction rating of new movies and the results of the hybrid model are encouraging. The proposed model may be used to find the top-rated movies of a particular calendar year.

The natural languages are medium of communication from the inception of civilization. As the technology improves, the text messages, voice messages and videos are the addons in medium of communication. In long distance communication, the analysis of expression is modern area of research. The parameters of assessment are subjective hence the emotion recognition is challenging task. This article furnishes the investigation of various machine learning techniques and novel methods for speech emotion recognition (SER) to determine the feeling/sentiments in a speech. Here, we investigate the three machine learning methods named multinominal Naive Bayes (MNB), logistic regression (LR), and linear support vector machine (LSVM). Further, these techniques are incorporated with the proposed method. The performance of these machine learning techniques is investigated on two different datasets. The datasets consist of voice and text data samples. The prosed method is trained and tested on these datasets. As per the experimentation, it has been observed that the LSVM has outperformed the other two machine learning techniques.

In today’s era, Digital watermarking is a well-known application in direction of proving the authenticity of digital content. In this concern, a new optimized image watermarking method has been sug...

Fluorescence methods have gained enormous attention due to their ease in use, simplicity, selectivity and sensitivity. Fluorescent chemosensors respond instantly by converting molecular recognition to fluorescent signals. The consumption of pharmaceutical products by living beings is on great increase. The disposal of such compounds in the environment is a matter of great concern as these compounds enter aquatic environment and show accumulation in tissues of aquatic organisms. In the present study, we have utilized naphthalimide based receptors to fabricate organic nanoparticles (ONPs). These ONPs were used for the development of hybrid nanoassemblies, and the developed nanoassemblies were characterized with the help of transmission electron microscopy and dynamic light scattering studies. The photophysical studies were performed and the hybrid assembly developed using receptor 2 demonstrated a turn-on fluorescence emission behavior on binding with Amitriptyline. The present sensing system acted as promi...

Sepsis is a severe infectious disease with high mortality, and it occurs when chemicals released in the bloodstream to fight an infection trigger inflammation throughout the body and it can cause a cascade of changes that damage multiple organ systems, leading them to fail, even resulting in death. In order to reduce the possibility of sepsis or infection antiseptics are used and process is known as antisepsis. Antiseptic peptides (ASPs) show properties similar to antigram-negative peptides, antigram-positive peptides and many more. Machine learning algorithms are useful in screening and identification of therapeutic peptides and thus provide initial filters or built confidence before using time consuming and laborious experimental approaches. In this study, various machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbour (KNN) and Logistic Regression (LR) were evaluated for prediction of ASPs. Moreover, the characteristics physicochemical features of ASPs were also explored to use them in machine learning. Both manual and automatic feature selection methodology was employed to achieve best performance of machine learning algorithms. A 5-fold cross validation and independent data set validation proved RF as the best model for prediction of ASPs. Our RF model showed an accuracy of 97%, Matthew’s Correlation Coefficient (MCC) of 0.93, which are indication of a robust and good model. To our knowledge this is the first attempt to build a machine learning classifier for prediction of ASPs.

With the dawn of the information age, the number of choices that each person has to make each day has seen an explosive growth. Every minute, users are bombarded with so many different options for everything user do, from buying clothes to watching TV, that one live in a state of constant information overload, feeling stressed out by this imposed free will then be liberated by it. One ways in which user have now become used to mitigating this constant barrage of incessant information is by the usage of Recommender Systems. These systems are software components that use some form of rating provided by a user to generate recommendations for items that they may be interested in. Unfortunately, recommender systems face a major problem called the “Cold – Start problem”, which essentially means that a recommender system in an online setting has no information about the user when the user first signs up. Many approaches have been proposed to solve this, but the one this paper shall be taking is to use information from social networks, specifically Twitter, to get this initial information. Implicitly infer user interest with good accuracy is proposed here and this understanding of interests can later be updated by observing user actions as they interact with the system. We leverage the power of the categorical information stored in the Wikipedia database to allow us to assign relative weights to entities that a user follows on Twitter. This proposed approach for creating the rating vector for each user by mining the Twitter account of the users’ is evaluated using the RMSE and MAE metrics.

Security of data has become a necessity in today’s world. Data as all know is the raw form and information can be extracted from it. Thus, data needs to be secured. This concern becomes even more significant when share information and it mostly constitute of images. As already know that a picture speaks a thousand words, there is no reason not to believe that picture carry the most information. Furthermore, they can be easily manipulated. This paper presents two schemes to secure such information in the form of images. Under the concept of (k, n) - Visual Cryptography, authors present these two schemes to secretly share images, that can be practically used to secure the information sent. The (3, 3) - enhanced visual cryptographic scheme, enhances security of the currently existing (3, 3) - visual cryptographic scheme by utilizing the concept of keys to secure information. Authors also introduce a new scheme, (2, 3) - visual cryptographic scheme. The (2, 3) - visual cryptographic scheme also utilizes the concepts of keys but reduces the bandwidth requirement or increases reliability. This provides us two modes of operation that are more practical than existing scheme. The paper shall discuss the scheme with a schematic analysis and an analysis of its implementation both subjectively and objectively. Using several parameters of security, this paper presents our findings as our results. The schemes discussed are found to be more secure than existing visual cryptographic scheme and are more reliable and imperceptible.

A rapid fluorescence method has been developed which describes the sensitive and selective determination of ketoprofen by developing hybrid assemblies composed of silver nanoparticles and organic nanoparticles. Three different ligands were synthesized using various thiourea derivatives and these were processed with organic nanoparticles using a single step re-precipitation method. Hybrid nanoassemblies were developed and consisted of silver nanoparticles and organic nanoparticles, respectively. These were further analysed for their response to various drug molecules and their fluorescence emission spectra were recorded. A non-selective response was observed for hybrid nanoassemblies composed of ethyl isothiocyanate and phenyl isothiocyanate. However, a turn-on fluorescence response to ketoprofen was noticed for the hybrid nanoassembly composed of 1-naphthyl isothiocyanate. Titrations were carried out revealing that with subsequent addition of ketoprofen, a gradual increase in fluorescence emission was noticed. Our sensor can effectively determine ketoprofen up to a detection limit of 34 nM. Moreover, other drug molecules were found to have negligible interference in the sensing of ketoprofen in aqueous media.

A library of 1H-1,2,3-triazole-tethered 4-aminoquinoline-benzoxaborole hybrids as well as aryl substituted benzoxaborole analogues was synthesized and screened for their anti-plasmodial efficacy against both chloroquine-susceptibility 3D7 and chloroquine-resistant W2 strains of P. falciparum. The inclusion of quinoline core among the synthesized analogues resulted in substantial enhancement of anti-plasmodial activities. Further, the spacer of a flexible alkyl chain is marginally preferred over piperazyl-ethyl in inhibiting growth of P. falciparum. The most potent 4-aminoquinoline-benzoxaborole conjugate with ethyl as spacer exhibited IC50 values of 4.15 and 3.78 μM against 3D7 CQ-susceptible and W2 CQ-resistant strains of P. falciparum with lower cross resistance with Chloroquine. There was no difference in anti-plasmodial activities between the CQ-susceptible 3D7 and CQ-resistant W2 strains of P. falciparum for the benzoxaborole derivatives lacking a quinoline core.

Plagiarism is the act of using someone else’s words or ideas without giving them due credit and representing it as one’s own work. In today's world, it is very easy to plagiarize others' work due to advancement in technology, especially by the use of the Internet or other offline sources such as books or magazines. Plagiarism can be classified into two broad categories on the basis of detection namely extrinsic and intrinsic plagiarism. Extrinsic plagiarism detection refers to detecting plagiarism in a document by comparing it against a given reference dataset, whereas, Intrinsic plagiarism detection refers to detecting plagiarism with the help of variation in writing styles without using any reference corpus. Although there are many approaches which can be adopted to detect extrinsic plagiarism, few are available for intrinsic plagiarism detection. In this paper, a simplified approach is proposed for developing an intrinsic plagiarism detector which is helpful in detecting plagiarism even when no reference corpus is available. The approach deals with development of an intrinsic plagiarism detection system by identifying the writing style of authors in the document using stylometric features and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. The proposed system has an easy to use interactive interface where user has to upload a text document to be checked for plagiarism and the result is displayed on the web page itself. In addition, the user can also see the analysis of the document in the form of graphs.

Healthcare is always a sensitive issue for all of us, and it will always remain. Predicting various types of health issues in advance can lead us to a better life. Various types of health problems are there like cancer, heart diseases, diabetes, arthritis, pneumonia, lungs disease, liver disease, and brain disease, which all are at high risk. To reduce the risk of health issues, some suitable models are needed for prediction. Thus, it became as a motivational factor for the authors to survey the existing literature on this topic thoroughly and have consequently to identify suitable machine learning techniques so that improvement can be possible while selecting a prediction model. In this chapter, concept of survey is used to provide the prediction models for healthcare issues along with the challenges associated with each model. This chapter will broadly cover the following: machine learning algorithms used in health industry, study various prediction models for Cancer, Heart diseases, Diabetes and Brain diseases, comparative study of various machine learning algorithms used for prediction.

The present paper describes the synthesis, anti-plasmodial, and cytotoxic evaluation of 7-chloroquinoline-based conjugates with isatins/indoles/ nitroimidazoles, obtained via Cu-promoted 1,3-dipolar cycloadditions. On contemplating SAR of the synthesized series, the inclusion of indole and nitroimidazole-core improved the anti-plasmodial activities while the isatin seemed to have a lesser effect. The conjugate with a nitroimidazole-core and hexyl chain length as a spacer between the two pharmacophores was found to be most potent among the synthesized series and displayed an IC50 of 0.12 µM and a selectivity index of 1748.

Iron-catalyzed hydrosilylation of imines to amines using a well-defined iron complex is reported. This method employs relatively mild conditions, by reaction of imine, (EtO)3SiH in a 1 : 2 ratio in the presence of 1 mol% precatalyst ([BIAN]Fe(η6-toluene), 3, BIAN = bis(2,6-diisopropylaniline)acenaphthene) at 70 °C. A broad scope of imines was readily converted into the corresponding secondary amines without the need for precatalyst activators.

In the present study, 1,8-naphthalimide based derivatives were synthesized for the fluorescence determination of Phenylbutazone in an aqueous medium. The synthesized compounds were characterized using various spectroscopic techniques. Fluorescent organic nanoparticles N4 developed from compound 4 were further evaluated for their sensing activity towards various drug molecules. The photophysical studies of N4 exhibited selective behaviour towards Phenylbutazone with increment in the fluorescence emission profile; however no such alteration was noticed for other drug molecules. To gain more insight towards the recognition behaviour, titrations were performed and the results elucidated the linear binding towards Phenylbutazone. A series of competitive experiments showed that the present sensor selectively recognized Phenylbutazone among competing drug molecules. The sensor can detect Phenylbutazone efficiently upto a detection limit of 20 nM. The real sample analysis for the present work has also been performed and the results reveal that our sensor can efficiently determine Phenylbutazone in commercially available tablets.

The present work depicts the development of a probe based on the organic-inorganic framework that sensitively and selectively recognized furosemide in aqueous medium. An organic receptor 1 is prepared using 1, 8-naphthalic anhydride and diethylenetriamine and the product are further reacted with 1-naphthylisothiocyanate. The receptor is processed into fluorescent organic nanoparticles (FONs); unfortunately, these FONs have not shown any promising sensor activity. The FONs are used as platform for the attachment of gold nanoparticles and the resultant hybrid nanoparticles (H1) are characterized. Hybrid nanoparticles are evaluated for their photophysical studies with fluorescence spectroscopy in aqueous medium and the studies revealed that hybrid nanoparticles selectively recognize furosemide in aqueous medium. The sensor has shown competitive binding for furosemide and no other tested drug has posed any interference in the detection of furosemide. The titrations are performed with furosemide and the lowest detection limit calculated is 551 nM.

Today every big company, like Google, Flipkart, Yahoo, Amazon etc., is dealing with the Big Data. This big data can be used to predict the recommendation for the user on the basis of their past behavior. Recommendation systems are used to provide the recommendation to the users. The author presents an overview of various types of recommendation systems and how these systems give recommendation by using various approaches of Collaborative Filtering. Various research works that employ collaborative filtering for recommendations systems are reviewed and classified by the authors. Finally, this chapter focuses on the framework of recommendation system of big data along with the detailed survey on the use of the Big Data mining in collaborative filtering.

Background: Masking dilemma is a condition prevalent whenever there is significant hearing loss in non-test ear and conductive hearing loss in test ear resulting into narrow or non-existent plateau width. Three feasible ways are divulged to circumvent this condition namely, Sensorineural acuity level testing, Fusion Inferred at threshold and use of insert type transducers. Different circumstances encountered when using insert type transducers and Fusion Inferred at threshold test has made it peril to use. Thus, administration of sensorineural acuity level test has been preferred. Sensorineural acuity level test involves computation of individual threshold shifts and its comparison with normal threshold shifts when a maximum level of bone conducted noise is presented at the centre of forehead via bone conduction vibrator. This research study aims to document normal threshold shifts and its validation in individual with bilateral symmetrical sensorineural hearing loss.Methods: A prospective study was carried out among 50 normal individuals and 50 patients with bilateral symmetrical sensorineural hearing loss. Pertinent measures were taken to ensure fulfilment of all-inclusive and exclusive criteria. In addition, air conduction and bone conduction threshold were computed using conventional methods followed by administration of sensorineural acuity level test. Necessary comparison was made among the threshold shifts. Results: Inferences drawn showed closer correlation between sensorineural acuity level threshold and bone conduction thresholds during puretone audiometry.Conclusions: Sensorineural acuity level test has shown to be effectual in both rehabilitation and medical interventions. Besides, imperative role of an audiologist can be recognized in its administration and elucidation for better prognostication.

In today’s world internet is the answer to every question. So at any time one can easily copy the content from web and use it. This is known as plagiarism .It is growing now days. Usually in plagiarism people reword the documents, copy them, do not give references. It is difficult to detect plagiarism as people rephrase the text do not copy it directly. To detect plagiarism apache lucene have been used. Firstly indexing of the original document is done and then used cosine similarity to compare the plagiarised document with set of documents which are there saved previously.

The use of fluorescent organic nanoparticles for the determination of metal ions is highly significant. Among other analytical techniques, fluorescence spectroscopy finds significance due to high sensitivity, selectivity and ease of operation. A ligand 1 was synthesized using 1,8-naphthalic anhydride and triethylenetetramine and it was further processed into organic nanoparticles that displayed excellent sensing properties for selective detection of Cr3+ ion in aqueous medium using fluorescence spectroscopy. The sensor for Cr3+ ion showed competitive binding as no other metal ions caused interference in detection of Cr3+ ion. To check the efficacy of the sensor, titrations were performed with addition of Cr3+ ion in small aliquots and it efficiently detects Cr3+ ion with a detection limit of 64 nM. The mechanism behind the Cr3+ ion detection was studied using DFT calculations, which showed that chromium gets electrons from the nitrogen part of triethylenetetramine which resulted in inhibition of photoinduced electron transfer (PET) that led to an increase in the fluorescence profile.

A dipodal naphthalimide based receptor 1 was synthesized, characterized and processed into organic nanoparticles ( N1 ) using single step re-precipitation technique. These organic nanoparticles were further used as a platform for the development of hybrid organic–inorganic framework ( H1 ). Hybrid nanoparticles thus formed were evaluated for their photo-physical behavior and their interaction with various available drugs using fluorescence spectroscopic technique and found it selective in determination of amoxicillin in aqueous medium. Competitive binding studies further confirmed the selectivity of H1 for amoxicillin, as no interference was perceived with any other drug in determination of amoxicillin. H1 was able to determine amoxicillin quite efficiently in commercially available tablets of the drug with a lowest detection limit of 236 nM.

Iron-based catalysis is an increasingly studied area of research, as is evident from the ever-increasing number of iron catalyzed chemical transformations developed over the past two decades. Well defined pincer based iron complexes are of interest, due to their thermal stability and the ability to fine tune (via synthesis) the electronic and steric properties of the metal complex. This chapter overviews the general pincer ligand framework, with detailed discussion of two of the most widely used ligand classes: 2,6-disubstituted pyridine and 1,3-disubstituted benzene. Discussion will include both ligand and complex synthesis and subsequent catalytic applications.

1H-1,2,3-triazole linked 4-aminoquinoline-chalcone/-N-acetylpyrazoline conjugates were synthesized and evaluated against cultured chloroquine (CQ) resistant strain. Antiplasmodial activities of the synthesized conjugates revealed dependence of activity on the length of the alkyl chain as well as on the presence of methoxy substituents on ring A/ring B of the chalcone. The most potent and non-cytotoxic conjugate showed comparable antiplasmodial activity with that of CQ, with an IC50 value of 53.7 nM.

Receptor 1 based on rhodamine dye was designed and characterized using 1H, 13C NMR and mass spectroscopy like spectroscopic techniques. Re-precipitation methods have been used for developing nano-aggregates of 1 (N1) in aqueous medium, which were further used for preparation of inorganic–organic hybrid nanomaterial (H1) i.e. by coating organic nanoparticles with gold nanoparticles. Changes in the photophysical profile by anion binding of N1 and H1 were evaluated using fluorescence spectroscopy. No selectivity was perceived with nano-aggregates (N1) for any anion. However, the hybrid nanomaterial (H1) selectively recognized HSO4− through enhancement of fluorescence emission intensity in aqueous medium. Thus, the hybrid nanomaterial (H1) has been established for sensing of HSO4− and was considered for various parameters like rapid response, good stability in water and is validated for determination of HSO4− ions in daily use items.

The sensors have been developed using silver nanoparticles coated with organic ligands and are fully characterized with spectroscopic methods. The energy-dispersive X-ray (EDX) analysis revealed the presence of organic receptors on the surface of metal nanoparticles. These chemosensors were tested against a range of biological and environmentally relevant cations in the HEPES buffered DMSO/H2O (8:2, v/v) solvent system. The fluorescence intensity of these chemosensors was quenched upon coordination with open shell metal ions such as Cu2+/Fe3+. Anion recognition properties of the corresponding metal complexes have been studied and the original fluorescence intensity of sensors was restored upon addition of phosphate (0–20 µM). Thus, a highly selective chemosensor has been devised for the micromolar estimation of phosphate in semi-aqueous medium.

The exponential growth in the amount of data gathered from various sources has resulted in the need for more efficient algorithms to quickly analyze large datasets. Clustering techniques, like K-Means are useful in analyzing data in a parallel fashion. K-Means largely depends upon a proper initialization to produce optimal results. K-means++ initialization algorithm provides solution based on providing an initial set of centres to the K-Means algorithm. However, its inherent sequential nature makes it suffer from various limitations when applied to large datasets. For instance, it makes k iterations to find k centres. In this paper, we present an algorithm that attempts to overcome the drawbacks of previous algorithms. Our work provides a method to select a good initial seeding in less time, facilitating fast and accurate cluster analysis over large datasets.

The manuscript describes the synthesis of novel amide tethered 7-chloroquinoline–chalcone and 7-chloroquinoline–ferrocenylchalcone bifunctional hybrids and their evaluation as antimalarial agents against W2 resistant strain of Plasmodium falciparum . The antiplasmodial activity of 7-chloroquinoline–ferrocenylchalcones was found to be less than their corresponding simple chalcone conjugates. The presence of a methoxy substituent at para position of ring B on chalcones and longer alkyl chain length markedly improved the antiplasmodial profiles of the synthesized scaffolds with the most potent of the test compound exhibiting an IC 50 value of 17.8 nM.

WSDL is used to describe the interface of a service, in XML format. The interface describes the functional properties as well as non functional properties. We are concerned with specifying ‘criteria’ as a non functional property of a web service. For this we have extend WSDL to X-WSDL. In order to add criteria information we extend the WSDL (Web Service Definition Language) schema by adding a new element ‘criteriaservice’ this is available in the new namespace. Using this ‘criteriaservcie’ element it is possible to specify the criteria along with a service in an X-WSDL document. The WSDL document is also extended by adding new attributes ‘criteria name’ and ‘description’ to service element. Using this extension it is possible to specify the criteria along with the service in X-WSDL document. The criteria are specified by the user when invoking a service. As a result, we are providing support to discover a more appropriate service according to his/her requirement.

An organic compound 1 was prepared using 1,8-naphthalimide and diethylenetriamine, and the resultant receptor showed interesting potential for metal recognition. The chemosensor activity of the naphthalimide-based receptor was evaluated following the synthesis of the organic nanoparticles (ONPs) using a reprecipitation method. The organic nanoparticles were evaluated for their photophysical properties, mainly using fluorescence spectroscopy in aqueous media, and the studies revealed that the ONPs selectively recognized aluminium ions in aqueous media. The sensor showed competitive binding for Al3+ and none of the other metal ions tested caused any interference in the detection of Al3+. Titrations were performed with aluminium ions, and the lowest detection limit calculated was 677 nM. The sensor was used to determine the Al3+ content of river water and acidic soils; results were validated using existing methods and the performance of the ONPs was found to be in a satisfactory error range. Finally, the mechanism of fluorescence recognition was further confirmed using DFT calculations, which showed that the aluminium, upon addition to receptor 1, takes away the excess electrons i.e. a lone pair from the central nitrogen of the diethylenetriamine part of receptor 1. This causes a reduction in photo-induced electron transfer (PET) and an increase in fluorescence intensity, which was authenticated using NMR and UV-Visible spectroscopic titrations of receptor 1 with Al3+ ions.