Secure Cluster-based Routing using TCSA and Hybrid Security Algorithm for WSN

Wireless Sensor Network (WSN) is operated as a medium to connect the physical and information network of Internet-of-Things (IoT). Energy and trust are two key factors that assist reliable communication over the WSN-IoT. Secure data transmission is considered a challenging task during multipath routing over the WSN-IoT. To address the aforementioned issue, secure routing is developed over the WSN-IoT. In this paper, the Trust-based Crow Search Algorithm (TCSA) is developed to identify the Secure Cluster Heads (SCHs) and secure paths over the network. Further, data security while broadcasting the data packets is enhanced by developing the Hybrid Security Algorithm (HSA). This HSA is a combination of the Advanced Encryption Standard (AES) and Hill Cipher Algorithm (HCA). Therefore, the developed TCSA-HSA avoids malicious nodes during communication which helps to improve data delivery and energy consumption. The performance of the TCSA-HSA method is analyzed using Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, End to End Delay (EED), and throughput. The existing methods namely Optimal Privacy-Multihop Dynamic Clustering Routing Protocol (OP-MDCRP) and Secure and Energy-aware Heuristic-based Routing (SEHR) are used to evaluate the TCSA-HSA performances. The PDR of TCSA-HSA for 100 nodes is 99.7449%, which is high when compared to the OP-MDCRP and SEHR

Music Recommendation System with User-based and Item-based Collaborative Filtering Technique

Internet and E-commerce are the generators of abundant of data, causing information Overloading.  The problem of information overloading is addressed by Recommendation Systems (RS). RS can provide suggestions about a new product, movie or music etc. This paper is about Music Recommendation System, which will recommend songs to users based on their past history i.e. taste. In this paper we proposed a collaborative filtering technique based on users and items. First user-item rating matrix is used to form user clusters and item clusters. Next these clusters are used to find the most similar user cluster or most similar item cluster to a target user. Finally songs are recommended from the most similar user and item clusters. The proposed algorithm is implemented on the benchmark dataset Last.fm. Results show that the performance of proposed method is better than the most popular baseline method

Method development and validation of aspirin and clopidogrel pharmaceutical dosage forms by developing new RP HPLC method

A simple and selective LC method is described for the determination of Aspirin and Clopidogrel in tablet dosage forms. Chromatographic separation was achieved on a c18 column along with mobile phase consisting of a combination of fifty five volumes of Mixed Phosphate Buffer and forty five volumes of Acetonitrile with detection of 235 nm. Linearity was observed in the range 20-60 μg/ml for Aspirin (r2=0.998) and 10-30 μg /ml for Clopidogrel (r2 =0.998) for the amount of drugs estimated by the projected ways was in smart agreement with the label claim. The proposed methods were validated. The accuracy of the methods was assessed by recovery studies at three different levels. Recovery experiments indicated the absence of interference from commonly encountered pharmaceutical additives. The method was found to be precise as indicated by the repeatability analysis, showing %RSD trials. All statistical data proves validity of the ways and may be used for routine analysis of pharmaceutical dosage form

Role of Association Rule Mining in DNA Microarray Data - A Research

Gene expression data analysis with DNA microarray technology has become a fundamental tool in genomic research. DNA Microarray technology monitors the gene expression levels of thousands of genes at a time under a certain condition makes them suit-able for a quite lot of biological applications. As the Association rules are widely using in market-basket analysis, these rules can also be applied in biological problems. Association rules play an important role in the computational biology. In this paper, how the Association rule mining is important for micro array gene association analysis have been discussed. Though the clustering offers a natural solution to these bio-logical problems by discovering frequent item sets on microarray data and reviewed the different methodologies discussed by the authors on micro array gene expressed data

A novel RP – HPLC methodology for method development and validation of aceclofenac and tizanidine pharmaceutical dosage forms

A simple and selective LC technique is chosen for the determination of Aceclofenac and Tizanidine in pill indefinite quantity forms. Chromatographic process separation was achieved on a c18 column victimization mobile part consisting of a combination of fifty volumes of Triethylamine buffer, fifty volumes of acetonitrile with detection of 230nm. Dimensionality was discovered within the vary 5-15 µg/ml for aceclofenac (r2 =0.999) and 1-3 µg /ml for tizanidine (r2 =0.998) for the number of medicine calculable by the planned strategies was in smart agreement with the label claim. The planned strategies have a sound procedure. At three completely different levels the accuracy of the strategies was assessed by recovery studies. The recovery experiments indicated the absence of interference from unremarkably encountered pharmaceutical additives showing %RSD below a pair of this technique was found to be precise as indicated by the repeatability analysis. All applied mathematics information proves all ways have valid procedure and might be used for routine analysis of pharmaceutical dose kin

Evaluation of lung function among the Indian elite female weightlifters

Background: Spirometry is an essential tool to evaluate lung function of health and disease. Adaptability of lung and chest among athletes can be assessed by lung function test (LFT). The quest of our study was to evaluate the lung function (LF) of highly trained Indian female weighting athletes, and intended to appraise the adaptation of LF among trained elite athletes.Methods: Top ranked Indian female professional weightlifters (study group, n=6) were recruited for this study. Three out of the six weightlifters were from top ten world ranking of 6th, 7th and 9th. Age matched controls (control group, n=6) were selected for this study. Maximum voluntary ventilation (MVV), vital capacity (VC), forced vital capacity (FVC), percentage of forced expiratory volume in first second (FEV1%) and ratio of forced expiratory volume in first second and forced vital capacity (FEV1/FVC%) have been evaluated as per the ATS/ERS guidelines.Results: Statistically higher significant values of VC and FVC were noted in study group, where as other values (MVV, FEV1% and FEV1/FVC%) found no significant difference between two groups.Conclusions: Power, strength and explosiveness of the skeletal muscles are vital domains in weightlifting sport. Weightlifting is such a sport doesn’t require much ventilatory efforts during training as well as competition. This study clueing that physiological adaptation/ improvement of the pulmonary function (PF) depends on the type of the sport being engaged by the athletes

Analysis of Behavioral Characteristics of Jammers to Detect Malicious Nodes in Mobile ADHOC Networks

Wireless ADHOC Networks are used to establish a wireless connection between two computing devices without the need for a Wi-Fi access point or router. This network is decentralized and uses omnidirectional communication media, which makes it more vulnerable to certain types of attacks compared to wired networks. Jamming attacks, a subset of denial-of-service (DoS) attacks, involve malicious nodes that intentionally interfere with the network, blocking legitimate communication. To address this issue, the proposed method analyzes various characteristics of nodes, such as packets sent, received, and dropped, at each node. Using the packet delivery ratio and packet drop ratio, the method detects jamming nodes from normal nodes, improving network performance. The network is simulated in NS2 environment

MBDS: MESSAGE AUTHENTICATION CODE (MAC) BASED BLACK HOLE DETECTION SYSTEM INMANET’S

Mobile ad hoc network is a collection of independent mobile nodes that can communicate to each other via radio waves. The mobile nodes can directly communicate to those nodes that are in radio range of each other, whereas others nodes need the help of intermediate nodes to route their packets. These networks are fully distributed, and can work at any place without the aid of any infrastructure. This property makes these networks highly robust.  Security is a major challenge for these networks due to their features of open medium, dynamically changing topologies. The black hole attack is a well known security threat in mobile ad hoc networks. However, it spuriously replies for any route request without having any active route to the specified destination. Sometimes the Black Hole Nodes cooperate with each other with the aim of dropping packets these are known as Cooperative Black Hole attack. This research work suggests the modification of Ad Hoc on Demand Distance Vector Routing Protocol. we are going to use a mechanism for detecting as well as defending against a cooperative black hole attack. This work suggest Maintenance of Routing Information Table and Reliability checking of a node with MAC address and Sequence Number. This system also decreases the end to end delay and Routing overhead

Protecting Girls from Harassment and Fraudulent Calls: A Voice-to-Text Approach

The rise in harassment calls and fraud, particularly targeting girls, has resulted in adverse consequences including psychological distress and, in extreme cases, suicide. Furthermore, fraudulent calls urging individuals to click on malicious links have led to substantial financial losses. This study presents a comprehensive approach to address this challenge for the development of an innovative detection system. Additionally, we introduce a novel prototype that employs a voice-to-text approach to transcribe phone calls, utilizing Natural Language Processing (NLP) techniques as well as Machine Learning (ML) algorithms to identify harassment or fraud-related content. When a malicious call is detected, the system automatically alerts parents or guardians and the nearest police station to prevent tragic outcomes such as suicides among targeted girls and financial fraud. By focusing on both preventive measures and advanced detection, this integrated approach aims to promote a safer communication environment and a more inclusive society

RNA–protein binding kinetics in an automated microfluidic reactor

Microfluidic chips can automate biochemical assays on the nanoliter scale, which is of considerable utility for RNA–protein binding reactions that would otherwise require large quantities of proteins. Unfortunately, complex reactions involving multiple reactants cannot be prepared in current microfluidic mixer designs, nor is investigation of long-time scale reactions possible. Here, a microfluidic ‘Riboreactor’ has been designed and constructed to facilitate the study of kinetics of RNA–protein complex formation over long time scales. With computer automation, the reactor can prepare binding reactions from any combination of eight reagents, and is optimized to monitor long reaction times. By integrating a two-photon microscope into the microfluidic platform, 5-nl reactions can be observed for longer than 1000 s with single-molecule sensitivity and negligible photobleaching. Using the Riboreactor, RNA–protein binding reactions with a fragment of the bacterial 30S ribosome were prepared in a fully automated fashion and binding rates were consistent with rates obtained from conventional assays. The microfluidic chip successfully combines automation, low sample consumption, ultra-sensitive fluorescence detection and a high degree of reproducibility. The chip should be able to probe complex reaction networks describing the assembly of large multicomponent RNPs such as the ribosome