Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Segmentation Techniques in Digital Image Based Identification of Ethiopian Paper Currency

Paper and coin are the two most common currencies in all over the world. In Ethiopia also paper and coin currency are used for medium of exchange. This paper presents the comparative study of segmentation techniques towards Ethiopian paper currency classification. Otsu, FCM and K-means segmentation techniques are considered for this study and BPNN is used for classification of currencies. For the classification, images are collected from commercial bank of Ethiopia and Dashen Bank; for our data set, a total of 500 images samples were collected. From these images, 91.2% accuracy is achieved when Otsu segmentation is used on BPNN with TANH learning function.
DESIGN, SIMULATION AND REAL-TIME IMPLEMENTATION OF A MAXIMUM POWER POINT TRACKER FOR PHOTOVOLTAIC SYSTEM

Maximization of power from a solar photovoltaic (PV) module is of special interest as the efficiency of the solar PV system is very low. A maximum power point tracker (MPPT) is needed to operate the PV array at its maximum power point. The present work describes the design and implementation of a MPPT for the solar PV array connected to a resistive load. The design consists of a dc-dc boost converter (also known as step up converter) and a control section that uses an ATmega32 microcontroller. The control section obtains the information from the PV array through microcontroller’s Analog and Digital (A/D) ports and hence performs the pulse width modulation (PWM). The incremental conductance method with some modification is used as an algorithm to track the maximum power point of the PV array. By using this algorithm the MPPT is able to track the maximum power point of the PV array very quickly under rapidly changing atmospheric conditions. The simulation is carried out in PROTEUS ISIS environment. The program is written in C and then is complied by a freeware version C compiler “WinAVR”. The power tracker is developed and tested in the laboratory. It is observed that the use of MPPT enhances the output power by 25%. The measured parameters such as the panel voltage, current, power and corresponding duty cycle are displayed on the LCD.
Software Performance Engineering for Cloud Applications – A Survey

— Cloud computing enables application service providers to lease their computing capabilities for deploying applications depending on user QoS (Quality of Service) requirements.Cloud applications have different composition, configuration and deployment requirements.Quantifying the performance of applications in Cloud computing environments is a challenging task. Software performance engineering(SPE) techniques enable us to assess performance requirements of software applications at the early stages of development. This assessment helps the developers to fine tune their design needs so that the targeted performance goals can be met. In this paper, we try to analyseperformance related issues of cloud applications and identify any SPE techniques currently available for cloud applications.
NEW PROVABLY SECURE BLIND SIGNATURE SCHEME WITH WEIL PAIRING

-------------------------------------------------------------------ABSTRACT-----------------------------------------------------Blind signature scheme provide the feature that a user is able to get a signature without giving the actual message to the signer. Many Blind signature schemes have been proposed in which security are based on intractability of factoring or DLP (Discrete Logarithm Problem ). In 2010, Fan et al.[11] gave a provably secure randomized blind signature whose security was based on solving ECDLP(Elliptic Curve Discrete Logarithm Problem). The security of this scheme is improved in order to propose a more secure and efficient scheme. The security of proposed scheme is based on expressing the torsion point of curve into linear combination of its basis points. This is more complicated than solving ECDLP (Elliptic Curve Discrete Logarithm Problem). Also, the simulation results from AVISPA tools confirm the security analysis of proposed protocol.
Determinant of Corporate Governance, Audit Quality and Financing Decision

This paper investigates the effect of both corporate governance and audit quality on the financing decision incurred by Indonesia manufacturing listed companies. By using secondary data; corporate governance measured with the index of CG issued by IICG. and audit quality measured by theusing of Big 4 public accounting firm in Indonesia . The data was analyzed under multiple linear regression analysis by SPSS v.18. The empirical findings reveal that corporate governance anda audit quality have no significant impact to Financial decision of the firm. Key w ords: Corporate governance, Audit quality, Financing decision, Indonesia manufacturing listed companies.
A Survey on Various Classification Methods for SAR Images

– Synthetic Aperture Radar is the ground images captured from the satellite. These images are captured by various GPS system, region, earth or weather monitoring system to identify the particular desired location or some abnormal activity happing in some area, city or country. These are the high resolution images captured from distance in day, night or under environmental conditions. These images can be processed into different forms to identify the relative regions, image categorization etc. In this paper, an exploration of the SAR images and the associated application is provided. The paper also provided the description some of the common region and SAR image classification methods including LDA, KNN, Decision Tree and Bayesian Network. The method exploration is here provided with the process level specification.
Multi-Criterion VM Selection Policy for Workload Consolidation in Cloud Computing

— As a part of workload consolidation in Cloud datacenter, after detecting overloaded hosts, it is required to select the VMs to be migrated such that after migration, the overloaded hosts become normal. Selection of VMs to be migrated is a crucial decision as it affects the performance of both the types of hosts (i.e. the overloaded hosts from which the VMs are selected and the hosts on which the selected VM are placed). This in turn impinges on the performance of overall datacenter. We propose a policy for selection of VMs to be migrated from overloaded hosts based on three criterions. Select VMs in such as way that (i) after migrating the VMs, utilization of overloaded hosts falls below upper threshold value and remain nearest to it ( MaxUtil ) (ii) the number of VMs remains minimal ( MinTotalMigr ) and (iii) resultant migration time stays minimum ( MinMigrTime ). All these three parameters are significant in the process of workload consolidation. Importance of individual parameter over the other is left to individual user’s requirement. In this research, we have proposed a method to optimally select a best VM combination such that all these three parameters are satisfied.
Prolonging the Network Lifetime using ACO in Wireless Sensor Networks

— This paper presents a novel approach to approve better network lifetime with the help of Swarm Intelligence (SI) technique which serves an ideal model of formulating routing protocols for WSNs. In this paper, wireless sensor network is firstly deployed and secondly the sensor nodes are communicated among themselves with the help of Ant Colony Optimization (ACO) so that it can find out the shortest path. Then, Low Energy Aware Clustering Hierarchy (LEACH) is compared with the proposed algorithm.
Efficient Way for Handling Small Files using Extended HDFS

— Hadoop file system is for managing very large amount of files and high fault tolerant. Hadoop is widely used file system. Hadoopfile system is HDFS Hadoop Distributed File System. In HDFS, it is master-slave architecture, but in this architecture there is nothing shared between mater and slaves. Master is single server i.e. NameNode which stores metadata, in its own memory. As consequence, HDFS become problematic with number of small size files. But for storing and managing penalty number of small size imposes Burden on the NameNode, so it is difficult for NameNode to manage all DataNodes & burden on its memory. In HDFS, it does not provide any prefetching, combined file to improve the I/O performance. Extended Hadoop Distributed file system (EHDFS) improves storing and accessing efficiency of small file on file system, In this approach the files are stored in one file is known as combined file on Datanode or client. The ConstituentFileMap is most important in EHDFS for efficient management. Indexing mechanism is used to access individual files from combined file. To minimize load on NameNode and I/O performance improvement index prefetching is used. The footprint in NameNode is reduced then the file system becomes more efficient. EHDFS will minimize the time required for processing.
Contextual Operation Using Pair Wise Ranking and Cot for Recommender Systems

The interest for omnipresent data preparing over the Web has required the improvement of setting - mindful recommender frameworks equipped for managing the issues of data over-burden and data separating. Contemporary recommender frameworks outfit setting - mindfulness with the personalization to offer the most exact proposals about various items, administrations, and assets. Be that as it may, such frameworks run over the issues, for example, meagerly, chilly begin, and versatility that prompt to loose suggestions. The cutting edge setting displaying strategies as a rule regard settings as specific measurements like those of clients and things, and catch importance's amongst settings and clients/things. In any case, such sort of pertinence has much trouble in clarification, e.g., it is not instinctive that a client is more pertinent to weekday than end of the week. A few chips away at multi-space connection forecast can likewise be utilized for the setting mindful proposal, yet they have restrictions in producing suggestions under a lot of logical data. Roused by late works in normal dialect handling, we speak to every setting esteem with an inactive vector, and model the relevant data as a semantic operation on the client and thing. Furthermore, we utilize the logical working tensor to catch the basic semantic impacts of settings. For the relevant data of every client thing collaboration, the logical operation can be displayed by duplicating the working tensor with inactive vectors of settings. However a client thing collaboration results can be produced under particular logical data yet can't be yielded under other relevant circumstances. So we propose a pairwise positioning limitation on the logical data. Our pair-wise positioning limitation uncovers the relative data among various logical circumstances and can be utilized to further improve setting demonstrating. Besides, we propose the top-n suggestion. It is another huge estimation of recommender frameworks.
Monitoring and Detection of Abnormal Driving Behavior Using Smartphone

–Internet of Things (IoT) is the main technological revolution in mobile computing, information and communication systems, describing a world of networked, digital elements where everything is interconnected. To improve Driving Safety, Monitoring the behavior of Real-time abnormal driving is a major part. The driving behaviors are usually monitored with the help of mobile phones and an additional hardware (sensors and cameras), it provides only coarse-grained result, i.e., abnormal driving behaviors are marked off from the normal ones. The ﬁne-grained monitoring system can inform drivers and alarm them about their abnormal driving behaviors and it can inform regarding the same to the monitoring system. This work uses smartphone sensing and machine learning techniques and does not require any additional hardware other than the sensors that comes inbuilt with the smartphones such as accelerometer, orientation sensor and magnetometer sensors. This identifies specific types of abnormal driving behaviors such as Weaving, Swerving, Side slipping, Fast U-turn, Turning with a wide radius and sudden braking. Two machine-learning methods, Support Vector Machine (SVM) and Neuron Networks (NN) respectively, are used to train the features of driving behaviors and obtain a classiﬁer model which can not only distinguish abnormal driving behaviors from normal ones but can also identify speciﬁc type of abnormal driving behavior.
Screening and characterisation of antimicrobial compound from endophytic Aspergillus sp. isolated from Ficus carica

Screening for endophytic fungal isolates capable of producing bioactive secondary metabolites was carried with the leaves of Baliospermum montanum, Plumbago zeylanica, Aegle marmelos and Ficus carica. The isolated endophytic fungi were cultured on Potato Dextrose broth and the mycelia were separated by filtration after incubating for 7 days. The bioactive compounds present in the filtered mycelia as well in the filtrate were extracted using Ethyl acetate. The ethyl acetate extract of Aspergillus sp. isolated from Ficus carica showed significant antimicrobial activity against Pseudomonas aeruginosa as test organisms. Instrumental analysis like UV-visible, TLC, IR and HPLC indicated the presence of phenol groups and amine groups in the bioactive compound.
Effective Path Queries with Random Sequence Controls

-We propose novel solutions to the general optimal route query, based on two different methodologies, namely backward search and forward search. In addition, we discuss how the proposed methods can be adapted to answer a variant of the optimal route queries, in which the route only needs to cover a subset of the given categories.The paper focus on operational time along with the distance. Extensive experiments, using both real and synthetic data sets, confirm that the proposed solutions are efficient and practical, and outperform existing methods by large margins.
Gastro-esophageal reflux in mechanically ventilated patients: The “oil well” analogy

From a conceptual and clinical point of view, a comprehensive interpretation of risk factors leading to gastro-esophageal reflux (GER) is translated in this review into an “oil well” analogy: whereas gastric contents represent the oil deposit, groups of “on-off” events reflect the presence or absence of specific risk factors and events, respectively, similar to opening and closing a drilling tower. In this dynamic analogy for both the risk and magnitude of GER, we emphasize that generalization of GER risk factors in clinical settings is extremely relevant but fraught with complexity, difficulties and limitations. Therefore, the primary purpose of this review is to examine the effect of the interaction between body positioning, gastrointestinal function, tube feeding, drugs and additional factors on the aspiration risk in critically ill, tube-fed patients.
Shortest Path Multicast Tree Construction Algorithm to Transmit Multimedia Real Time Traffic in MANETS

: Mobile ad hoc network is a self-configuring, self organizing and decentralized network. It is connected by wireless links. Each device in a MANET is free to move independently in any direction. Hence it can change its links to other devices frequently. It is a collection of mobile wireless nodes that can dynamically form a network without using any pre-existing infrastructure. Due to characteristics like temporary topology and absence of centralized authority, routing is one of the major issues in ad hoc networks. Because of connectivity richness in mobile ad hoc networks, there often exist multiple paths between a source and a destination. Since many applications require uninterrupted connectivity of a session, the ability to find long-living paths can be very useful. However, MANETs does not seem to effectively support live video transmission and multimedia applications. The main objective of our work is to improve the performance. In mobile ad hoc networks (MANET), during real time traffic multicasting, there is increased energy consumption and delay. Hence in mobile ad hoc network (MANET) cluster head selection is considered a gigantic challenge. An efficient ENB cluster head selection algorithm [1] based on the combination of important matrices Residual Energy (E), Node connectivity (C) and Available Bandwidth (B) is used for selection of the cluster head efficiently in Mobile ad hoc networks. The multimedia traffic stream is split into multiple sub-streams prior to transmission using Top-N rule selection approach algorithm [2]. In this paper we are proposing a shortest path multicast tree construction algorithm to transmit the real time traffic effectively among the nodes in MANETS. Using the cluster head as group leaders and cluster members as leaf nodes, a shortest path multicast tree is established. This helps in transmitting multimedia real time traffic to different receivers. This algorithm works very well in Energy and Bandwidth Aware Clustering Algorithm for Real-time Traffic Multicasting in Mobile Ad Hoc Networks.
COMPARATIVE STUDIES OF PROXIMATE AND MINERAL COMPOSITION IN LEAVES AND FLOWERS OF CATHARANTHUS

Current study is aimed for determining proximate and mineral composition of the leaves and flowers of Catharanthus roseus (C. roseus) due to the plant's wide application in the indigenous medicinal system and its chemical constituents' importance. Although overall parts of the plant are found to be beneficial and used as an important medicine but the flower and leaves of Catharanthus roseus are widely used as a vital drug from the ancient system of medicine to treat almost every common ailment of the humans. In the present study, the proximate and mineral analysis has been carried out by AOAC methods using atomic absorption spectrophotometer. The Catharanthus roseus leaves and flower showed the presence of important elements. The results showed that the leaves of Catharanthus roseus have high moisture content (15.72 mg/100g), ash (8.94 mg/100g) and high protein content (4.74 mg/100g) than flowers. The results for mineral analysis showed the presence of Sodium (Na), Potassium (K), Calcium (Ca), Magnesium (Mg), Iron (Fe) and Zinc (Zn) in both leaves and flowers. The most important finding of the work was that leaves of Catharanthus roseus revealed higher concentration levels of all minerals excluding Fe and Zn. The proximate composition in both leaves and flowers of Catharanthus roseus were found to be different. Therefore, different parts of this medicinal plant are enriched in some micro and macro nutrients like Iron (Fe), Calcium (Ca), Sodium (Na), Potassium (K) and Zinc (Zn) which are very important for biological metabolic system as well as human health.
Job shop Scheduling under Answer Set Programming Environment

—Answer set programming (ASP) is a new programming language paradigm combining the declarative aspect with non-monotonic reasoning. In this paper, we will show an application of job shop scheduling in Answer Set Programming. The problem is a highly combinatorial and is generally solved by specific programs written in procedural languages. We present an approach to solve the job shop scheduling problem in Answer Set Programming and compare them with the standard ones. It turns out that, although Answer Set Programming greatly simplifies the problem statement. It is comparable in efficiency to specialized programs.
A Review on Quantitative Models in Railway Rescheduling

This paper draws an overview of various quantitative rescheduling models that have been developed in past research in dealing with delay management of passenger railway services. In general, the quantitative models highlighted aimed to reduce or minimize the service delays experienced by passengers when service disruptions occur. In many literatures observed, the delay management problems are formulated as mathematical and non-mathematical models, which are then tested on real-life data of railway companies worldwide. Local search and exact methods which provide near-optimal and optimal solutions, respectively, are adopted as the solution approaches, with a target of producing short computing time and better quality solution. In this sense, this review paper provides a comprehensive analysis for researchers to enhance the existing rescheduling models or perhaps to adopt a new approach in solving the models developed. This review also suggests the need to minimize service delays for passenger trains due to disruptions, in the context of local railway services.
PERFORMANCE COMPARISON OF APRIORI, ECLAT AND FP-GROWTH ALGORITHM FOR ASSOCIATION RULE LEARNING

— The main aim is to generate a frequent itemset. Big Data analytics is the process of examining big data to uncover hidden patterns. Association Rule Learning is a technique which is used to implement big data. It finds the frequent items in the dataset. Frequent itemsets are those items which occur frequently in the database. To find the frequent itemsets, we are using three algorithms APRIORI ALGORITHM, ECLAT ALGORITHM and FP-GROWTH ALGORITHM. The performance of these three algorithms is compared based on the Time efficiency. After the comparison, we conclude that APRIORI algorithm is the fastest algorithm for large dataset and FP-GROWTH algorithm is the fastest algorithm for small dataset. It takes less time to generate the frequent item-sets as compared to other algorithm, that is, ECLAT algorithm.
Heart Anomaly Forecasting Applying a Series of Data Mining Methods

Many medicinal fields comprise lots of data regarding the health care. There may be some data with which we can derive some interesting patterns which frequently go exploited. Data mining is the process of analyzing huge data sets and derive the relevant information. It becomes more prominence in case of heart disease which is the major concern behind the deaths all over the world. The term heart disease refers to various types of conditions that can affect the heart functions. This medicinal condition defines the unpredicted health conditions that directly control all the parts of the heart. It makes use of different parameters included in data mining include clustering, association rule mining and prediction which are used to predict the heart disease. Initially the heart disease data set is preprocessed to eliminate any irrelevant data. The preprocessed data is now clustered using K-means clustering algorithm. Maximal Frequent Item set Algorithm (MAFIA) is used to derive the maximal frequent patterns in data set. These results can be classified using C 4.5 training algorithm to build a decision tree. The calculated metrics proved that our designed prediction system is capable of predicting the heart disease successfully.
A Two Factor Authentication System for Touchscreen Mobile Devices Using Static Keystroke Dynamics and Password

- The number of touchscreen mobile devices are rapidly increasing each day and so are the number of people that use them. Username-password combination is the most common method of authentication but has many vulnerabilities like shoulder surfing, social engineering, brute force attacks, key-loggers, etc. Keystroke Dynamics provides a novel approach to strengthen this existing method. Typing rhythm or keystroke of every user is distinct in the same way as signatures of users are distinct. Keystroke Dynamics is a biometric but unlike other biometrics it does not need any special hardware. In this paper, we have proposed a simple yet elegant method for touchscreen mobile devices which uses keystroke dynamics as first factor authentication and a secret password as second factor authentication to grant access to the mobile device.