bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigation on the Services of Private Cloud Computing by Using ADAM Method
Authors: Widiyasono Nur , Riadi Imam , Luthfie Ahmad 
Published: October 2019

<p>Cloud services are offered by many cloud service providers, but in for large companies generally are build by a private cloud computing. In cloud systems of abuse it can be done by internal users or due to misconfiguration or may also refer to weaknesses in the system. This study evaluated the ADAM method (Advanced Data Acquisition Model) and tested the case schemes which are being carried out in the laboratory simulation of the process in order to obtain forensic evidence of digital data on private cloud computing services. Referring to the results of the investigation process by using ADAM Method, it can be verified that there are several parameters of the success investigation including the structure of files, files, time stamp, mac-address, IP address, username password, and the data from a server both from the desktop PC or smartphone, therefore the investigation by using ADAM can be succesed properly and correctly. Another contribution of this study was to identify the weaknesses of the service system that used owncloud in users list of the the same group can change another’s user’s passwod.</p>
Multicriteria Group Decision Making Using Fuzzy Approach for Evaluating Criteria of Electrician
Authors: Hadikurniawati Wiwien , Mustofa Khabib 
Published: October 2019

<p>This paper presents an approach of fuzzy multicriteria group decision making in determining alternatives to solve the selection problem of the electrician through a competency test. Fuzzy approach is used to determine the highest priority of alternative electrician who has knowledge and ability that best fits the given parameters. Linguistic variables are presented by triangular fuzzy numbers. They are used to represent a subjective assessment of the decision-makers so that uncertainty and imprecision in the selection process can be minimized. Fuzzy approach require transforming crisp data to fuzzy numbers. Output of the best alternatives is generated by ranking method. Ranking has been made base on eight criteria which make the evaluation basis of each alternative. Ranking of the results is determined using different value of optimism index (). The fuzzy multi criteria decision making (FMCDM) calculation is using the best alternative using three value of optimism index. The result of calculation shows that the same alternative reached from different index of optimism. This alternative is the highest priority of decision making process.</p>
Environment Friendly Voltage Up-gradation Model for Distribution Power Systems
Authors: Nithiyananthan K. , Umasankar Umasankar 
Published: October 2019

<p>The main aim of this research work is to analyze and develop voltage up gradation procedure model for effective &amp; economic power distribution in urban and suburban area. Voltage up gradation from 6.6KV to 11KV of the distribution power system network has been considered for the proposed research work. Electric power consumption has been increasing uninterruptedly, being this increase specially accelerated in the last few years. Nowadays electric lines are saturated; they are reaching critical values of ampere capacity and sag. Therefore, building new lines has been necessary to provide the ever increasing consumption. The difficulty to find new corridors to construct new distribution lines, underground cables is increasing in cities, industrial areas and in many cases it is simply impossible. The construction of new electric lines is increasing difficulty, thus there is a need to look at alternatives that increases the power transfer capacity. Voltage up gradation of the existing electric cables/lines of the distribution system is the most viable solution and it stresses on the savings of power due to a reduction in system losses when the voltage is high. The proposed research work is to develop and analyze voltage up gradation procedures and protocols for converting 6.6KV network into 11KV network in a distributed system. It also takes into account the expenses incurred in the process and the various other important constraints.</p>
Face Recognition using Multi Region Prominent LBP Representation
Authors: Konda Srinivasa Reddy , Kumar V Vijaya , Krishna Venkata 
Published: October 2019

<p>Various face recognition methods are derived using local features among them the Local Binary Pattern (LBP) approach is very famous. The histogram techniques based on LBP is a complex task. Later Uniform Local Binary Pattern (ULBP) is derived on LBP, based on the bitwise transitions and ULBP’s are treated as the fundamental property of texture. The ULBP approach treated all Non-Uniform Local Binary Patterns’ (NULBP) into one miscellaneous label. Recently we have derived Prominent LBP (PLBP), Maximum PLBP (MPLBP) and Smallest PLBP (SPLBP). The PLBP consists of the majority of the ULBP’s and some of the NULBP’s. The basic disadvantage of these various variants of LBP’s is they are basically local approaches and completely failed in representing features derived from large regions or macrostructures, which are very much essential for faces. This paper derives PLBP’s on the large region. The rectangular region of this paper is assumed with a size of multiples of three and PLBPs are evaluated on dividing each region into multiple regions. The proposed Multi Region-PLBP (MR-PLBP) approach is tested on three facial databases namely Yale, Indian and AT&amp;T ORL. The experimental results show the proposed approach significantly outperforms the other LBP based face recognition methods.</p>
A Hybrid Cryptographic System for Secured Device to Device Communication
Authors: Krishna A. Rama , Chakravarthy A. S. N. , Sastry A. S. C. S. 
Published: October 2019

<p>It is general fact that even after enormous expansion of wireless communication there are still dead regions that hampers the effective communication. With exponential rise in the smart phones, a new layer of communication has evolved that could address the concerns of dead regions and capacity barriers. D2D is the evolving communication technology which focuses on short distance hops between the public devices to reach the destination. The major drawback of this technology is that most of the devices are public hence trustworthiness of the entire channel needs to be addressed in order to make it a viable solution. In this paper, we introduce a novel hybrid cryptographic approach that could address multiple eavesdroppers’ scenario. This approach incorporates both Huffman coding and Binary coding to enhance the crypto benefits for the information transmitted over D2D channel that consists of several public devices. The dual-crypto nature of the proposed algorithm offers higher efficiency, better security and improved key transmission. Thus, the proposed hybrid cryptographic approach is robust in nature while easy and simple to operate. In addition, the proposed approach could recover the original information without any distortion from the encrypted data making the approach lossless in nature. Further simulation results prove that the proposed offers confidentiality to the transmitted to data while addressing the network capacity crunch.</p>
Energy Efficient Frame Structure for Gigabit Passive Optical Networks
Authors: Butt Rizwan Aslam , Idrus Sevia Mahdaliza , Radzi Raja Zahilah , Qureshi Kashif Naseer 
Published: October 2019

<p>Increasing power consumption in information and communication access networks is one of the major cause of greenhouse gas emissions. These emissions are harmful to life on earth. Passive Optical Networks (PONs) are energy efficient but the broadcast nature of downstream traffic may cause of huge unnecessary processing of frames by the optical network units and result in significant energy wastage. Bi-PON technique tried to solve this problem by changing the XGPON / GPON frame structure to an interleaved pattern but also required additional hardware changings at the optical network units. In this study, we have tried to achieve the same objective by making a few changings in the GPON frame structure without modifying the existing hardware structure. The simulation results show that 25.25% processing energy of an ONU can be saved by incorporating these changes.</p>
EEG Based Eye State Classification using Deep Belief Network and Stacked AutoEncoder
Authors: Narejo Sanam , Pasero Eros , Kulsoom Farzana 
Published: October 2019

<p>A Brain-Computer Interface (BCI) provides an alternative communication interface between the human brain and a computer. The Electroencephalogram (EEG) signals are acquired, processed and machine learning algorithms are further applied to extract useful information. During EEG acquisition, artifacts are induced due to involuntary eye movements or eye blink, casting adverse effects on system performance. The aim of this research is to predict eye states from EEG signals using Deep learning architectures and present improved classifier models. Recent studies reflect that Deep Neural Networks are trending state of the art Machine learning approaches. Therefore, the current work presents the implementation of Deep Belief Network (DBN) and Stacked AutoEncoders (SAE) as Classifiers with encouraging performance accuracy. One of the designed SAE models outperforms the performance of DBN and the models presented in existing research by an impressive error rate of 1.1% on the test set bearing accuracy of 98.9%. The findings in this study, may provide a contribution towards the state of the art performance on the problem of EEG based eye state classification.</p>
Application of the Least Square Support Vector Machine for point-to-point forecasting of the PV Power
Authors: Farhadi Mahdi , Mollayi Nader 
Published: October 2019

<p>In today's industrial world, the growing capacity of renewable energy sources is a crucial factor for sustainable power generation. The application of solar photovoltaic (PV) energy sources, as a clean and safe renewable energy resource has found great attention among the consumers in the recent decades. Accurate forecasting of the generated PV power is an important task for scheduling the generators and planning the consumption patterns of customers to save electricity costs. To this end, it is necessary to develop a global model of the generated power based on the effective factors which are mainly the solar radiation intensity and the ambient weather temperature. As a result of the wide numerical range of these parameters and various weather conditions, a large training database must be used for developing the models, which results in high-computational complexity of the algorithms used for training the models. In this paper, a novel algorithm for point to point prediction of the generated power based on the least squares support vector machine (LS-SVM) has been proposed which can handle the large training database with a very fewer deal of computation and benefits from reasonable accuracy and generalization capability. </p>
A New Design of Capacitive Power Transfer Based on Hybrid Approach for Biomedical Implantable Device
Authors: Mustapa Zaki , Saat Shakir , Yusof Yusmarnita 
Published: October 2019

<p>This paper presents the development of a new design method of capacitive power transfer (CPT) which is based on hybrid concept for Biomedical Implants. This method is able to improve various issues found in the widely used CPT system that is bipolar CPT method. Based on the ability of this purposed, the simulation of the CPT system has been designed to prove an amount of power transferred through a layer of tissue. The design used to validate the suggested model which to powering implanted device, and it was performed with 3cm square plates, which have a layer of beef with the 5mm thickness in between 2 coupling plate. Power signal was generated by Class E zero voltage switching. The Class E zero voltage switching has been designed to generating alternate current with the 1MHz frequency appropriate to the hybrid CPT system specification.</p><p class="Abstract"> </p>
High Level Speaker Specific Features as an Efficiency Enhancing Parameters in Speaker Recognition System
Authors: Singh Satyanand 
Published: October 2019

<p>In this paper, I present high-level speaker specific feature extraction considering intonation, linguistics rhythm, linguistics stress, prosodic features directly from speech signals. I assume that the rhythm is related to language units such as syllables and appears as changes in measurable parameters such as fundamental frequency ( ), duration, and energy. In this work, the syllable type features are selected as the basic unit for expressing the prosodic features. The approximate segmentation of continuous speech to syllable units is achieved by automatically locating the vowel starting point. The knowledge of high-level speaker’s specific speakers is used as a reference for extracting the prosodic features of the speech signal. High-level speaker-specific features extracted using this method may be useful in applications such as speaker recognition where explicit phoneme/syllable boundaries are not readily available. The efficiency of the particular characteristics of the specific features used for automatic speaker recognition was evaluated on TIMIT and HTIMIT corpora initially sampled in the TIMIT at 16 kHz to 8 kHz. In summary, the experiment, the basic discriminating system, and the HMM system are formed on TIMIT corpus with a set of 48 phonemes. Proposed ASR system shows 1.99%, 2.10%, 2.16% and 2.19 % of efficiency improvements compared to traditional ASR system for and of 16KHz TIMIT utterances.</p>
Crowd Recognition System Based on Optical Flow Along with SVM classifier
Authors: K Shreedarshan , Selvi S.Sethu 
Published: October 2019

<p>The manuscript discusses about abnormalities in a crowded scenario. To prevent the mishap at a public place, there is no much mechanism which could prevent or alert the concerned authority about suspects in a crowd. Usually in a crowded scene, there are chances of some mishap like a terrorist attack or a crime. Our target is finding techniques to identify such activities and to possibly prevent them. If the crowd members exhibit abnormal behavior, we could identify and say that this particular person is a suspect and then the concerned authority would look into the matter. There are various methods to identify the abnormal behavior. The proposed approach is based on optical flow model. It has an ability to detect the sudden changes in motion of an individual among the crowd. First, the main region of motion is extracted by the help of motion heat map. Harris corner detector is used for extracting point of interest of extracted motion area. Based on the point of interest an optical flow is estimated here. After analyzing this optical flow model, a threshold value is fixed. Basically optical flow is an energy level of individual frame. The threshold value is forwarded to SVM classifier, which produces a better result with 99.71% accuracy. This approach is very useful in real time video surveillance system where a machine can monitor unwanted crowd activity. </p>
Secure and Proficient Cross Layer (SPCL) QoS Framework for Mobile Ad-hoc
Authors: Sahu Santosh , Sharma Sanjeev 
Published: October 2019

A cross layer QoS framework is a complete system that provides required QoS services to each node present in the network. All components within it cooperate together for providing the required services. In existing QoS frameworks there is no security mechanism provided while Security is a critical aspect for QoS in the MANET environment. Cross layer QoS framework tend to be vulnerable to a number of threats and attacks like, over/under-reporting of available bandwidth, over-reservation, state table starvation, QoS degradation, information disclosure, theft of services timing attack, flooding attack, replay attack, and denial of service (DoS) attack, attacks on information in transit and attacks against routing. So it is necessary when designing protocols for QoS framework, the harmony between security and QoS must be present as one impacts the others. In this work we proposed secure and proficient cross layer (SPCL) QoS frameworks which prevents from various types of threats and attacks. The proposed SPCL QoS framework achieves better performance compared to existing QoS frameworks in metrics of throughput, packet drop ratio, end-to-end delay, and average jitter in both condition when malicious node present in the network and when malicious node not present in the network
Business recommendation based on collaborative filtering and feature engineering – aproposed approach
Authors: Rokade Prakash Pandharinath , D Aruna Kumari 
Published: October 2019

Business decisions for any service or product depend on sentiments by people. We get these sentiments or rating on social websites like twitter, kaggle. The mood of people towards any event, service and product are expressed in these sentiments or rating. The text of sentiment contains different linguistic features of sentence. A sentiment sentence also contains other features which are playing a vital role in deciding the polarity of sentiments. If features selection is proper one can extract better sentiments for decision making. A directed preprocessing will feed filtered input to any machine learning approach. Feature based collaborative filtering can be used for better sentiment analysis. Better use of parts of speech (POS) followed by guided preprocessing and evaluation will minimize error for sentiment polarity and hence the better recommendation to the user for business analytics can be attained.
A statistical approach to spectrum sensing using bayes factor and p-Values
Authors: Reddy Deepa N. , Ravinder Y. 
Published: October 2019

<p>The sensing methods with multiple receive antennas in the Cognitive Radio (CR) device, provide a promising solution for reducing the error rates in the detection of the Primary User (PU) signal. The received Signal to Noise Ratio at the CR receiver is enhanced using the diversity combiners. This paper proposes a statistical approach based on minimum Bayes factors and p-Values as diversity combiners in the spectrum sensing scenario. The effect of these statistical measures in sensing the spectrum in a CR environment is investigated. Through extensive Monte Carlo simulations it is shown that this novel statistical approach based on Bayes factors provides a promising solution to combine the test statistics from multiple receiver antennas and can be used as an alternative to the conventional hypothesis testing methods for spectrum sensing. The Bayesian results provide more accurate results when measuring the strength of the evidence against the hypothesis.</p>
Personal data protection and liability of internet service provider: a comparative approach
Authors: Dharmawan Ni Ketut Supasti , Kasih Desak Putu Dewi , Stiawan Deris 
Published: October 2019

The users of electronic service provider often suffered losses caused by internet services did not work properly including losses due to leakage of personal data protection stored in cloud computing. The study aims to examine electronic service provider liability upon their failure performing internet services properly and security attacks on cloud computing. This study was normative legal research by examining national and international legal materials. The finding shows that the electronic provider shall be responsible based on right and obligation agreed under the agreement. Related to cloud computing, providing adequate security to avoid security attacks and misuse of private data that caused losses to the users becoming the liability of service provider. Based on the Federal Trade Commission Act, the liability arises on the grounds of deceptive and unfair trade practices. Under the General Data Protection Regulation of the European Union, the liability arises on the basis as the controller then provider liable for compensation for user’s suffered damage. In Indonesia, based on the Electronic Information and Transaction Law Amendment, the liability to the owner of personal data whose rights are violated and suffered losses arises due to a failure of ISP protect the data security. For better protection in Indonesia, the protection of big data and clear territorial scope of protection become necessary to consider.
A fuzzy system for detection and classification of textile defects to ensure the quality of fabric production
Authors: Mohammed Iman Subhi , Alhamdani Israa Mohammed 
Published: October 2019

<p>The aim of this research focuses on construct a computerized system for textile defects detection. The system merges between image processing methods, statistical methods in addition to the Intelligent techniques via Neural Network and Fuzzy Logic. Gabor filters were used to identify edges and to highlight defective areas in fabric images, then to train the neural network on statistical and geometry features derived from fabric images to form the special neural network distinguish and classify defects into the fourteen categories, which are the most common defects in the textile factory. The proposed work includes two phases. The first phase is to detect the defects in fabrics. The second phase is the classification phase of the defect. At the defect detection stage, a Discrete Cosine Transfer (DCT) converts the images to the frequency domain. Image features then drawn and introduce them to the Elman Neural Network to detect the existence of defects. In the classification stage, the images are converted to the frequency domain by the Gabor filter and then the image features are extracted and inserted into the back propagation network to classify the fabric defects in those images. Fuzzy logic is then applied to neural network outputs and interference values are used in fuzzy logic to increase final discrimination. We evaluate a distinction rate of 91.4286% .After applying the fuzzy logic to neural network output; the discrimination rate was raised to 97.1428%. </p>
Real-time online fingerprint image classification using adaptive hybrid techniques
Authors: Mishra Annapurna , Dehuri Satchidananda 
Published: October 2019

<p class="Abstract">This paper presents three different hybrid classification techniques applied for the first time in real-time online fingerprint classification. Classification of online real time fingerprints is a complex task as it involves adaptation and tuning of classifier parameters for better classification accuracy. To accomplish the optimal adaptation of parameters of functional link artificial neural network (FLANN) for real-time online fingerprint classification, proven and established optimizers, such as Biogeography based optimizer (BBO), Genetic algorithm (GA), and Particle swarm optimizer (PSO) are intelligently infused with it to form hybrid classifiers. The global features of the real-time fingerprints are extracted using a Gabor filter-bank and then passed into adaptive hybrid classifiers for the desired classification as per the Henry system. Three hybrid classifiers, the optimized weight adapted Biogeography based optimized functional link artificial neural network (BBO-FLANN), Genetic algorithm based functional link artificial neural network (GA-FLANN) and Particle swarm optimized functional link artificial neural network (PSO-FLANN), are explored for real-time online fingerprint classification, where the PSO-FLANN technique is showing superior performance as compared to GA-FLANN and BBO-FLANN techniques. The best accuracy observed by the application of PSO-FLANN, is 98% for real-time online fingerprint classification.</p>