List of Articles ISI

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Achieving Better Performance of S-MMA Algorithm in the OFDM Modulation
  Saeed Ghazi-Maghrebi Babak Haji Bagher Naeeni Mojtaba Lotfizad
  10.7508/jist.2013.01.002
  20.1001.1.23221437.2013.1.1.2.7
  Effective algorithms in modern digital communication systems provide a fundamental basis for increasing the efficiency of the application networks which are in many cases neither optimized nor very close to their practical limits. Equalizations are one of the preferred More

  Effective algorithms in modern digital communication systems provide a fundamental basis for increasing the efficiency of the application networks which are in many cases neither optimized nor very close to their practical limits. Equalizations are one of the preferred methods for increasing the efficiency of application systems such as orthogonal frequency division multiplexing (OFDM). In this paper, we study the possibility of improving the OFDM modulation employing sliced multi-modulus algorithm (S-MMA) equalization. We compare applying the least mean square (LMS), multi modulus algorithm (MMA) and S-MMA equalizations to the per tone equalization in the OFDM modulation. The paper contribution lies in using the S-MMA technique, for weight adaptation, to decreasing the BER in the OFDM multicarrier modulation. For more efficiency, it is assumed that the channel impulse response is longer than the cyclic prefix (CP) length and as a result, the system will be more efficient but at the expense of the high intersymbol interference (ISI) impairment existing. Both analysis and simulations demonstrate better performance of the S-MMA compared to LMS and MMA algorithms, in standard channels with additive white Gaussian noise (AWGN) and ISI impairment simultanously. Therefore, the S-MMA equalization is a good choice for high speed and real-time applications such as OFDM based systems. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Performance Analysis of SVM-Type Per Tone Equalizer Using Blind and Radius Directed Algorithms for OFDM Systems
  Babak Haji Bagher Naeeni
  10.7508/jist.2013.02.001
  In this paper, we present Support Vector Machine (SVM)-based blind per tone equalization for OFDM systems. Blind per tone equalization using Constant Modulus Algorithm (CMA) and Multi-Modulus Algorithm (MMA) are used as the comparison benchmark. The SVM-based cost funct More

  In this paper, we present Support Vector Machine (SVM)-based blind per tone equalization for OFDM systems. Blind per tone equalization using Constant Modulus Algorithm (CMA) and Multi-Modulus Algorithm (MMA) are used as the comparison benchmark. The SVM-based cost function utilizes a CMA-like error function and the solution is obtained by means of an Iterative Re-Weighted Least Squares Algorithm (IRWLS). Moreover, like CMA, the error function allows to extend the method to multilevel modulations. In this case, a dual mode algorithm is proposed. Dual mode equalization techniques are commonly used in communication systems working with multilevel signals. Practical blind algorithms for multilevel modulation are able to open the eye of the constellation, but they usually exhibit a high residual error. In a dual mode scheme, once the eye is opened by the blind algorithm, the system switches to another algorithm, which is able to obtain a lower residual error under a suitable initial ISI level. Simulation experiments show that the performance of blind per tone equalization using support vector machine has better than blind per tone equalization using CMA and MMA, from viewpoint of average Bit-Error Rate (BER). Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - PSO-Algorithm-Assisted Multiuser Detection for Multiuser and Inter-symbol Interference Suppression in CDMA Communications
  Atefeh Haji Jamali Arani paeez azmi
  10.7508/jist.2014.01.006
  Applying particle swarm optimization (PSO) algorithm has become a widespread heuristic technique in many fields of engineering. In this paper, we apply PSO algorithm in additive white Gaussian noise (AWGN) and multipath fading channels. In the proposed method, PSO algor More

  Applying particle swarm optimization (PSO) algorithm has become a widespread heuristic technique in many fields of engineering. In this paper, we apply PSO algorithm in additive white Gaussian noise (AWGN) and multipath fading channels. In the proposed method, PSO algorithm was applied to solve joint multiuser and inter-symbol interference (ISI) suppression problems in the code-division multiple-access (CDMA) systems over multipath Rayleigh fading channel and consequently, to reduce the computational complexity. At the first stage, to initialize the POS algorithm, conventional detector (CD) was employed. Then, time-varying acceleration coefficients (TVAC) were used in the PSO algorithm. The simulation results indicated that the performance of PSO-based multiuser detection (MUD) with TVAC is promising and it is outperforming the CD. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  4 - GoF-Based Spectrum Sensing of OFDM Signals over Fading Channels
  Seyed Sadra Kashef paeez azmi Hamed Sadeghi
  10.7508/jist.2014.02.005
  Goodness-of-Fit (GoF) based spectrum sensing of orthogonal frequency-division multiplexing (OFDM) signals is investigated in this paper. To this end, some novel local sensing methods based on Shapiro-Wilk (SW), Shapiro-Francia (SF), and Jarque-Bera (JB) tests are first More

  Goodness-of-Fit (GoF) based spectrum sensing of orthogonal frequency-division multiplexing (OFDM) signals is investigated in this paper. To this end, some novel local sensing methods based on Shapiro-Wilk (SW), Shapiro-Francia (SF), and Jarque-Bera (JB) tests are first studied. In essence, a new threshold selection technique is proposed for SF and SW tests. Then, three studied methods are applied to spectrum sensing for the first time and their performance are analyzed. Furthermore, the computational complexity of the above methods is computed and compared to each other. Simulation results demonstrate that the SF detector outperforms other existing GoF-based methods over AWGN channels. Furthermore simulation results demonstrate the superiority of the proposed SF method in additive colored Gaussian noise channels and over fading channel in comparison with the conventional energy detector. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  5 - A Study on Clustering for Clustering Based Image De-noising
  Hossein Bakhshi Golestani Mohsen Joneidi Mostafa Sadeghi
  10.7508/jist.2014.04.001
  In this paper, the problem of de-noising of an image contaminated with Additive White Gaussian Noise (AWGN) is studied. This subject is an open problem in signal processing for more than 50 years. In the present paper, we suggest a method based on global clustering of i More

  In this paper, the problem of de-noising of an image contaminated with Additive White Gaussian Noise (AWGN) is studied. This subject is an open problem in signal processing for more than 50 years. In the present paper, we suggest a method based on global clustering of image constructing blocks. As the type of clustering plays an important role in clustering-based de-noising methods, we address two questions about the clustering. The first, which parts of the data should be considered for clustering? The second, what data clustering method is suitable for de-noising? Then clustering is exploited to learn an over complete dictionary. By obtaining sparse decomposition of the noisy image blocks in terms of the dictionary atoms, the de-noised version is achieved. Experimental results show that our dictionary learning framework outperforms its competitors in terms of de-noising performance and execution time. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  6 - Fusion Infrared and Visible Images Using Optimal Weights
  Mehrnoush  Gholampour Hassan Farsi Sajad Mohammadzadeh
  10.7508/jist.2015.03.006
  Image fusion is a process in which different images recorded by several sensors from one scene are combined to provide a final image with higher quality compared to each individual input image. In fact, combination of different images recorded by different sensors is on More

  Image fusion is a process in which different images recorded by several sensors from one scene are combined to provide a final image with higher quality compared to each individual input image. In fact, combination of different images recorded by different sensors is one of image fusion methods. The fusion is performed based on maintaining useful features and reducing or removing useless features. The aim of fusion has to be clearly specified. In this paper we propose a new method which combines vision and infrared images by weighting average to provide better image quality. The weighting average is performed in gradient domain. The weight of each image depends on its useful features. Since these images are recorded in night vision, the useful features are related to clear scene details. For this reason, object detection is applied on the infrared image and considered as its weight. The vision image is also considered as a complementary of infrared image weight. The averaging is performed in gradient of input images, and final composed image is obtained by Gauss-Seidel method. The quality of resulted image by the proposed algorithm is compared to the obtained images by state-of-the-art algorithms using quantitative and qualitative measures. The obtained results show that the proposed algorithm provides better image quality. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  7 - Selecting Enterprise Resource Planning System Using Fuzzy Analytic Hierarchy Process Approach
  hojatallah hamidi
  10.7508/jist.2015.04.001
  To select an enterprise resource planning (ERP) system is time consuming due to the resource constraints, the software complexity, and the different of alternatives. A comprehensively systematic selection policy for ERP system is very important to the success of ERP pro More

  To select an enterprise resource planning (ERP) system is time consuming due to the resource constraints, the software complexity, and the different of alternatives. A comprehensively systematic selection policy for ERP system is very important to the success of ERP project. In this paper, we propose a fuzzy analytic hierarchy process (FAHP) method to evaluate the alternatives of ERP system. The selection criteria of ERP system are numerous and fuzzy, so how to select an adequate ERP system is crucial in the early phase of an ERP project. The framework decomposes ERP system selection into four main factors. The goal of this paper is to select the best alternative that meets the requirements with respect to product factors, system factors, management factors and vendor factors. The sub-attributes (sub-factors) related to ERP selection have been classified into thirteen main categories of Functionality, Reliability , Usability , Efficiency , Maintainability , Portability , Cost, Implementation time, User friendliness , Flexibility , Vendor Reputation , Consultancy Services, and R&D Capability and arranged in a hierarchy structure. These criteria and factors are weighted and prioritized and finally a framework is provided for ERP selection with the fuzzy AHP method. Also, a real case study from Iran (PARDIS-LO Company) is also presented to demonstrate efficiency of this method in practice. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  8 - Optimization of Random Phase Updating Technique for Effective Reduction in PAPR, Using Discrete Cosine Transform
  Babak Haji Bagher Naeeni
  10.7508/jist.2016.02.004
  One of problems of OFDM systems, is the big value of peak to average power ratio. To reduce it, any attempt have been done amongst which, random phase updating is an important technique. In contrast to paper, since power variance is computable before IFFT block, the com More

  One of problems of OFDM systems, is the big value of peak to average power ratio. To reduce it, any attempt have been done amongst which, random phase updating is an important technique. In contrast to paper, since power variance is computable before IFFT block, the complexity of this method would be less than other phase injection methods which could be an important factor. Another interesting capability of random phase updating technique is the possibility of applying the variance of threshold power. The operation of phase injection is repeated till the power variance reaches threshold power variance. However, this may be a considered as a disadvantage for random phase updating technique. The reason is that reaching the mentioned threshold may lead to possible system delay. In this paper, in order to solve the mentioned problem, DCT transform is applied on subcarrier outputs before phase injection. This leads to reduce the number of required carriers for reaching the threshold value which results in reducing system delay accordingly. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  9 - Crisis management using spatial query processing in wireless sensor networks
  mohammad shakeri seyyed majid mazinani
  10.7508/jist.2017.18.004
  Natural disasters are an inevitable part of the world that we inhabit. Human casualties and financial losses are concomitants of these natural disasters. However, by an efficient crisis management program, we can minimize their physical and social damages. The real chal More

  Natural disasters are an inevitable part of the world that we inhabit. Human casualties and financial losses are concomitants of these natural disasters. However, by an efficient crisis management program, we can minimize their physical and social damages. The real challenge in crisis management is the inability to timely receive the information from the stricken areas. Technology has come to the aid of crisis management programs to help find an answer to the problem. One of these technologies is wireless sensor network. With recent advances in this field, sensor nodes can independently respond to the queries from the users. This has transformed the processing of the queries into one of the most useful chapters in sensor networks. Without requiring any infrastructure, the sensor network can easily be deployed in the stricken area. And with the help of spatial query processing, it can easily provide managers with the latest information. The main problem, however, is the irregular shape of the area. Since these areas require many points to present them, the transmission of the coordinates by sensor nodes necessitates an increase in the number of data packet transmissions in the sensor network. The high number of packets considerably increases energy consumption. In related previous works, to solve this problem, line simplification algorithm s, such as Ramer-Douglas-Peucker (RDP), were used. These algorithms could lessen energy consumption by reducing the number of points in the shape of the area. In this article, we present a new algorithm to simplify packet shapes which can reduce more points with more accuracy. This results in decreasing the number of transmitted packets in the network, the concomitant reduction of energy consumption, and, finally, increasing network lifetime. Our proposed method was implemented in different scenarios and could on average reduce network’s energy consumption by 72.3%, while it caused only 4.5% carelessness which, when compared to previous methods, showed a far better performance. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  10 - Better performance of special triangular constellations for the OFDM systems in complicated fading channel
  saeed ghazi-maghrebi
  Due to high spectral efficiency, immunity to frequency selective fading, and high data rate, OFDM became a popular modulation technique in digital communication systems. Effectiveness of a signal constellation used in a communication system can provide a fundamental bas More

  Due to high spectral efficiency, immunity to frequency selective fading, and high data rate, OFDM became a popular modulation technique in digital communication systems. Effectiveness of a signal constellation used in a communication system can provide a fundamental basis for efficiency of application networks. During recent years, different constellations were designed for different modulation schemes. In this research, two new triangular constellations schemes which are named as TRI1 and TRI2 are introduced to replace for the well-known rectangular QAM constellation in OFDM modulation. In this paper, the new proposed schemes are compared with different triangular constellations. It has been shown that these new schemes have three major advantages with respect to the QAM. The first advantage is its lower BER which results from the better usage of the constellation space with longer minimum distances. The second advantage of these schemes is their lower PAR with respect to the rectangular QAM constellation. The third property, as the last advantages, is their higher noise immunity with respect to the commonly used QAM constellation. Both mathematical analysis and simulation results demonstrate that by applying high fading channels with AWGN and ISI impairment simultaneously, the TRI1 and TRI2 exhibit a superior performance compared to the rectangular QAM. As a result, they are good choice for high speed and real-time multicarrier applications such as DAB, DVB, and WiFi at no extra cost. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  11 - Design and Implementation of an Ultra-Wide Band, High Precision, and Low Noise Frequency Synthesizer
  Yas Hosseini Tehrani Nasser Masoumi
  10.7508/jist.2017.20.001
  This paper presents system-level design and implementation of an ultra-wide tunable, high precision, fast locking, low phase noise, and low power portable fractional-N frequency synthesizer. The output frequency of the proposed design is ranged from 54 MHz to 6.8GHz. Th More

  This paper presents system-level design and implementation of an ultra-wide tunable, high precision, fast locking, low phase noise, and low power portable fractional-N frequency synthesizer. The output frequency of the proposed design is ranged from 54 MHz to 6.8GHz. The VCO cores cover frequencies from 3.4GHz to 6.8GHz. The programmable output dividers allow generation of the lower frequencies. The frequency resolution of the implemented system is ±20 parts per million (ppm) over -40/85ºC. The output power is tunable between -4dBm and +5dBm. The implemented system has a phase adjust feature that allows shifting of the output phase in relation to the reference oscillator ranged from 0° to 180°. It can generate a wide range, high precision, and linear frequency sweep. The sweep rate, frequency step, and frequency range are tunable. The new frequency tuning algorithm, named Yas algorithm, is proposed to improve frequency precision of the synthesizer. To demonstrate the efficiency of the Yas algorithm, the simulation result MATLAB and the experimental measurements are presented. The system consumes 300mA; therefore, it can be powered by Li-Ion battery. The output phase noise is -95.55 dBc/Hz at 1KHz offset from 3GHz. The experimental measurement results demonstrate that the implemented frequency synthesizer can be used for applications, such as oscillator of spectrum analyzer, automatic test equipment, FMCW radars, high-performance clock source for high speed data converter Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  12 - DBCACF: A Multidimensional Method for Tourist Recommendation Based on Users’ Demographic, Context and Feedback
  Maral Kolahkaj Ali Harounabadi Alireza Nikravan shalmani Rahim Chinipardaz
  10.7508/jist.2018.04.004
  By the advent of some applications in the web 2.0 such as social networks which allow the users to share media, many opportunities have been provided for the tourists to recognize and visit attractive and unfamiliar Areas-of-Interest (AOIs). However, finding the appropr More

  By the advent of some applications in the web 2.0 such as social networks which allow the users to share media, many opportunities have been provided for the tourists to recognize and visit attractive and unfamiliar Areas-of-Interest (AOIs). However, finding the appropriate areas based on user’s preferences is very difficult due to some issues such as huge amount of tourist areas, the limitation of the visiting time, and etc. In addition, the available methods have yet failed to provide accurate tourist’s recommendations based on geo-tagged media because of some problems such as data sparsity, cold start problem, considering two users with different habits as the same (symmetric similarity), and ignoring user’s personal and context information. Therefore, in this paper, a method called “Demographic-Based Context-Aware Collaborative Filtering” (DBCACF) is proposed to investigate the mentioned problems and to develop the Collaborative Filtering (CF) method with providing personalized tourist’s recommendations without users’ explicit requests. DBCACF considers demographic and contextual information in combination with the users' historical visits to overcome the limitations of CF methods in dealing with multi- dimensional data. In addition, a new asymmetric similarity measure is proposed in order to overcome the limitations of symmetric similarity methods. The experimental results on Flickr dataset indicated that the use of demographic and contextual information and the addition of proposed asymmetric scheme to the similarity measure could significantly improve the obtained results compared to other methods which used only user-item ratings and symmetric measures. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  13 - DeepFake Detection using 3D-Xception Net with Discrete Fourier Transformation
  Adeep  Biswas Debayan  Bhattacharya Kakelli Anil Kumar
  10.52547/jist.9.35.161
  20.1001.1.23221437.2021.9.35.2.1
  The videos are more popular for sharing content on social media to capture the audience’s attention. The artificial manipulation of videos is growing rapidly to make the videos flashy and interesting but they can easily misuse to spread false information on social media More

  The videos are more popular for sharing content on social media to capture the audience’s attention. The artificial manipulation of videos is growing rapidly to make the videos flashy and interesting but they can easily misuse to spread false information on social media platforms. Deep Fake is a problematic method for the manipulation of videos in which artificial components are added to the video using emerging deep learning techniques. Due to the increase in the accuracy of deep fake generation methods, artificially created videos are no longer detectable and pose a major threat to social media users. To address this growing problem, we have proposed a new method for detecting deep fake videos using 3D Inflated Xception Net with Discrete Fourier Transformation. Xception Net was originally designed for application on 2D images only. The proposed method is the first attempt to use a 3D Xception Net for categorizing video-based data. The advantage of the proposed method is, it works on the whole video rather than the subset of frames while categorizing. Our proposed model was tested on the popular dataset Celeb-DF and achieved better accuracy. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  14 - Using Decision Lattice Analysis to Model IOT-based Companies’ profit
  Nazanin Talebolfakhr Seyed Babak Ebrahimi Donya Rahmani
  10.29252/jist.8.32.230
  20.1001.1.23221437.2020.8.32.4.0
  Demand uncertainty and high initial investments for IOT-based projects lead to analyzing various types of options, especially real options in project execution to decrease these uncertainties. In this study, we investigate the firms’ expected profits that resulted from More

  Demand uncertainty and high initial investments for IOT-based projects lead to analyzing various types of options, especially real options in project execution to decrease these uncertainties. In this study, we investigate the firms’ expected profits that resulted from appropriate chosen static and dynamic pricing strategies namely low-pricing, high-pricing, and contingent pricing combined with binomial decision lattices. Besides, the reciprocal influence between pricing strategies and IOT investment could provide useful insights for the firms that confront demand uncertainties in selling the firms’ products. We propose a model which is the integration of binomial decision lattices, which have been calculated by Real Option Super Lattice Solver 2017 software, and pricing policies under uncertainty. The results provide insights into what pricing strategies to choose based on the project’s real option value and the level of the firm uncertainty about the purchasing of the high-value consumer. Among the mentioned static and dynamic pricing strategies, high-pricing and contingent pricing strategies under different situations can be selected and expected profits of each of the strategies will be calculated and compared with each other. On the contrary, as the low-pricing strategy resulted in the lowest option value, it will not be scrutinized in this study. Experimental results show that if the IOT investment level and high-value consumer purchasing likelihood are high, the firm will implement the high-pricing strategy, otherwise choosing the contingent pricing due to the demand uncertainty would be appropriate. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  15 - An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier
  Ramesh G Prasanna  G B Santosh  V Bhat Chandrashekar  Naik Champa  H N
  10.52547/jist.9.35.169
  20.1001.1.23221437.2021.9.35.3.2
  Handwritten digit recognition is one of the classical issues in the field of image grouping, a subfield of computer vision. The event of the handwritten digit is generous. With a wide opportunity, the issue of handwritten digit recognition by using computer vision and m More

  Handwritten digit recognition is one of the classical issues in the field of image grouping, a subfield of computer vision. The event of the handwritten digit is generous. With a wide opportunity, the issue of handwritten digit recognition by using computer vision and machine learning techniques has been a well-considered upon field. The field has gone through an exceptional turn of events, since the development of machine learning techniques. Utilizing the strategy for Support Vector Machine (SVM) and Principal Component Analysis (PCA), a robust and swift method to solve the problem of handwritten digit recognition, for the Kannada language is introduced. In this work, the Kannada-MNIST dataset is used for digit recognition to evaluate the performance of SVM and PCA. Efforts were made previously to recognize handwritten digits of different languages with this approach. However, due to the lack of a standard MNIST dataset for Kannada numerals, Kannada Handwritten digit recognition was left behind. With the introduction of the MNIST dataset for Kannada digits, we budge towards solving the problem statement and show how applying PCA for dimensionality reduction before using the SVM classifier increases the accuracy on the RBF kernel. 60,000 images are used for training and 10,000 images for testing the model and an accuracy of 99.02% on validation data and 95.44% on test data is achieved. Performance measures like Precision, Recall, and F1-score have been evaluated on the method used. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  16 - Denoising and Enhancement Speech Signal Using Wavelet
  Meriane Brahim
  10.52547/jist.9.33.37
  20.1001.1.23221437.2021.9.33.5.0
  Speech enhancement aims to improve the quality and intelligibility of speech using various techniques and algorithms. The speech signal is always accompanied by background noise. The speech and communication processing systems must apply effective noise reduction techni More

  Speech enhancement aims to improve the quality and intelligibility of speech using various techniques and algorithms. The speech signal is always accompanied by background noise. The speech and communication processing systems must apply effective noise reduction techniques in order to extract the desired speech signal from its corrupted speech signal. In this project we study wavelet and wavelet transform, and the possibility of its employment in the processing and analysis of the speech signal in order to enhance the signal and remove noise of it. We will present different algorithms that depend on the wavelet transform and the mechanism to apply them in order to get rid of noise in the speech, and compare the results of the application of these algorithms with some traditional algorithms that are used to enhance the speech. The basic principles of the wavelike transform are presented as an alternative to the Fourier transform. Or immediate switching of the window The practical results obtained are based on processing a large database dedicated to speech bookmarks polluted with various noises in many SNRs. This article tends to be an extension of practical research to improve speech signal for hearing aid purposes. Also learn about the main frequency of letters and their uses in intelligent systems, such as voice control systems. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  17 - Smart Pre-Seeding Decision Support System for Agriculture
  Ahmed Wasif Reza Kazi  Saymatul Jannat MD.Shariful  Islam Surajit  Das Barman
  10.52547/jist.15698.10.38.102
  20.1001.1.23221437.2022.10.38.4.6
  In recent years, the Internet of Things (IoT) brings a new dimension for establishing a precision network connectivity of sensors, especially in the agriculture and farming industry, medical, economic, and several sectors of modern society. Agriculture is an important a More

  In recent years, the Internet of Things (IoT) brings a new dimension for establishing a precision network connectivity of sensors, especially in the agriculture and farming industry, medical, economic, and several sectors of modern society. Agriculture is an important area for the sustainability of mankind engulfing manufacturing, security, and resource management. Due to the exponential diminishing of the resources, innovative techniques to support the subsistence of agriculture and farming. IoT aims to extend the use of internet technology to a large number of distributed and connected devices by representing standard and interoperable communication protocols. This paper brings up a solution by IoT, presents the design and implementation of a smart pre-seeding decision support system for agricultural modernization. This project is accomplished by understanding the real-time circumstances in the agriculture field using wireless technology that highlighted the features including pH and temperature sensors, hardware, mobile application, system’s frontend, and backend analysis, and stores the extracted information in the cloud using IoT. The system is made up of frontend data acquisition, data transmission, data processing, and reception, and is also experimentally validated to find out all possible crops that can be cultivated in a specific land with the required amount of fertilizers as well as the overall crops distribution lists. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  18 - Rough Sets Theory with Deep Learning for Tracking in Natural Interaction with Deaf
  Mohammad Ebrahimi Hossein Ebrahimpour-Komeleh
  10.52547/jist.16060.10.39.211
  20.1001.1.23221437.2022.10.39.7.1
  Sign languages commonly serve as an alternative or complementary mode of human communication Tracking is one of the most fundamental problems in computer vision, and use in a long list of applications such as sign languages recognition. Despite great advances in recent More

  Sign languages commonly serve as an alternative or complementary mode of human communication Tracking is one of the most fundamental problems in computer vision, and use in a long list of applications such as sign languages recognition. Despite great advances in recent years, tracking remains challenging due to many factors including occlusion, scale variation, etc. The mistake detecting of head or left hand instead of right hand in overlapping are, modes like this, and due to the uncertainty of the hand area over the deaf news video frames; we proposed two methods: first, tracking using particle filter and second tracking using the idea of the rough set theory in granular information with deep neural network. We proposed the method for Combination the Rough Set with Deep Neural Network and used for in Hand/Head Tracking in Video Signal DeafNews. We develop a tracking system for Deaf News. We used rough set theory to increase the accuracy of skin segmentation in video signal. Using deep neural network, we extracted inherent relationships available in the frame pixels and generalized the achieved features to tracking. The system proposed is tested on the 33 of Deaf News with 100 different words and 1927 video files for words then recall, MOTA and MOTP values are obtained. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  19 - Membrane Cholesterol Prediction from Human Receptor using Rough Set based Mean-Shift Approach
  Rudra Kalyan Nayak Ramamani  Tripathy Hitesh  Mohapatra Amiya  Kumar Rath Debahuti  Mishra
  10.52547/jist.27327.10.39.161
  20.1001.1.23221437.2022.10.39.5.9
  In human physiology, cholesterol plays an imperative part in membrane cells which regulates the function of G-protein-coupled receptors (GPCR) family. Cholesterol is an individual type of lipid structure and about 90 percent of cellular cholesterol is present at plasma More

  In human physiology, cholesterol plays an imperative part in membrane cells which regulates the function of G-protein-coupled receptors (GPCR) family. Cholesterol is an individual type of lipid structure and about 90 percent of cellular cholesterol is present at plasma membrane region. Cholesterol Recognition/interaction Amino acid Consensus (CRAC) sequence, generally referred as the CRAC (L/V)-X1−5-(Y)-X1−5-(K/R) and the new cholesterol-binding domain is similar to the CRAC sequence, but exhibits the inverse orientation along the polypeptide chain i.e. CARC (K/R)-X1−5-(Y/F)-X1−5-(L/V). GPCR is treated as a biggest super family in human physiology and probably more than 900 protein genes included in this family. Among all membrane proteins GPCR is responsible for novel drug discovery in all pharmaceuticals industry. In earlier researches the researchers did not find the required number of valid motifs in terms of helices and motif types so they were lacking clinical relevance. The research gap here is that they were not able to predict the motifs effectively which are belonging to multiple motif types. To find out better motif sequences from human GPCR, we explored a hybrid computational model consisting of hybridization of Rough Set with Mean-Shift algorithm. In this paper we made comparison among our resulted output with other techniques such as fuzzy C-means (FCM), FCM with spectral clustering and we concluded that our proposed method targeted well on CRAC region in comparison to CARC region which have higher biological relevance in medicine industry and drug discovery. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  20 - Breast Cancer Classification Approaches - A Comparative Analysis
  Mohan Kumar Sunil Kumar Khatri Masoud Mohammadian
  10.52547/jist.33395.11.41.1
  20.1001.1.23221437.2023.11.41.8.3
  Cancer of the breast is a difficult disease to treat since it weakens the patient's immune system. Particular interest has lately been shown in the identification of particular immune signals for a variety of malignancies in this regard. In recent years, several methods More

  Cancer of the breast is a difficult disease to treat since it weakens the patient's immune system. Particular interest has lately been shown in the identification of particular immune signals for a variety of malignancies in this regard. In recent years, several methods for predicting cancer based on proteomic datasets and peptides have been published. The cells turns into cancerous cells because of various reasons and get spread very quickly while detrimental to normal cells. In this regard, identifying specific immunity signs for a range of cancers has recently gained a lot of interest. Accurately categorizing and compartmentalizing the breast cancer subtype is a vital job. Computerized systems built on artificial intelligence can substantially save time and reduce inaccuracy. Several strategies for predicting cancer utilizing proteomic datasets and peptides have been reported in the literature in recent years.It is critical to classify and categorize breast cancer treatments correctly. It's possible to save time while simultaneously minimizing the likelihood of mistakes using machine learning and artificial intelligence approaches. Using the Wisconsin Breast Cancer Diagnostic dataset, this study evaluates the performance of various classification methods, including SVC, ETC, KNN, LR, and RF (random forest). Breast cancer can be detected and diagnosed using a variety of measurements of data (which are discussed in detail in the article) (WBCD). The goal is to determine how well each algorithm performs in terms of precision, recall, and accuracy. The variation of each classification threshold has been tested on various algorithms and SVM turned out to be very promising. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  21 - Spectrum Sensing of OFDM Signals Utilizing Higher Order Statistics under Noise Uncertainty Environments in Cognitive Radio Systems
  MOUSUMI HAQUE Tetsuya Shimamura
  10.61186/jist.37961.11.43.251
  20.1001.1.23221437.2023.11.43.8.7
  Cognitive radio (CR) is an important issue to solve the spectrum scarcity problem for modern and forthcoming wireless communication systems. Spectrum sensing is the ability of the CR systems to sense the primary user signal to detect an ideal portion of the radio spectr More

  Cognitive radio (CR) is an important issue to solve the spectrum scarcity problem for modern and forthcoming wireless communication systems. Spectrum sensing is the ability of the CR systems to sense the primary user signal to detect an ideal portion of the radio spectrum. Spectrum sensing is mandatory to solve the spectrum scarcity problem and the interference problem of the primary user. Noise uncertainty consideration for orthogonal frequency division multiplexing (OFDM) transmitted signals in severe noise environments is a challenging issue for measuring the performance of spectrum sensing. This paper proposed a method using higher order statistics (HOS) functions including skewness and kurtosis for improving the sensing performance of a cyclic prefix (CP) based OFDM transmitted signal for noise uncertainty. The detection performance of OFDM systems is measured for various CP sizes using a higher order digital modulation technique over a multipath Rayleigh fading channel for low signal-to-noise ratio (SNR) cases. In the proposed method, the CP-based OFDM transmitted signal sensing performance is measured and compared with the conventional methods under noise uncertainty environments. Through comprehensive evaluation of simulation, it is demonstrated that the sensing performance of this method significantly outperforms conventional schemes in the case of noise uncertainty in severe noise environments. Manuscript profile