To obtain a position that facilitates my interest in teaching and performance for contributing to the research works of related area - Information & Communication Technology [ICT].

Wireless Mobile Communication, Cognitive Radio Networking, Multimedia and Virtual Reality, Computer Networking, Computer Graphics & Information Systems

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Extended the application area of Neural Network Computing in Biological data, Bio-Informatics, Text mining, Information Extraction, Pattern Recognition, Intelligence Communication Networking.

Ad-Hoc Networking, ATM Networking and Parallel Processing.

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I am currently serving as a faculty member in the Department of Information and Communication Technology at Islamic University, Kushtia-7003, Bangladesh. I have more than two decades of experience in teaching, research, and academic service in the field of Information and Communication Technology. My primary academic interests include computer networks, wireless communication, digital systems, medical image processing, artificial intelligence, and the Internet of Things (IoT). I am committed to providing quality education by combining theoretical knowledge with practical and research-oriented learning. I actively supervise undergraduate and postgraduate students in their academic projects and research activities. My research focuses on developing innovative and technology-driven solutions that address real-world challenges in healthcare, communication systems, and smart technologies. I have authored and reviewed scholarly articles published in national and international journals and conferences. I continuously strive to enhance my professional knowledge by engaging in research collaborations, academic writing, and lifelong learning. My goal is to contribute to the advancement of ICT education and research while inspiring students to become skilled professionals and responsible citizens. I believe that dedication, integrity, and continuous innovation are the keys to achieving excellence in teaching, research, and community service.

Wireless Communication, Integral Imaging, Image Processing, Machine Learning

https://www.researchgate.net/profile/Md-Islam-852?ev=hdr_xprf

https://scholar.google.com/citations?user=O21pYI0AAAAJ

Abstract:

The rapid evolution of mobile communication technologies from 4G to the envisioned 6G systems has substantially enhanced network capabilities, including data throughput, latency, connection density, and intelligence. Fourth generation (4G) networks brought broadband internet access with mobility, while fifth generation (5G) systems enabled ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC), and enhanced mobile broadband (eMBB). However, the accelerating demand for real-time data, intelligent applications, and seamless user experience is pushing the limits of 5G infrastructure. Sixth generation (6G) wireless networks are expected to operate in sub-terahertz (Sub-THz) and terahertz (THz) bands, supporting peak data rates up to 1 Tbps, latencies below 0.1 ms, and connection densities exceeding 10⁷ devices/km2. This paper presents a comprehensive comparative analysis of 4G, 5G, and 6G technologies based on key performance indicators such as user experience data rate, peak data rate, latency, connection density, and spectral efficiency. It also explores architectural trends and application domains unique to 6G, including holographic communication, immersive extended reality (XR), remote healthcare, and AI-native networking. The analysis offers valuable insights for researchers and industry stakeholders to better understand the technological evolution and challenges of the transition to 6G.

Abstract: In this paper, we analyzed Non-Orthogonal Multiple Access (NOMA) performance in the context of modern wireless cellular communications under various channel conditions. We examined different path loss models and multipath fading effects to evaluate the Bit Error Rate (BER) of NOMA. Additionally, this study was tailored for wireless communications involving multiple users in Rayleigh, Rician, AWGN, and Nakagami fading channels, all of which were considered within the framework of NOMA. To establish connections, ensure equitable user treatment, and achieve efficient spectrum utilization for multiple users across diverse channel scenarios, the Base Station (BS) adopted the NOMA technique. Furthermore, the BS employed a power allocation strategy to distribute available power among users based on their respective Power Allocation Factors (PAFs). This approach allowed users to simultaneously communicate with the BS within the same frequency range in the power domain. For users with lower PAFs, we utilized sequential interference cancellation to mitigate strong signals from other users. We validated our performance analysis using Monte-Carlo simulations, which closely aligned with the theoretical analysis. Our simulated results demonstrated that NOMA outperformed traditional Orthogonal Multiple Access (OMA) in most cases, especially in channels with various multipath fading conditions. Moreover, we observed that the effectiveness of NOMA hinged on the selection of power allocation and user pairing schemes. According to our study, NOMA exhibits significant potential to enhance the Spectral Efficiency (SE) and capacity of wireless networks in the future. We evaluated the accuracy of predicting received signal strength using various path loss models, including free space path loss, Log-normal path loss distribution, Shadowing path loss distribution, and Logdistance path loss model. In contrast to 4G networks, which are primarily affected by distancedependent path loss, 5G and beyond networks experience more significant fading effects due to their reliance on higher frequencies and wider bandwidths.  Keywords: NOMA, OMA, Fading Channels, BER, Successive Interference Cancellation (SIC), Signal attenuation   

Intelligent Reflecting Surfaces (IRSs) represents a pivotal component of technology, facilitating the enhancement of wireless communication performance and the manipulation of electromagnetic propagation environment. IRS technology has the remarkable capability to transform wireless channels from highly probabilistic to notably deterministic, effectively mitigating the substantial losses encountered in the millimeterwave (mmWave) band. Our analysis emphasizes how this innovative technology has ushered in a new era in wireless communications. Within the scope of this study, we delved into investigating the effectiveness of IRSassisted wireless transmissions across various scenarios, encompassing both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Our investigation involved the simulation of a 32×32 IRS array with a wavelength of 1 meter and an incident angle of 45 degrees. By manipulating the phase shifts of individual IRS elements, we examined their impact on achievable data rates concerning the number of elements. We also explored the relationship between throughput and separation distances, highlighting the significance of IRS placement in achieving optimal data rates. Channel capacity analysis was conducted for single IRS configurations with 50 and 100 elements, as well as dual IRS setups, shedding light on the capacity improvements achievable in different arrangements. Additionally, our study delved into Bit Error Rate (BER) performance in cooperative doubled IRS-aided wireless communication, employing a range of digital modulation techniques across various Signal-to-Noise Ratio (SNR) levels. This insight offers a valuable perspective on the reliability of IRS-aided systems across diverse modulation schemes. We also undertook a comprehensive Spectral Efficiency (SE) analysis, investigating IRS-assisted Multiple-Input, Single-Output (MISO) and Multiple-Input, Multiple-Output (MIMO) communications using various modulation schemes. Finally, we examined path loss characteristics across indoor encompassing different environments, especially at 20 GHz and 28 GHz using vertical to vertical (V-V) polarization. The culmination of this thorough simulation study underscores the tremendous potential of IRS technology in revolutionizing wireless communication across diverse scenarios, offering invaluable insights for future design and development endeavors

Multiput-Input, Multiple-Output (MIMO) systems have greatly improved data speed, connection stability, and spectrum efficiency, revolutionizing wireless communication. In cases where numerous fading channels coexist and the number of antennas at both the transmitter and receiver fluctuates, this paper examined the functionality of MIMO systems. In actual wireless settings, fading channels frequently occur, resulting in time-varying and spatially correlated channel characteristics. In this paper, we used a thorough analysis to investigate the capability of MIMO systems under some difficult circumstances. We considered circumstances with various numbers of antennas, channel correlation, and fading statistics, ranging from SingleInput, Single-Output (SISO) to MIMO setups. We investigated the impact of geographic diversity, Rayleigh, Rician, Nakagami fading models, and correlated fading channels on system performance. The trade-offs between the quantity of antennas, channel correlation, and capacity in MIMO systems are well illustrated by our findings. We presented our evidence in this paper to show that MIMO-NOMA is solely superior to MIMO-OMA in terms of total channel capacity, except in scenarios where communication is limited to one individual, with a power disparity for which MIMO-NOMA can achieve strictly larger rate pairs than MIMO-OMA. This study also explored the outage probability (OP) performance of MIMO-NOMA and MIMO-OMA systems in a massive MIMO communication scenario, including different fading channels. Based on these findings, we demonstrated that MIMONOMA can achieve a higher total ergodic capacity than MIMO-OMA.

As the range of customers in cellular era will increase at a geometrical rate, the call for and complexity of the provider is rising, making sure within side they want for cellular operators to shift the provider using difficulty to their core technology with the intention to switch extra information with excessive speed. The 5G cell networks is that the consequences of this paradigm shift and is currently being deployed in advanced nations regardless of the reality that they may be capable of put in force 6G cell networks. However, maximum of the growing nations in addition to Bangladesh ( Middle-earnings country) are, or will be, the usage of 3G or 4G cellular networks very lately for which the general position is not but complete. In this study, we are now exploring how a lot emphasis can be positioned on setting up the 5G cell networks in Bangladesh in addition to health effect of 4G and upcoming 5G cellular networks. We first take note of display that there may be capability for tackling foremost technological demanding situations in Bangladesh while putting in place the 5G cellular networks. The numerous protection factors of 5G cellular networks were provided that is an ongoing hassle and Bangladesh goes to technologically ready to deal with many protection issues of it. At the same time, it has been proven that the context of the usage of 5G networks in Bangladesh is relevant at present. As a result, putting in place the 5G networks in Bangladesh will take now not than expected. Finally, the outcomes of the usage of 4G, 5G cellular networks on human health had been presented.

Abstract:

Abstract: Based on Base Transceiver Station (BTS) and Mobile Station (MS) radiation, the residence of Kushtia district in Bangladesh is usually concerned with the potential health effects. Inhabitants, living in this part of Bangladesh are using communication, internet browsing, and other related uses with the help of mobile cellular communication system. The subscriber needed improving radio coverage, excellent communication networks, and quicker speed for internet browsing with the recent developments in mobile cellular communication technologies. To meet this demand, service providers of mobile cellular companies set up several BTS, which emit harmful radiation that has extremely detrimental impact on the human body. Through this paper, the effects of electromagnetic radiation of MS as well as BTS on the human body have been analyzed and Electromagnetic Field (EMF) radiation levels of MSs along with BTSs located in this region have been measured. These measurements have been compared with the measured value provided by the World Health Organization (WHO) and International Commission of Non-Ionizing Radiation Protection (ICNIRP). Finally, a cross-sectional analysis was carried out to evaluate the understanding and self-reported health risks of Electromagnetic waves (EMW) radiation emitted from the MS as well as BTS among the inhabitants of this region who are living near and a little away from BTS. Keywords: Mobile Cellular Communications, Base Transceiver Station (BTS), Mobile Station (MS), Electromagnetic field (EMF), Ionizing and Non-Ionizing Radiation

The prime aspect of this paper is to report the atmospheric impairment effects in wireless communication signalfor different frequencies. Comprehensive tests have been carried out to justify and confront theattenuation impacts of rain, cloud and water vapor oxygen for various wave length channels. The result of the atmospheric impairments is obtained by the parameters which are resembled to the real world measurements. Moreover, the performance is analyzed by the use of different propagation modules and transmission parameters that are frequently used in satellite and radar communications. Experimental resultsindicate that the attenuation effect due to rain has initiated at frequencies above 10 GHz while, other atmospheric effects preceded by attenuation of the cloud and gasses have less effect. It is also observed thatduring frequency transmission, the rate of rainfall and relative humidity become directly proportional to signal quality degradation.

The paper deals with the study based on the comparative analysis of radio propagation models for mobile cellular wireless communication of global system for mobile at frequencies 0.9 GHz and 1.8 GHz, respectively. The path loss propagation models arevital tool for planning the wireless network as well as designed to predict path loss in a meticulous environment. Various propagation models: Free-space model, CCIR (ITU-R) model, Hata model, Ericson model, and Stanford University Interim (SUI) model have been studied and examined through analytically from the base station (BS) to mobile station (MS) and vice versa followed by respective simulation performance evaluation by using Matlab simulator. The observed data is collected at the operating frequency of 0.9 GHz from various environments (high density region and low density region) using the spectrum analyzer and path loss comparison is shown for different model.

There are many routing protocols of these DSR, DSDV performance has been shown under different environment through this paper. 

In this paper an attemppt has been made to compare three routing protocols on the basis of performance under different environments. The comparison has been done under the UDP payload (CBR traffic).

This study presents a three-dimensioanl (3-D) Integral Imaging system to improve concurently the viewing angle, Depth of Focus, and Resolution of the reconstructed object images by using multiprojections and nonuniform lenslets array with Intermediate-View Reconstruction Method (IVRM). 

The main goal of this article is analyzing the recent work in multiple objects tracking to handle those challenges. 

 Through this paper one of the limitation of Integral Imaging three-dimensional (3D) display system is enhanced (Viewing angle. 

This paper presents a Computer Generated Integral Imaging (CGII) system that makes better the visual quality of three-dimensioanl (3D) reconstructed images based on real depth of 3D object. 

This paper delas with a three-dimensional Integral Imaging systen to improve viewing angle by using multiple illuminations. 

In this paper we proposed a three-dimensional (3-D) Integral Imaging (II) system using double layer structure microlens array in place of conventional microlens array. 

The main goal of this work is to optimization of facial feature point by establishing a mathematical relationship among facial features and used optimize feature points for age classification. 

ABSTRACT In this paper, a system, developed for speech encoding, analysis, synthesis and gender identification is presented. A typical gender recognition system can be divided into front-end system and back-end system. The task of the front-end system is to extract the gender related information from a speech signal and represents it by a set of vectors called feature. Features like power spectrum density, frequency at maximum power carry speaker information. The feature is extracted using First Fourier Transform (FFT) algorithm. The task of the back-end system (also called classifier) is to create a gender model to recognize the gender from his/her speech signal in recognition phase. This paper also presents the digital processing of a speech signals (pronounced “A” and “B”) which are taken from 10 persons, 5 of them are Male and the rest of them are Female.  Power Spectrum Estimation of the signal is examined .The frequency at maximum power of the English Phonemes is extracted from the estimated power spectrum. The system uses threshold technique as identification tool. The recognition accuracy of this system is 80% on average. KEYWORDS Gender Recognition, Feature Extraction, First Fourier Transform (FFT), Font-end, Back-end. 

In this paper a system developed for speech encoding, analysis, synthesis and gender identification is presented 

ABSTRACT This paper proposes a Multi-Code Multi-Carrier Code Division Multiple Access (CDMA) scheme for next generation wireless communication systems. This system retains the advantages of Multi-Carrier CDMA in combating multipath and rejecting interference, and provides variable and adaptive data rates through the use of Multi-Code scheme.  The rate adaptation algorithm proposed computes the user data rate as a function of the instantaneous channel condition and application dependent data rates. Exploiting the channel information improves the effective data rate and overall capacity of the system. The performance improvement of the proposed system to the Multi-Code CDMA system is shown through simulations. Walsh-Hadamard, Gold and Kasami codes are explored as possible choices for the multiple codes in the system.  KEYWORDS AWGN, MC-CDMA, OFDM. 

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I'm Professor Dr. Md. Sipon Miah [PhD (Ireland), Postdoc (Spain)] Faculty member of Information & Communication Technology (ICT), Islamic University, Kushtia-7003, Bangladesh.

Wireless Communications, Wireless Sensor Networks, Cognitive Radio Networks, RF Energy Harvesting, Cloud Computing, Internet of Things, Machine Learning, etc.

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https://scholar.google.com/citations?user=XhP8D5oAAAAJ&hl=en&oi=ao

Md. Sipon Miah (Co-Author)

Abstract-For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO).

Md Sipon Miah (Corresponding Author)

Abstract- Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum, to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. In this paper, we address the issues of enhancing sensing gain, average throughput, energy consumption, and network lifetime in a cognitive radio-based Internet of things (CR-IoT) network using the non-sequential approach. As a solution, we propose a Dempster–Shafer theory-based throughput analysis of an energy-efficient spectrum sensing scheme for a heterogeneous CR-IoT network using the sequential approach, which utilizes firstly the signal-to-noise ratio (SNR) to evaluate the degree of reliability and secondly the time slot of reporting to merge as a flexible time slot of sensing to more efficiently assess spectrum sensing. Before a global decision is made on the basis of both the soft decision fusion rule like the Dempster–Shafer theory and hard decision fusion rule like the “n-out-of-k” rule at the fusion center, a flexible time slot of sensing is added to adjust its measuring result. Using the proposed Dempster–Shafer theory, evidence is aggregated during the time slot of reporting and then a global decision is made at the fusion center. In addition, the throughput of the proposed scheme using the sequential approach is analyzed based on both the soft decision fusion rule and hard decision fusion rule. Simulation results indicate that the new approach improves primary user sensing accuracy by 13% over previous approaches, while concurrently increasing detection probability and decreasing false alarm probability. It also improves overall throughput, reduces energy consumption, prolongs expected lifetime, and reduces global error probability compared to the previous approaches under any condition [part of this paper was presented at the EuCAP2018 conference (Md. Sipon Miah et al. 2018)].

Md. Sipon Miah (Co-author)

Abstract- The COVID-19 outbreak resulted in preventative measures and restrictions for Bangladesh during the summer of 2020—these unstable and stressful times led to multiple social problems (e.g., domestic violence and divorce). Globally, researchers, policymakers, governments, and civil societies have been concerned about the increase in domestic violence against women and children during the ongoing COVID-19 pandemic. In Bangladesh, domestic violence against women and children has increased during the COVID-19 pandemic. In this article, we investigated family violence among 511 families during the COVID-19 outbreak. Participants were given questionnaires to answer, for a period of over ten days; we predicted family violence using a machine learning-based model. To predict domestic violence from our data set, we applied random forest, logistic regression, and Naive Bayes machine learning algorithms to our model. We employed an oversampling strategy named the Synthetic Minority Oversampling Technique (SMOTE) and the chi-squared statistical test to, respectively, solve the imbalance problem and discover the feature importance of our data set. The performances of the machine learning algorithms were evaluated based on accuracy, precision, recall, and F-score criteria. Finally, the receiver operating characteristic (ROC) and confusion matrices were developed and analyzed for three algorithms. On average, our model, with the random forest, logistic regression, and Naive Bayes algorithms, predicted family violence with 77%, 69%, and 62% accuracy for our data set. The findings of this study indicate that domestic violence has increased and is highly related to two features: family income level during the COVID-19 pandemic and education level of the family members.

Md. Sipon Miah (Corresponding author)

Abstract- The Cognitive Radio based Internet of Things (CR-IoT) is a promising technology that provides IoT endpoints, i.e., CR-IoT users the capability to share the radio spectrum otherwise allocated to licensed Primary Users (PUs). Cooperative Spectrum Sensing (CSS) improves spectrum sensing accuracy in a CR-IoT network. However, its performance may be degraded by potential attacks of the malicious CR-IoT users that send their incorrect sensing information to the corresponding Fusion Center (FC). This study presents a promising Machine Learning (ML)-based malicious user detection scheme for a CR-IoT network that uses a Support Vector Machine (SVM) algorithm to identify and classify malicious CR-IoT users. The classification allows the FC to make a more robust global decision based on the sensing results (i.e., energy vectors) which are reported only by the normal CR-IoT users. The effectiveness of the proposed SVM algorithm based ML in a CR-IoT network with the malicious CR-IoT users is verified via simulations.

 Md. Sipon Miah (Co-author)

Md. Sipon Miah (Co-author)

Abstract- Skin cancer is one of the top three perilous types of cancer caused by damaged DNA that can cause death. This damaged DNA begins cells to grow uncontrollably and nowadays it is getting increased speedily. There exist some researches for the computerized analysis of malignancy in skin lesion images. However, analysis of these images is very challenging having some troublesome factors like light reflections from the skin surface, variations in color illumination, different shapes, and sizes of the lesions. As a result, evidential automatic recognition of skin cancer is valuable to build up the accuracy and proficiency of pathologists in the early stages. In this paper, we propose a deep convolutional neural network (DCNN) model based on deep learning approach for the accurate classification between benign and malignant skin lesions. In preprocessing we firstly, apply filter or kernel to remove noise and artifacts; secondly, normalize the input images and extract features that help for accurate classification; and finally, data augmentation increases the number of images that improves the accuracy of classification rate. To evaluate the performance of our proposed, DCNN model is compared with some transfer learning models such as AlexNet, ResNet, VGG-16, DenseNet, MobileNet, etc. The model is evaluated on the HAM10000 dataset and ultimately we obtained the highest 93.16% of training and 91.93% of testing accuracy respectively. The final outcomes of our proposed DCNN model define it as more reliable and robust when compared with existing transfer learning models.

Md. Sipon Miah (First author & Corresponding author)

Absract- Spectrum sensing plays a vital role in cognitive radio networks (CRNs) for identifying the spectrum hole. However, an individual cognitive radio user in a CRN does not obtain sufficient sensing performance and sum rate of the primary and secondary links to support the future Internet of Things (IoT) using conventional detection techniques such as the energy detection (ED) technique in a noise-uncertain environment. In an environment comprising noise uncertainty, the performance of conventional energy detection techniques is significantly degraded owing to the noise fluctuation caused by the noise temperature, interference, and filtering. To mitigate this problem, we present a cooperative spectrum sensing technique that comprises the use of the Kullback–Leibler divergence (KLD) in cognitive radio-based IoT (CR-IoT). In the proposed method, each unlicensed IoT device that is capable of spectrum sensing, which is called a CR-IoT user, makes a local decision using the KLD technique. The spectrum sensing performed with the KLD requires a smaller number of samples than other conventional approaches, e.g., energy detection, for reliable sensing even in a noise uncertain environment. After the local decision is made, each CR-IoT user sends its own local decision result to the corresponding fusion center, which makes a global decision using the soft fusion rule. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, i.e., higher detection and lower false-alarm probabilities, enhances the sum rate, and reduces the total time as compared to the conventional ED scheme under various fading channels.

Md. Sipon Miah (First author)

Absract- State-of-the-art energy detection (ED) based spectrum sensing requires perfect knowledge of noise power and is vulnerable to noise uncertainty. An eigenvalue-based spectrum sensing approach performs well in such an uncertain environment, but does not mitigate the spectrum scarcity problem, which evolves with the future Internet of Things (IoT) rollout. In this paper, we propose a multi-user multiple-input and multiple-output (MU-MIMO) based cognitive radio scheme for the Internet of Things (CR-IoT) with weighted-eigenvalue detection (WEVD) for the analysis of sensing, system throughput, energy efficiency and expected lifetime. In this scheme, each CR-IoT user is being equipped with MIMO antennas; we calculate the WEVD ratio, which is defined as the ratio between the difference of the maximum eigenvalue and minimum eigenvalue to the sum of the maximum eigenvalue and minimum eigenvalue. This mitigates against the spectrum scarcity problem, enhances system throughput, improves energy efficiency, prolongs expected lifetime and lowers error probability. Simulation results confirm the effectiveness of the proposed scheme; here the WEVD technique demonstrates a better detection gain and enhanced system throughput in comparison to the conventional scheme with eigenvalue based detection (EVD) and ED techniques in a noise uncertainty environment (i.e. SNR <-28). Furthermore, the proposed scheme has a lower energy consumption, prolonged expected lifetime and achieves a low error probability when compared with other schemes like the conventional single-input and single-output (SISO) based CR-IoT scheme with EVD and ED spectrum sensing.

Md. Sipon Miah* (Corresponding Author)

Abstract- In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human brain tumors in a magnetic resonance imaging (MRI) image. In this proposed algorithm, firstly, the template-based K-means algorithm is used to initialize segmentation significantly through the perfect selection of a template, based on gray-level intensity of image; secondly, the updated membership is determined by the distances from cluster centroid to cluster data points using the fuzzy C-means (FCM) algorithm while it contacts its best result, and finally, the improved FCM clustering algorithm is used for detecting tumor position by updating membership function that is obtained based on the different features of tumor image including Contrast, Energy, Dissimilarity, Homogeneity, Entropy, and Correlation. Simulation results show that the proposed algorithm achieves better detection of abnormal and normal tissues in the human brain under small detachment of gray-level intensity. In addition, this algorithm detects human brain tumors within a very short time—in seconds compared to minutes with other algorithms.

Md. Sipon Miah* (Corresponding Author)

Absract- In this paper, we proposed the unscented Kalman filter (UKF) based on cooperative spectrum sensing (CSS) scheme in a cognitive radio network (CRN) using an adaptive fuzzy system—in this proposed scheme, firstly, the UKF to apply the nonlinear system which is used to minimize the mean square estimation error; secondly, an adaptive fuzzy logic rule based on an inference engine to estimate the local decisions to detect a licensed primary user (PU) that is applied at the fusion center (FC). After that, the FC makes a global decision by using a defuzzification procedure based on a proposed algorithm. Simulation results show that the proposed scheme achieved better detection gain than the conventional schemes like an equal gain combining (EGC) based soft fusion rule and a Kalman filter (KL) based soft fusion rule under any conditions. Moreover, the proposed scheme achieved the lowest global probability of error compared to both the conventional EGC and KF schemes.

Md. Sipon Miah (First author)

Absract- The cognitive radio relay plays a vital role in cognitive radio networking (CRN), as it can improve the cognitive sum rate, extend the coverage, and improve the spectral efficiency. However, cognitive relay aided CRNs cannot obtain a maximal sum rate, when the existing sensing approach is applied to a CRN. In this paper, we present an enhanced sum rate in the cluster based cognitive radio relay network utilizing a reporting framework in the sequential approach. In this approach a secondary user (SU) extends its sensing time until right before the beginning of its reporting time slot by utilizing the reporting framework. Secondly all the individual measurement results from each relay aided SU are passed on to the corresponding cluster head (CH) through a noisy reporting channel, while the CH with a soft-fusion report is forwarded to the fusion center that provides the final decision using the n-out-of-k-rule. With such extended sensing intervals and amplified reporting, a better sensing performance can be obtained than with a conventional non-sequential approach, therefore making it applicable for the future Internet of Things. In addition, the sum rate of the primary network and CCRRN are also investigated for the utilization reporting framework in the sequential approach with a relay using the n-out-of-k rule. By simulation, we show that the proposed sequential approach with a relay (Lemma 2) provides a significant sum rate gain compared to the conventional non-sequential approach with no relay (Lemma 1) under any condition.

Md. Sipon Miah (First author)

Md. Sipon Miah (First author)

Md. Sipon Miah (First author)

Md. Sipon Miah (First author)

Md. Sipon Miah (First author)

Md Sipon Miah (Co-author)

Abstract- The purpose of this paper is to investigate the performance of UDP over various routing protocols in ad hoc networks. For this investigation we have chosen three routing schemes, DSDV, DSR and AODV and four network scenarios of 4, 8, 16 and 32 nodes of various node mobility speeds. Results are produced by evaluating throughput and end to end packet delay over the UDP connection through simulation experiments.

Data Privacy and Security, Machine Learning, Big Data Analytics, Online Social Networks Analysis, and Blockchain Technology

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https://scholar.google.com/citations?user=ZO0t9ZcAAAAJ&hl=en

Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods.

Human civilization is experiencing a critical situation that presents itself for a new coronavirus disease 2019 (COVID-19). This virus emerged in late December 2019 in Wuhan city, Hubei, China. The grim fact of COVID-19 is, it is highly contagious in nature, therefore, spreads rapidly all over the world and causes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Responding to the severity of COVID-19 research community directs the attention to the analysis of COVID-19, to diminish its antagonistic impact towards society. Numerous studies claim that the subcontinent, i.e., Bangladesh, India, and Pakistan, could remain in the worst affected region by the COVID-19. In order to prevent the spread of COVID-19, it is important to predict the trend of COVID-19 beforehand the planning of effective control strategies. Fundamentally, the idea is to dependably estimate the reproduction number to judge the spread rate of COVID-19 in a particular region. Consequently, this paper uses publicly available epidemiological data of Bangladesh, India, and Pakistan to estimate the reproduction numbers. More specifically, we use various models (for example, susceptible infection recovery (SIR), exponential growth (EG), sequential Bayesian (SB), maximum likelihood (ML) and time dependent (TD)) to estimate the reproduction numbers and observe the model fitness in the corresponding data set. Experimental results show that the reproduction numbers produced by these models are greater than 1.2 (approximately) indicates that COVID-19 is gradually spreading in the subcontinent.

A smart environment consisting of sensors and actuators that network a physical space makes the lives of users extremely convenient. In such environments, service providers collect, store, and process a huge amount of personal information from the users, and provide appropriate services in return. Processing personal information helps service providers dispense personalized services to users. The flow of personal information requires legal documents such as privacy preferences and privacy policies from the users and service providers, respectively. A software agent (to be installed either at the client’s or server’s end) performs the privacy checking (i.e., matches the service provider’s privacy policies with the user’s privacy preferences), and produces a decision based on a “take it or leave it” approach. Often, this check is too restrictive such that the user is denied the services, even if the privacy policies partially satisfy the user preferences. In this work, the privacy checking problem is envisaged as a knapsack problem, and to address it, we derive (i) knapsack privacy checking (KPC), and (ii) knapsack graph-based privacy checking (KPC-G) techniques. The system developed using these techniques finds the services that best match the user’s privacy preferences as well as the service provider’s policies, with a twofold functionality: (a) it recommends the best matching services based on user-supplied tolerance values, or (b) it solves an optimization problem to suggest services after adjusting the user-supplied tolerance values. Our experimental results demonstrate the effectiveness of the proposed model.

The university admission tests find the applicant's ability to admit to the desired university. Nowadays, there is a huge competition in the university admission tests. The failure in the admission tests makes an examinee depressed. This paper proposes a method that predicts undergraduate admission in universities. It can help students to improve their preparation to get a chance at their desired university. Many factors are responsible for the failure or success in an admission test. Educational data mining helps us to analyze and extract information from these factors. Here, the authors apply three machine learning algorithms XGBoost, LightGBM, and GBM on a collected dataset to estimate the probability of getting admission to the university after attending or before attending the admission test. They also evaluate and compare the performance levels of these three algorithms based on two different evaluation metrics – accuracy and F1 score. Furthermore, the authors explore the important factors which influence predicting undergraduate admission.

Automated social behavior analysis in the mammalian animal has become an increasingly popular and attractive alternative to traditional manual human annotation with the advancement of machine learning and video tracking system for automatic detection. In this work, we study a framework of how different features perform on the different classifiers to analyze automatic mice behavior. We conducted experiments on the Caltech Resident-Intruder Mouse (CRIM13) dataset, which provides two types of features: trajectory features and spatio-temporal features. With this feature, we train AdaBoost and Random Decision Forest (TreeBagger) classifiers to classify different mouse behaviors to show which features perform best on which classifier. The experimental result shows that the trajectory features are more informative and provide better accuracy than the widely used spatio-temporal features, and AdaBoost classifier shows better performance than the TreeBagger on these features.

In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human brain tumors in a magnetic resonance imaging (MRI) image. In this proposed algorithm, firstly, the template-based K-means algorithm is used to initialize segmentation significantly through the perfect selection of a template, based on gray-level intensity of image; secondly, the updated membership is determined by the distances from cluster centroid to cluster data points using the fuzzy C-means (FCM) algorithm while it contacts its best result, and finally, the improved FCM clustering algorithm is used for detecting tumor position by updating membership function that is obtained based on the different features of tumor image including Contrast, Energy, Dissimilarity, Homogeneity, Entropy, and Correlation. Simulation results show that the proposed algorithm achieves better detection of abnormal and normal tissues in the human brain under small detachment of gray-level intensity. In addition, this algorithm detects human brain tumors within a very short time—in seconds compared to minutes with other algorithms.

Recently, Personal Data Storage (PDS) has inaugurated a substantial change to the way people can store and control their personal data, by moving from a service-centric to a user-centric model. PDS offers individuals the capability to keep their data in a unique logical repository, that can be connected and exploited by proper analytical tools, or shared with third parties under the control of end users. Up to now, most of the research on PDS has focused on how to enforce user privacy preferences and how to secure data when stored into the PDS. In contrast, in this paper we aim at designing a Privacy-aware Personal Data Storage (P-PDS), that is, a PDS able to automatically take privacy-aware decisions on third parties access requests in accordance with user preferences. The proposed P-PDS is based on preliminary results presented in [1], where it has been demonstrated that semi-supervised learning can be successfully exploited to make a PDS able to automatically decide whether an access request has to be authorized or not. In this paper, we have deeply revised the learning process so as to have a more usable P-PDS, in terms of reduced effort for the training phase, as well as a more conservative approach w.r.t. users privacy, when handling conflicting access requests. We run several experiments on a realistic dataset exploiting a group of 360 evaluators. The obtained results show the effectiveness of the proposed approach.

The concept of Personal Data Storage (PDS) has recently emerged as an alternative and innovative way of managing personal data w.r.t. the service-centric one commonly used today. The PDS offers a unique logical repository, allowing individuals to collect, store, and give access to their data to third parties. The research on PDS has so far mainly focused on the enforcement mechanisms, that is, on how user privacy preferences can be enforced. In contrast, the fundamental issue of preference specification has been so far not deeply investigated. In this paper, we do a step in this direction by proposing different learning algorithms that allow a fine-grained learning of the privacy aptitudes of PDS owners. The learned models are then used to answer third party access requests. The extensive experiments we have performed show the effectiveness of the proposed approach.

Personal data storages (PDSs) give individuals the ability to store their personal data in a data unified repository and control release of their data to data consumers. Being able to gather personal data from different data sources (e.g., banks, hospitals), PDSs will play strategic role in individual privacy management. As such, PDS demands for new privacy models for protecting personal data. In this paper, we propose a new technical approach that empowers individuals to better control data in PDS. Particularly, we present a privacy-aware PDS architecture by focusing on two logical data zones based on the categories of personal data. Moreover, we propose a strategy for regulating personal data release that takes in consideration both user preferences and possible risks and benefits of the data release.

Studies on friendships in online social networks involving geographic distance have so far relied on the city location provided in users’ profiles. Consequently, most of the research on friendships has provided accuracy at the city level, at best, to designate a user’s location. This study analyses a Twitter dataset because it provides the exact geographic distance between corresponding users. We start by introducing a strong definition of ‘friend’ on Twitter (i.e. a definition of bidirectional friendship), requiring bidirectional communication. Next, we utilize geo-tagged mentions delivered by users to determine their locations, where ‘@username’ is contained anywhere in the body of tweets. To provide analysis results, we first introduce a friend-counting algorithm. From the fact that Twitter users are likely to post consecutive tweets in the static mode, we also introduce a two-stage distance-estimation algorithm. As the first of our main contributions, we verify that the number of friends of a particular Twitter user follows a well-known power-law distribution (i.e. a Zipf’s distribution or a Pareto distribution). Our study also provides the following newly discovered friendship degree related to the issue of space: the number of friends according to distance follows a double power-law (i.e. a double Pareto law) distribution, indicating that the probability of befriending a particular Twitter user is significantly reduced beyond a certain geographic distance between users, termed the separation point. Our analysis provides concrete evidence that Twitter can be a useful platform for assigning a more accurate scalar value to the degree of friendship between two users.

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Computer Vision, Image Processing, Deep Learning, Machine Learning

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https://scholar.google.com/citations?user=B0kyLGkAAAAJ&hl=en

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MANET, BigData, IoT, Blockchain Communication

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