Showing papers by "University Institute of Technology, Burdwan University published in 2021"

Deep Bidirectional Classification Model for COVID-19 Disease Infected Patients

Abstract: In December of 2019, a novel coronavirus (COVID-19) appeared in Wuhan city, China and has been reported in many countries with millions of people infected within only four months. Chest computed Tomography (CT) has proven to be a useful supplement to reverse transcription polymerase chain reaction (RT-PCR) and has been shown to have high sensitivity to diagnose this condition. Therefore, radiological examinations are becoming crucial in early examination of COVID-19 infection. Currently, CT findings have already been suggested as an important evidence for scientific examination of COVID-19 in Hubei, China. However, classification of patient from chest CT images is not an easy task. Therefore, in this paper, a deep bidirectional long short-term memory network with mixture density network (DBM) model is proposed. To tune the hyperparameters of the DBM model, a Memetic Adaptive Differential Evolution (MADE) algorithm is used. Extensive experiments are drawn by considering the benchmark chest-Computed Tomography (chest-CT) images datasets. Comparative analysis reveals that the proposed MADE-DBM model outperforms the competitive COVID-19 classification approaches in terms of various performance metrics. Therefore, the proposed MADE-DBM model can be used in real-time COVID-19 classification systems.

Dynamical Properties of Ion-Acoustic Waves in Space Plasma and Its Application to Image Encryption

Abstract: The nonlinear ion-acoustic waves (IAWs) in a space plasma are capable of exhibiting chaotic dynamics which can be applied to cryptography. Dynamical properties of IAWs are examined using the direct method in plasmas composed of positive and negative ions and nonextensive distributed electrons. Applying the wave transformation, the governing equations are deduced into a dynamical system (DS). Supernonlinear and nonlinear periodic IAWs are presented through phase plane analysis. The analytical periodic wave solution for IAW is obtained. Under the influence of an external periodic force, the DS is transformed to a perturbed system. The perturbed DS describes multistability property of IAWs with change of initial conditions. The multistability behavior features coexisting trajectories such as, quasiperiodic, multiperiodic and chaotic trajectories of the perturbed DS. The chaotic feature in the perturbed DS is supported by Lyapunov exponents. This interesting behavior in the windows of chaotic dynamics is exploited to design efficient encryption algorithm. First SHA-512 is used to compute the hash digest of the plain image which is then used to update the initial seed of the chaotic IAWs system. Note that SHA-512 uses one-way function to map input data to the output, consequently it is quite impossible to break the proposed encryption technique. Second DNA coding is used to confuse and diffuse the DNA version of the plain image. The diffused image follows DNA decoding process leading to the cipher image. The security performance is evaluated using some well-known metrics and results indicate that the proposed cryptosystem can resist most of existing cryptanalysis techniques. In addition complexity analysis shows the possibility of practical implementation of the proposed algorithm.

Taxonomy on EEG Artifacts Removal Methods, Issues, and Healthcare Applications

Abstract: Electroencephalogram (EEG) signals are progressively growing data widely known as biomedical big data, which is applied in biomedical and healthcare research. The measurement and processing of EEG ...

Synthesis, functionalization, and environmental application of silica-based mesoporous materials of the M41S and SBA-n families: A review

Abstract: The functionalized silica-based mesoporous molecular sieves are widely used successfully in various applications, mainly due to their interesting and unique textural and structural features, which allow their use in the most different scientific areas, such as catalysis, adsorption, separation of target molecules, drug delivery devices, chemosensors, biosensors, so on. In this review, we report the state of the art of recent advancements on the modified, inspired, and architectured M41S- and SBA-n-based mesoporous arrays syntheses and its strategic use in the environmental approaches, mainly due to the great interest and need of the scientific community to obtain functionalized arrays with multiple features. In view of the different approaches that have been extensively explored for the functionalization of nano-architectured arrays presented in the literature, it is possible to state that the strategies of modification of the M41S- and SBA-n-based mesoporous arrays can enhance the adsorptive, catalytic, and separation properties of the functionalized mesoporous architectures. Thus, evidencing that these multifunctional materials synergistically modified, with different pore geometries and architectures, can be widely used in the environmental remediation area, aiming to minimize environmental impacts, improve quality of life, and avoid the generation of waste, aiming at an increasingly green, clean, and sustainable footprint in the synthesis and application processes of these multifunctional materials.

A malaria model with Caputo-Fabrizio and Atangana-Baleanu derivatives

Abstract: In this paper, we study two fractional models in the Caputo–Fabrizio sense and Atangana–Baleanu sense, in which the effects of malaria infection on mosquito biting behavior and attractiveness of hu...

Prediction studies of the epidemic peak of coronavirus disease in Brazil via new generalised Caputo type fractional derivatives

Abstract: The first reported case of coronavirus disease (COVID-19) in Brazil was confirmed on 25 February 2020 and then the number of symptomatic cases produced day by day. In this manuscript, we studied the epidemic peaks of the novel coronavirus (COVID-19) in Brazil by the successful application of Predictor–Corrector (P-C) scheme. For the proposed model of COVID-19, the numerical solutions are performed by a model framework of the recent generalized Caputo type non-classical derivative. Existence of unique solution of the given non-linear problem is presented in terms of theorems. A new analysis of epidemic peaks in Brazil with the help of parameter values cited from a real data is effectuated. Graphical simulations show the obtained results to classify the importance of the classes of projected model. We observed that the proposed fractional technique is smoothly work in the coding and very easy to implement for the model of non-linear equations. By this study we tried to exemplify the roll of newly proposed fractional derivatives in mathematical epidemiology. The main purpose of this paper is to predict the epidemic peak of COVID-19 in Brazil at different transmission rates. We have also attempted to give the stability analysis of the proposed numerical technique by the reminder of some important lemmas. At last we concluded that when the infection rate increases then the nature of the diseases changes by becoming more deathly to the population.

A mathematical study of a tuberculosis model with fractional derivatives

Abstract: In this work, we use a Predictor–Corrector method to implement and derive an iterative solution of an existing Tuberculosis (TB) model with two fractional derivatives, namely, Caputo–Fabrizio fract

Mechanical and physical performance of date palm/bamboo fibre reinforced epoxy hybrid composites

Abstract: Date palm fibre is considered as an agricultural waste suitable as reinforcement for polymeric composites has received interest as an interior component in the automotive industry. The aim of this work to investigate the physical and mechanical properties of date palm fibre (leaf stalk, tree trunk, fruit stalk and leaf sheath) reinforced epoxy composites and date palm/bamboo hybrid composites suitable for non-structural and semi-structural applications. The different composites were fabricated using a hand lay-up technique in combination with compression moulding method. Date palm fibre/bamboo hybrid composite, and pure composites were characterized by various mechanical testing such as tensile, flexural and low-velocity impact testing. Exposure to liquid water and environmental conditions affect the durability of biobased composites. The water absorption behaviour and its effects on the thickness swelling, changes in density and physical performance of different composites were further investigated. The experimental results of the date palm fibre/bamboo hybrid composite exhibited the highest mechanical properties compared to date palm fibre composite without hybridisation. The tensile strength, flexural strength and impact toughness values for the date palm/bamboo hybrid composite were recorded at 39.16 MPa, 61.10 MPa and 12.70 J/m, respectively. Interestingly, the physical test of thickness swelling and water absorption results has indicated that date palm fibre/bamboo hybrid composite displayed 27.68% and 15.39% less when compared with single date palm fibre composite. Therefore, the results have discovered that the date palm fibre/bamboo hybrid composite has the prospective to be applied as a non-structural and semi-structural components.

SARS-CoV-2 neutralising antibody testing in Europe: towards harmonisation of neutralising antibody titres for better use of convalescent plasma and comparability of trial data.

Abstract: We compared the performance of SARS-CoV-2 neutralising antibody testing between 12 European laboratories involved in convalescent plasma trials. Raw titres differed almost 100-fold differences between laboratories when blind-testing 15 plasma samples. Calibration of titres in relation to the reference reagent and standard curve obtained by testing a dilution series reduced the inter-laboratory variability ca 10-fold. The harmonisation of neutralising antibody quantification is a vital step towards determining the protective and therapeutic levels of neutralising antibodies.

Calcium oxide nanoparticles synthesis from hen eggshells for removal of lead (Pb(II)) from aqueous solution

Abstract: Lead is an important industrial heavy metal used in various production industries. Remediation of Lead poisoned areas has both economical and technological challenges, as conventional and techniques are very expensive to apply for wastewater treatment and its operation is difficult. The adsorption method could solve the problem using sol-gel-based synthesized adsorbent since it is environmentally friendly with high-quality product produced. In the present study, the application of synthesized calcium oxide nanoparticles from hen eggshells for the removal of lead ions from aqueous solutions is what was investigated. Characterization of the adsorbent like proximate analysis, particle density, bulk density, porosity, point of zero charges, Fourier transform infrared radiation spectroscope, X-ray diffraction, specific surface area, thermal gravimetric analysis, and scanning electron microscopy was done before batch adsorption experiments. X-ray diffraction revealed that the size of synthesized calcium oxide nanoparticles was 24.34 nm and the specific surface area was 77.4m2/g. The removal of divalent lead ions from aqueous solutions was optimized by using response surface methodology. The optimum percent removal of lead (99.07) has resulted at initial concentration 75.46 ppm, pH 6.94, adsorbent dose 0.838 g, and contact time 101.97 min. The experimental removal efficiency (98.86%) agreed very well with the predicted one (99.07%), showing the suitability of the model used and the success of response surface methodology in optimizing of removal of Pb (II) ions from aqueous solutions. The lead ions removal was well fitted into the Langmuir isotherm model with correlation coefficients of 0.9963. The adsorption kinetic data were well fitted with the pseudo-second-order model with a correlation coefficient of 0.9982. The pseudo-second-order model was the rate-limiting step in the lead (II) ions adsorption process onto CaO NPs. Based on the obtained results, the calcium oxide nanoparticles prepared from eggshell have a good capacity for the removal of the lead ions from the aqueous solutions.

Linear stability analysis of (Cu-Al2O3)/water hybrid nanofluid flow in porous media in presence of hydromagnetic, small suction and injection effects

Abstract: In this paper, a stability analysis of a hybrid nanofluid flow between two parallel and stationnary plates filled with a porous medium was investigated. The hybrid nanofluid is composed of water as regular fluid, copper ( Cu ) and alumina ( Al 2 O 3 ) as nanoparticles. A mathematical modeling of the problem was developed by taking account other effects such as aspiration (suction/injection) and magnetic field. An eigenvalue differential equation namely the modified Orr-Sommerfeld equation governing the stability of the flow was derived and solved numerically by spectral collocation method with expansions in Chebyshev polynomials. The effect of the density of particles, suction/injection Reynolds number, Hartmann number, Darcy number and volume fraction on the flow stability was examined and presented. It was found the following: the Darcy number affects the stability of the flow, the suction/injection reduces the drag and the transition is delayed/prevented, the magnetic field makes the dissipation very important because the kinetic energy of the electrically conductive fluid is absorbed by the Lorentz force, and the volume fraction and the density of nanoparticles increased the inertia of the fluid which decreased the speed gradient and damped the disturbances.

Multistability and circuit implementation of tabu learning two-neuron model: application to secure biomedical images in IoMT

Abstract: In this paper, the dynamics of a non-autonomous tabu learning two-neuron model is investigated. The model is obtained by building a tabu learning two-neuron (TLTN) model with a composite hyperbolic tangent function consisting of three hyperbolic tangent functions with different offsets. The possibility to adjust the compound activation function is exploited to report the sensitivity of non-trivial equilibrium points with respect to the parameters. Analysis tools like bifurcation diagram, Lyapunov exponents, phase portraits, and basin of attraction are used to explore various windows in which the neuron model under the consideration displays the uncovered phenomenon of the coexistence of up to six disconnected stable states for the same set of system parameters in a TLTN. In addition to the multistability, nonlinear phenomena such as period-doubling bifurcation, hysteretic dynamics, and parallel bifurcation branches are found when the control parameter is tuned. The analog circuit is built in PSPICE environment, and simulations are performed to validate the obtained results as well as the correctness of the numerical methods. Finally, an encryption/decryption algorithm is designed based on a modified Julia set and confusion–diffusion operations with the sequences of the proposed TLTN model. The security performances of the built cryptosystem are analyzed in terms of computational time (CT = 1.82), encryption throughput (ET = 151.82 MBps), number of cycles (NC = 15.80), NPCR = 99.6256, UACI = 33.6512, χ2-values = 243.7786, global entropy = 7.9992, and local entropy = 7.9083. Note that the presented values are the optimal results. These results demonstrate that the algorithm is highly secured compared to some fastest neuron chaos-based cryptosystems and is suitable for a sensitive field like IoMT security.

Impacts of climate change to African indigenous communities and examples of adaptation responses.

Abstract: Climate change negatively impacts the livelihoods of indigenous communities across the world, including those located on the African continent. This Comment reports on how five African indigenous communities have been impacted by climate change and the adopted adaptation mechanisms.

Environmental remediation potentialities of metal and metal oxide nanoparticles: Mechanistic biosynthesis, influencing factors, and application standpoint

Abstract: Nanotechnology has emerged as a promising field in the development and modification of new nanostructured materials for multifaceted applications, including environmental remediation. Nanoparticles based trendy materials as robust adsorbents and efficient catalytic constructs offer unique structural, chemical, and multifunctional properties, such as high mechanical strength, morphologies, and diverse composition, which promise its use in catalysis, adsorption, degradation, and so on. Herein, we report contemporary advances in metal and metal oxide nanoparticles and their strategic role in environmental approaches, mainly due to the great interest and need for scientific community. The concept of green nanoscience is a practical application of advanced nanotechnology and, therefore, following this relationship between nanotechnology and green chemistry, the biosynthesis of nanoparticles has evolved as an excellent approach. In view of the different techniques widely explored for nanoparticles synthesis, it is possible to state that the main methods currently used are expensive, environmentally harmful, and inefficient regarding the use of materials with applications viewpoints. The natural extracts contain several secondary metabolites that assist in the redox process of metal ions in nanoparticles. Special attention is given to synthetic biological procedures and solvent systems when compared to nanoparticles obtained by non-biological methods. Here, we review recent advances in mechanistic approaches to nanoparticle biosynthesis, as well as the parameters influencing production rate, size, morphology, and their environmental implications. It will become evident that the environmental implication is a rapidly growing field.

Novel framework based on deep learning and cloud analytics for smart patient monitoring and recommendation (SPMR)

Abstract: In world, patients suffering from chronic and lifestyle diseases are substantially increasing that effects social as well as economic life. In this work, initially a broad survey of ubiquitous, smart and networked healthcare systems for monitoring of patients with chronic and lifestyle diseases is presented. Afterwards, Smart Patient Monitoring and Recommendation, a novel framework based on Deep Learning (DL) and Cloud oriented analytics is proposed. Based on the patients’ vital signs and activity context, generated through Ambient Assisted Living devices, SPMR monitors and predicts the real health status and calls assistive services. The real time processing and intelligence facilitated by both Local Intelligent Processing (LIP) module and cloud oriented analytics devised in SPMR. LIP is based on predictive DL with novel Categorical Cross Entropy (CCE) Optimization. In the experimental study, imbalanced dataset collected through a case study on patients suffering from Chronic Blood Pressure disorder is utilized and real health status of patient is predicted. SPMR offers prevention and care in real time even in the absence of internet and cloud service. It eliminates the drawbacks of existing works, in which Machine Learning models and associated methods are copied to local portion. Our proposed model demonstrates the efficacy when compared with similar and recent models. The highest accuracy improvement with our model ranges from 8–18%. Also, F-score average and F-score for emergency class improved up to 17% and 36% respectively. The results show the effectiveness of SPMR even in case of emergencies.

An Optimized Communication Scheme for Energy Efficient and Secure Flying Ad-hoc Network (FANET)

Abstract: FANET (flying ad-hoc network) has provided broad area for research and deployment due to efficient use of the capabilities of drones and UAVs (unmanned ariel vehicles) in several military and rescue applications. Drones have high mobility in 3D (3 dimensional) environment and low battery power, which produce various problems such as small journey time and infertile routing. The optimal routing for communication will assist to resolve these problems and provide the energy efficient and secure data transmission over FANET. Hence, in this paper, we proposed a whale optimization algorithm based optimized link state routing (WOA-OLSR) over FANET to provide optimal routing for energy efficient and secure FANET. The efficiency of OLSR is enhanced by using WOA and evaluated performance shows the better efficiency of WOA-OLSR in terms of some parameters such as a packet delivery ratio, end to end delay, energy utilization, throughput, and time complexity against the previous approaches OLSR, MP-OLSR, P-OLSR, ML-OLSR-FIFO and ML-OLSR-PMS.

XAI-AV: Explainable Artificial Intelligence for Trust Management in Autonomous Vehicles

Abstract: Artificial intelligence (AI) is the most looked up technology with a diverse range of applications across all the fields, whether it is intelligent transportation systems (ITS), medicine, healthcare, military operations, or others. One such application is autonomous vehicles (AVs), which comes under the category of AI in ITS. Vehicular Adhoc Networks (VANET) makes communication possible between AVs in the system. The performance of each vehicle depends upon the information exchanged between AVs. False or malicious information can perturb the whole system leading to severe consequences. Hence, the detection of malicious vehicles is of utmost importance. We use machine learning (ML) algorithms to predict the flaw in the data transmitted. Recent papers that used the stacking ML approach gave an accuracy of 98.44%. Decision tree-based random forest is used to solve the problem in this paper. We achieved accuracy and F1 score of 98.43% and 98.5% respectively on the VeRiMi dataset in this paper. Explainable AI (XAI) is the method and technique to make the complex black-box ML and deep learning (DL) models more interpretable and understandable. We use a particular model interface of the evaluation metrics to explain and measure the model’s performance. Applying XAI to these complex AI models can ensure a cautious use of AI for AVs.

A memristive RLC oscillator dynamics applied to image encryption

Abstract: One of the major challenges in chaos dynamics is the design of simple and realisable electronic circuits capable of complex dynamic behaviours. Our contribution studies the dynamics and implementation of a simple chaotic memristive RLC oscillator circuit of jerky type as well as its application in designing image cryptosystem. The proposed circuit consists of an operational amplifier and an RLC network, comprising a resistor, an inductor, and two capacitors. Considering the crucial role of symmetry in physical, chemical, biological, and mechanical systems, a voltage control memristor emulator is employed to induce symmetry in the proposed circuit. For the dynamic analysis, fourth-order differential equation with exponential nonlinearities is used. Furthermore, standard analysis tools such as Lyapunov spectrum, bifurcation analysis, and phase space trajectories are employed. The phase space is magnetized using four symmetric attractors coexisting for the same set of parameters and different initial conditions indicate the symmetricity of the proposed system. Meanwhile, asymmetric attractor is capable of merging into one symmetric attractor through an attractor-merging bifurcation. Extensive experimental validations of these properties via PSPICE simulations and by using real electronic components are reported. Finally, the chaotic sequences emanating from the proposed oscillator are exploited to design an encryption algorithm that is subsequently validated via simulations and standard image security analysis with prominent outcomes in terms of very low encryption time (0.0746s for 128 × 128 colour image), mean entropy of 7.999 and mean NPCR of 99.62% just to name a few.

CBO-IE: A Data Mining Approach for Healthcare IoT Dataset Using Chaotic Biogeography-Based Optimization and Information Entropy

Abstract: Data mining is mostly utilized for a huge variety of applications in several fields like education, medical, surveillance, and industries. The clustering is an important method of data mining, in which data elements are divided into groups (clusters) to provide better quality data analysis. The Biogeography-Based Optimization (BO) is the latest metaheuristic approach, which is applied to resolve several complex optimization problems. Here, a Chaotic Biogeography-Based Optimization approach using Information Entropy (CBO-IE) is implemented to perform clustering over healthcare IoT datasets. The main objective of CBO-IE is to provide proficient and precise data point distribution in datasets by using Information Entropy concepts and to initialize the population by using chaos theory. Both Information Entropy and chaos theory are facilitated to improve the convergence speed of BO in global search area for selecting the cluster heads and cluster members more accurately. The CBO-IE is implemented to a MATLAB 2021a tool over eight healthcare IoT datasets, and the results illustrate the superior performance of CBO-IE based on F-Measure, intracluster distance, running time complexity, purity index, statistical analysis, root mean square error, accuracy, and standard deviation as compared to previous techniques of clustering like K-Means, GA, PSO, ALO, and BO approaches.

Robust cryptosystem using a new hyperchaotic oscillator with stricking dynamic properties

Abstract: This paper presents a digital image cryptosystem utilizing a novel dynamic system with very interesting features. The oscillator is designed by introducing a feedback control law to the third line of the Lorenz oscillator with exponential nonlinearity. This exponential nonlinearity is replaced with hyperbolic sine nonlinearity to induce more complexity in the oscillator. Using some well-known computation analysis tools like Lyapunov spectrum, bifurcation analysis, and phase portraits representations, the dynamic analysis indicates that the oscillator can show chaos or hyperchaos for the same parameter space. In addition, the oscillator is equilibrium free, consequently its attractors are classified as hidden. Finally, the sequences of the oscillator are utilized to design a robust encryption scheme. Our method relies on a discrete orthogonal moment, confusion and diffusion stages. The input image is represented in the transform domain using Hahn orthogonal moments. Chaotic sequences are used to confuse and diffuse the obtaind image. Various security techniques have been used with success to show that our encryption process is powerful to resist malicious attacks.

Preparation and characterization of biomass briquettes made from banana peels, sugarcane bagasse, coconut shells and rattan waste

Abstract: Biofuel briquettes were prepared using banana peels, rattan waste, coconut shells and sugarcane bagasse through a conventional method that involved drying, carbonizing, crushing, mixing with cassava starch as binder, compacting and drying. Briquettes produced were evaluated by proximate analysis using American Society for Testing and Materials (ASTM) standard and European Committee for Standardization (CEN) standard. Results of mass reduction study show that the losses are more important for banana peels (97.98%) and sugarcane bagasse (96%). Bulk density of briquettes produced is in the range 0.470–0.851 cm3/g; results of proximate analysis show that high calorific values of briquettes produced are 16.98, 30.07, 32.16 and 25.93 MJ/kg for banana peels, rattan waste, coconut shells and sugarcane bagasse respectively; of the four different residues used, volatile matters are slowly lower to the recommendation of literature, ash content has values in the range 7.44–11.95%, and moisture content is relatively higher than other agricultural residues especially banana peels and sugarcane bagasse.

Improving the recovery of copper from electric cable waste derived from automotive industry by corona-electrostatic separation

Abstract: The aim of this work is to demonstrate the possibility of using electric field forces for the recovery of copper from a fine PVC/Cu granular mixture of low metal content (less than 15%), obtained a...

Reclamation and amelioration of saline-sodic soil using gypsum and halophytic grasses: Case of Golina-Addisalem irrigation scheme, Raya Kobo Valley, Ethiopia

Abstract: In Golina-Adisalem irrigation scheme, 500 ha becoming salt-affected. Rehabilitation of salt-affected fields using drainage is expensive and leaching by far not feasible in shallow water table areas...