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| 1 | Cognitive frequency diverse array radar with symmetric non-uniform frequency offset显示文摘Frequency diverse array(FDA) radar with uniform inter-element frequency offset generates a beam pattern with maxima at multiple range and angle values. Multiple maxima property allows interferers located at any of the maxima to affect the target-returns. As a result the signal to interference noise ratio(SINR) and probability of detection decreases. In this paper, we propose a cognitive uniformly-spaced FDA with non-uniform but symmetric frequency offsets to achieve a single maximum beam pattern at the target position. Moreover,these non-uniform frequency offsets are calculated using well known mu-law formulae. The design sharpens or broadens the transmitted beam pattern based on the receiver feedback to achieve a better detection probability and an improved SINR as compared to the previous designs. The performance is also analyzed by considering the Cramer-Rao lower bound(CRLB) on target angle and range estimation. | Abdul BASIT Ijaz Mansoor QURESHI Wasim KHAN Aqdas Naveed MALIK | 2016 | Science China(Information Sciences)2016，59，10： | 1 |
| 2 | Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification显示文摘Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake news.The recently developed machine learning(ML)models can be employed for the detection and classification of fake news.This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and Classification.The goal of the CAS-WELM technique is to discriminate news into fake and real.The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding process.Then,N-gram based feature extraction technique is derived to gen-erate feature vectors.Lastly,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm.The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions.The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches. | Ashit Kumar Dutta Basit Qureshi Yasser Albagory Majed Alsanea Manal Al Faraj Abdul Rahaman Wahab Sait | 2023 | Computer Systems Science & Engineering2023，44，3： | 0 |
| 3 | Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification显示文摘Recently,developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day lives.It results in illegal access to users’private data and compromises it.Phishing is a popular attack which tricked the user into accessing malicious data and gaining the data.Proper identification of phishing emails can be treated as an essential process in the domain of cybersecurity.This article focuses on the design of bio-geography based optimization with deep learning for Phishing Email detection and classification(BBODL-PEDC)model.The major intention of the BBODL-PEDC model is to distinguish emails between legitimate and phishing.The BBODL-PEDC model initially performs data pre-processing in three levels namely email cleaning,tokenization,and stop word elimination.Besides,TF-IDF model is applied for the extraction of useful feature vectors.Moreover,optimal deep belief network(DBN)model is used for the email classification and its efficacy can be boosted by the BBO based hyperparameter tuning process.The performance validation of the BBODL-PEDC model can be performed using benchmark dataset and the results are assessed under several dimensions.Extensive comparative studies reported the superior outcomes of the BBODL-PEDC model over the recent approaches. | Ashit Kumar Dutta T.Meyyappan Basit Qureshi Majed Alsanea Anas Waleed Abulfaraj Manal M.Al Faraj Abdul Rahaman Wahab Sait | 2023 | Computer Systems Science & Engineering2023，44，3： | 0 |
| 4 | Exploration of IoT Nodes Communication Using LoRaWAN in Forest Environment显示文摘The simultaneous advances in the Internet of Things(IoT),Artificial intelligence(AI)and Robotics is going to revolutionize our world in the near future.In recent years,LoRa(Long Range)wireless powered by LoRaWAN(LoRa Wide Area Network)protocol has attracted the attention of researchers for numerous applications in the IoT domain.LoRa is a low power,unlicensed Industrial,Scientific,and Medical(ISM)bandequipped wireless technology that utilizes a wide area network protocol,i.e.,LoRaWAN,to incorporate itself into the network infrastructure.In this paper,we have evaluated the LoRaWAN communication protocol for the implementation of the IoT(Internet of Things)nodes’communication in a forest scenario.The outdoor performance of LoRa wireless in LoRaWAN,i.e.,the physical layer,has been evaluated in the forest area of Kashirampur Uttarakhand,India.Hence,the present paper aims towards analyzing the performance level of the LoRaWAN technology by observing the changes in Signal to Noise Ratio(SNR),Packet Reception Ratio(PRR)and Received Signal Strength Indicator(RSSI),with respect to the distance between IoT nodes.The article focuses on estimating network lifetime for a specific set of LoRa configuration parameters,hardware selection and power constraints.From the experimental results,it has been observed that transmissions can propagate to a distance of 300 m in the forest environment,while consuming approx.63%less energy for spreading factor 7 at 2 dBm,without incurring significant packet loss with PRR greater than 80%. | Anshul Sharma Divneet Singh Kapoor Anand Nayyar Basit Qureshi Kiran Jot Singh Khushal Thakur | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 5 | Synthesis,Characterization,Biological and Docking Simulations of 4-(Benzylideneamino) Benzoic Acids显示文摘The present research paper presents the synthesis, characterization, biological and computational studies of 4-(benzylideneamino) benzoic acid derivatives(3a~3g). Derivatives 3a~3c displayed best antidiabetic potential with a glucose-lowering effect compared to the reference drug Glibenclamide. Biochemical parameters including plasma glucose, serum triglycerides, cholesterol, alanine amino transferase and aspartate amino transferase levels showed significant alterations in concentrations relative to the control. Similarly, the derivatives 3a, 3d and 3e displayed potent in vitro antibacterial potential. Molecular docking simulations delineated that the ligands and complexes were stabilized at the active site by electrostatic and hydrophobic forces, consistent with the corresponding experimental results. In silico study of the binding pattern predicted that the synthesized ligands, 3d and 3a could serve as a potential surrogate for hit-to-lead generation and the design of novel antibacterial drugs. | HAMID Aziz AAMER Saeed FARUKH Jabeen ABDUL Basit IRFAN Zia Qureshi ABDUL Aziz ATIF Haroon ASHFAQ Ur Rehman | 2021 | Chinese Journal of Structural Chemistry2021，40，3： | 0 |
| 6 | Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting显示文摘In the past few decades,climatic changes led by environmental pollution,the emittance of greenhouse gases,and the emergence of brown energy utilization have led to global warming.Global warming increases the Earth’s temperature,thereby causing severe effects on human and environmental conditions and threatening the livelihoods of millions of people.Global warming issues are the increase in global temperatures that lead to heat strokes and high-temperature-related diseases during the summer,causing the untimely death of thousands of people.To forecast weather conditions,researchers have utilized machine learning algorithms,such as autoregressive integrated moving average,ensemble learning,and long short-term memory network.These techniques have been widely used for the prediction of temperature.In this paper,we present a swarm-based approach called Cauchy particle swarm optimization(CPSO)to find the hyperparameters of the long shortterm memory(LSTM)network.The hyperparameters were determined by minimizing the LSTM validationmean square error rate.The optimized hyperparameters of the LSTM were used to forecast the temperature of Chennai City.The proposed CPSO-LSTM model was tested on the openly available 25-year Chennai temperature dataset.The experimental evaluation on MATLABR2020a analyzed the root mean square error rate and mean absolute error to evaluate the forecasted output.The proposed CPSO-LSTM outperforms the traditional LSTM algorithm by reducing its computational time to 25 min under 200 epochs and 150 hidden neurons during training.The proposed hyperparameter-based LSTM can predict the temperature accurately by having a root mean square error(RMSE)value of 0.250 compared with the traditional LSTM of 0.35 RMSE. | Malini M.Patil P.M.Rekha Arun Solanki Anand Nayyar Basit Qureshi | 2022 | Computers, Materials & Continua2022，，5： | 0 |
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