检索结果-南通市图书馆

Neural Regeneration Research 2022年第2期17卷 245-245页

作者： gollapalli

The Editors of Neural Regeneration Research have retracted the publication entitled“Emerging concepts underlying selective neuromuscular dysfunction in infantile-onset spinal muscular atrophy”(gollapalli et al.,2021;doi:10.4103/1673-5374.308073)following concerns stemming from misquoting and,accordingly,faulty characterization of key literature cited.

关键词： Regeneration infantile dysfunction

Artificial intelligence powered glucose monitoring and controlling system:Pumping module

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

World Journal of Experimental Medicine 2024年第1期14卷 100-112页

作者： Sravani Medanki Nikhil Dommati Hema Harshitha Bodapati Venkata Naga Sai Kowsik Katru gollapalli Moses Abhishek Komaraju Nanda Sai Donepudi Dhanya Yalamanchili Jasti Sateesh Pratap Turimerla Department of Electronics and Communication Engineering Velagapudi Ramakrishna Siddhartha Engineering CollegeVijayawada 520007Andhra PradeshIndia Department of General Medicine Siddhartha Government Medical CollegeVijayawada 520008Andhra PradeshIndia

BACKGROUND Diabetes,a globally escalating health concern,necessitates innovative solutions for efficient detection and management.Blood glucose control is an essential aspect of managing diabetes and finding the most effective ways to control it.The latest findings suggest that a basal insulin administration rate and a single,highconcentration injection before a meal may not be sufficient to maintain healthy blood glucose levels.While the basal insulin rate treatment can stabilize blood glucose levels over the long term,it may not be enough to bring the levels below the post-meal limit after 60 min.The short-term impacts of meals can be greatly reduced by high-concentration injections,which can help stabilize blood glucose levels.Unfortunately,they cannot provide long-term stability to satisfy the postmeal or pre-meal restrictions.However,proportional-integral-derivative(PID)control with basal dose maintains the blood glucose levels within the range for a longer period.AIM To develop a closed-loop electronic system to pump required insulin into the patient's body automatically in synchronization with glucose sensor readings.METHODS The proposed system integrates a glucose sensor,decision unit,and pumping module to specifically address the pumping of insulin and enhance system effectiveness.Serving as the intelligence hub,the decision unit analyzes data from the glucose sensor to determine the optimal insulin dosage,guided by a pre-existing glucose and insulin level table.The artificial intelligence detection block processes this information,providing decision instructions to the pumping module.Equipped with communication antennas,the glucose sensor and micropump operate in a feedback loop,creating a closed-loop system that eliminates the need for manual intervention.RESULTS The incorporation of a PID controller to assess and regulate blood glucose and insulin levels in individuals with diabetes introduces a sophisticated and dynamic element to diabetes management.The simulation not only allows visualization of ho

关键词： Diabetes Hyperglycemia Insulin Micropump Closed loop systems Artificial intelligence automation

Modeling and Verification of Aircraft Takeoff Through Novel Quantum Nets

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第8期72卷 3331-3348页

作者： Maryam Jamal Nazir Ahmad Zafar Atta-ur-Rahman Dhiaa Musleh Mohammed A.gollapalli Sghaier Chabani School of Systems and Technology University of Management&Technology(UMT)C-II Block C 2 Phase 1 Johar TownLahore54770PunjabPakistan Department of Computer Science COMSATS University Islamabad–Sahiwal CampusSahiwal57000PunjabPakistan Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)Dammam31441Saudi Arabia Department of Computer Information System(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)Dammam31441Saudi Arabia

The formal modeling and verification of aircraft takeoff is a challenge because it is a complex safety-critical operation.The task of aircraft takeoff is distributed amongst various computer-based controllers,however,with the growing malicious threats a secure communication between aircraft and controllers becomes highly important.This research serves as a starting point for integration of BB84 quantum protocol with petri nets for secure modeling and verification of takeoff procedure.The integrated model combines the BB84 quantum cryptographic protocol with powerful verification tool support offered by petri nets.To model certain important properties of BB84,a new variant of petri nets coined as Quantum Nets are proposed by defining their mathematical foundations and overall system dynamics,furthermore,some important system properties are also abstractly defined.The proposed QuantumNets are then applied for modeling of aircraft takeoff process by defining three quantum nets:namely aircraft,runway controller and gate controller.For authentication between quantum nets,the use of external places and transitions is demonstrated to describe the encryptiondecryption process of qubits stream.Finally,the developed takeoff quantum network is verified through simulation offered by colored petri-net(CPN)Tools.Moreover,reachability tree(RT)analysis is also performed to have greater confidence in feasibility and correctness of the proposed aircraft takeoff model through the Quantum Nets.

关键词： Aircraft takeoff BB84 quantum cryptography petri-nets quantum nets formal modeling and verification

Emerging concepts underlying selective neuromuscular dysfunction in infantile-onset spinal muscular atrophy

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Neural Regeneration Research 2021年第10期16卷 1978-1984页

作者： Kishore gollapalli Jeong-Ki Kim Umrao R.Monani Department of Neurology Columbia University Medical CenterNew YorkNYUSA Department of Pathology&Cell Biology Columbia University Medical CenterNew YorkNYUSA Center for Motor Neuron Biology and Disease Columbia University Medical CenterNew YorkNYUSA

Infantile-onset spinal muscular atrophy is the quintessential example of a disorder characterized by a predominantly neurodegenerative phenotype that nevertheless stems from perturbations in a housekeeping protein.Resulting from low levels of the Survival of Motor Neuron(SMN)protein,spinal muscular atrophy manifests mainly as a lower motor neuron disease.Why this is so and whether other cell types contribute to the classic spinal muscular atrophy phenotype continue to be the subject of intense investigation and are only now gaining appreciation.Yet,what is emerging is sometimes as puzzling as it is instructive,arguing for a careful re-examination of recent study outcomes,raising questions about established dogma in the field and making the case for a greater focus on milder spinal muscular atrophy models as tools to identify key mechanisms driving selective neuromuscular dysfunction in the disease.This review examines the evidence for novel molecular and cellular mechanisms that have recently been implicated in spinal muscular atrophy,highlights breakthroughs,points out caveats and poses questions that ought to serve as the basis of new investigations to better understand and treat this and other more common neurodegenerative disorders.

关键词： motor neuron neurodegeneration neuromuscular spinal muscular atrophy splicing

A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第10期73卷 295-310页

作者： Mohammed gollapalli Atta-ur-Rahman Dhiaa Musleh Nehad Ibrahim Muhammad Adnan Khan Sagheer Abbas Ayesha Atta Muhammad Aftab Khan Mehwash Farooqui Tahir Iqbal Mohammed Salih Ahmed Mohammed Imran BAhmed Dakheel Almoqbil Majd Nabeel Abdullah Omer Department of Computer Information System(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Pattern Recognition and Machine Learning Lab Department of Software EngineeringGachon UniversityInchonKorea Department of Computer Science National College of Business Administration and EconomicsLahorePunjab54000Pakistan Department of Computer Science Government College University(GCU)LahorePunjab54000Pakistan Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Networks and Communications(NC) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems.Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide.Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road.To address this overwhelming problem,in this article,a cloudbased intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach.The aim of the study is to reduce the delay in the queues,the vehicles experience at different road junctions across the city.The proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things(IoT)sensors across the road.After due preprocessing over the cloud server,the proposed approach makes use of this data by incorporating the neuro-fuzzy engine.Consequently,it possesses a high level of accuracy by means of intelligent decision making with minimum error rate.Simulation results reveal the accuracy of the proposed model as 98.72%during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%,95.84%,97.56%and 98.03%,respectively.As far as the training phase analysis is concerned,the proposed scheme exhibits 99.214% accuracy. The proposed prediction modelis a potential contribution towards smart cities environment.

关键词： Neuro-fuzzy machine learning congestion prediction AI cloud computing smart cities

A Novel Metadata Based Multi-Label Document Classification Technique

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第8期46卷 2195-2214页

作者： Naseer Ahmed Sajid Munir Ahmad Atta-ur Rahman Gohar Zaman Mohammed Salih Ahmed Nehad Ibrahim Mohammed Imran BAhmed Gomathi Krishnasamy Reem Alzaher Mariam Alkharraa Dania AlKhulaifi Maryam AlQahtani Asiya A.Salam Linah Saraireh Mohammed gollapalli Rashad Ahmed Barani Institute of Information Technology(BIIT) PMAS Arid Agriculture UniversityRawalpindi46000PunjabPakistan Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Faculty Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaBatu PahatMalaysia Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia College of Business Administration Imam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information System(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia ICS Department King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

From the beginning,the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies,its growth rate is overwhelming.On a rough estimate,more than thirty thousand research journals have been issuing around four million papers annually on average.Search engines,indexing services,and digital libraries have been searching for such publications over the web.Nevertheless,getting the most relevant articles against the user requests is yet a fantasy.It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification.To overcome this issue,researchers are striving to investigate new techniques for the classification of the research articles especially,when the complete article text is not available(a case of nonopen access articles).The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess,“to what extent metadata-based features can perform in contrast to content-based approaches.”In this regard,novel techniques for investigating multilabel classification have been proposed,developed,and evaluated on metadata such as the Title and Keywords of the articles.The proposed technique has been assessed for two diverse datasets,namely,from the Journal of universal computer science(J.UCS)and the benchmark dataset comprises of the articles published by the Association for computing machinery(ACM).The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature.

关键词： Multilabel classification indexing metadata content/data mining

Supervised Machine Learning-Based Prediction of COVID-19

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第10期69卷 21-34页

作者： Atta-ur-Rahman Kiran Sultan Iftikhar Naseer Rizwan Majeed Dhiaa Musleh Mohammed Abdul Salam gollapalli Sghaier Chabani Nehad Ibrahim Shahan Yamin Siddiqui Muhammad Adnan Khan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information System College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of CIT Faculty of Applied StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Directorate of IT The Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science Minhaj University LahoreLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan

COVID-19 turned out to be an infectious and life-threatening viral disease,and its swift and overwhelming spread has become one of the greatest challenges for the world.As yet,no satisfactory vaccine or medication has been developed that could guarantee its mitigation,though several efforts and trials are underway.Countries around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely treatment.In this regard,healthcare experts,researchers and scientists have delved into the investigation of existing as well as new technologies.The situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this disease.The state-of-the-art research in Artificial intelligence(AI),Machine learning(ML)and cloud computing have encouraged healthcare experts to find effective detection schemes.This study aims to provide a comprehensive review of the role of AI&ML in investigating prediction techniques for the COVID-19.A mathematical model has been formulated to analyze and detect its potential threat.The proposed model is a cloud-based smart detection algorithm using support vector machine(CSDC-SVM)with cross-fold validation testing.The experimental results have achieved an accuracy of 98.4%with 15-fold cross-validation strategy.The comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency.

关键词： COVID-19 CSDC-SVM artificial intelligence machine learning cloud computing support vector machine

Pulmonary Tuberculosis as Differential Diagnosis of Lung Cancer:CT Findings

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Pulmonary Tuberculosis as Differential Diagnosis of Lung Can...

作者： DR.gollapalli RAVI 重庆医科大学

学位级别：硕士

ObjectiveTo evaluate the Pulmonary tuberculosis as differential diagnosis of lung cancer:CT Findings With Pathologic Correlation.MethodsFrom January to December,2015,the patients of Pulmonary tuberculosis had under gone Computed Tomography（CT）imaging examinations differentiate with other diagnosis like lung cancer.The results were compared with pathological reports,those of difficult to diagnosis mismatch of Pulmonary tuberculosis and lung cancer.ResultsCT Findings With Pathologic CorrelationConclusionAlthough the slow reduction of the incidence of TB has been seen in develo ped countries,TB is still a major challenge among infectious diseases,even in the 21st century.Fast and accurate TB testing,such as bacterial DNA analysis and whole-blood interferon-γ assay,has been developed for detecting latent infection.The traditional imaging concept of primary and reactivation Tb has recently been challenged on the basisof DNA fingerprinting,and radiologic featres depend on the level of host immunity rather than the elapsed time after the infection.PET scans using 18F-FDG or 11C-cho-line can some times help diffentiate a tuberculous nodule from lung malignncy.CT is an effective diagnostic method when chest radiographs are normal or inconclusive,and it provides valuable information for the diagnosis and management of TB.

关键词：

Shift in pKa of 1,10-Phenanthroline in TBAB and PEG-400 Micellar Media: A Potentiometric Study

同方学位论文库评论

在线全文

同方学位论文库

学校读者我要写书评

暂无评论

American Journal of Analytical Chemistry 2018年第9期9卷 397-405页

作者： Atnafu Guadie Assefa Rongali Srinivasu Pulipaka Shyamala gollapalli Nageswara Rao Department of Inorganic and Analytical Chemistry Andhra University Visakhapatnam India Department of Chemistry College of Natural and Computational Sciences University of Gondar Gondar Ethiopia Department of Physical and Nuclear Chemistry & Chemical Oceanography Andhra University Visakhapatnam India

The acid-base equilibrium of 1,10-phenanthroline (1,10-phen) in the presence of polyethylene glycol 400 (PEG-400, non-ionic) and tetrabutylammonium bromide (TBAB, ionic) micellar media has been studied by Calvin-Wilson titration technique at different mole fractions (0.5% - 2.5%) of PEG-400 and TBAB micellar solutions at an ionic strength of 0.16 mol dm-3 NaCl and at a temperature of 298 K. The pH metric data were subjected to SCPHD program to obtain correction factor and Kw which were given as initial inputs for MINIQUAD75 program to refine protonation constants. HySS program was then used to generate the species distribution diagrams versus pH using the results obtained from the MINIQUAD75 program and SIM run data. The difference in values of protonation constants in aqueous medium (logβ1 = 4.93 & logβ2 = 6.22) and in the micellar media (PEG-400, logβ1 = 4.89 & logβ2 = 5.91 and TBAB, logβ1 = 4.84 & logβ2 = 5.73) is attributed to different intrinsic solvent characteristics of micelles. In case of ionic surfactants, the electrostatic micellar surface is an additional contributing factor.

关键词： Protonation Equilibria 1 10-Phenanthroline MINIQUAD75 PEG-400 TBAB Micelles