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Predicting the success rate of a start-up using LSTM with a swish activation function

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Journal of Control and Decision 2022年第3期9卷 355-363页

作者： Ramakrishna Allu Venkata Nageswara Rao Padmanabhuni department of computer science and engineering GITAM(Deemed to be university)VisakhapatnamAndhra PradeshIndia

The researchers emphasised more small-scale start-ups which required improvement in the economy decreased the failure rate by avoiding the valuable *** resources were required to be used without wasting them lead to the development of the economy and reduces the rate of ***,to minimise resource wastage and to avoid the risk of failure the researchers considered certain factors that affected the failure and success of smallscale *** present research uses the Crunch Base dataset that predicts the success or failure of a start-up by using the Long Short Term Memory(LSTM).The LSTM unit Recurrent Neural Network(RNN)uses the Swish activation function in Feed Forward Neural(FFN)Network for the *** proposed LSTM obtained better accuracy of 71.64%when compared with existing methods such as RNN that attained 65.67%and Artificial Neural Network(ANN)of 69.7%.

关键词： Crunch base dataset gross domestic product long short term memory recurrent neural network start-up companies

Federated Approach for Privacy-Preserving Traffic Prediction Using Graph Convolutional Network

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Journal of Shanghai Jiaotong university(science) 2024年第3期29卷 509-517页

作者： LONARE Savita BHRAMARAMBA Ravi Pimpri Chinchwad College of engineering Pune 411044India department of computer science and engineering GITAMViskhapatnam 530045India

Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning ***,data privacy and security are always a challenge in every field where data need to be uploaded to the *** learning(FL)is an emerging trend for distributed training of *** primary goal of FL is to train an efficient communication model without compromising data *** traffic data have a robust spatio-temporal correlation,but various approaches proposed earlier have not considered spatial correlation of the traffic *** paper presents FL-based traffic flow prediction with spatio-temporal *** work uses a differential privacy(DP)scheme for privacy preservation of participant's *** the best of our knowledge,this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP *** proposed framework trains the data locally at the client-side with *** then uses the model aggregation mechanism federated graph convolutional network(FedGCN)at the server-side to find the average of locally trained *** results of the proposed work show that the FedGCN model accurately predicts the *** scheme at client-side helps clients to set a budget for privacy loss.

关键词： federated learning(FL) traffic flow prediction data privacy graph convolutional network(GCN) differential privacy(DP)

Secured Cloud Data Migration Technique by Competent Probabilistic Public Key Encryption

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China Communications 2020年第5期17卷 168-190页

作者： M.G.Aruna K.G.Mohan Aassociate professor department of computer science and engineeringm s engineering collegeaffiliated to vtubengalurukarnataka562110india professor and head department of computer science and engineeringpresidency universitybengalurukarnataka560064india

Cloud computing, a recently emerged paradigm faces major challenges in achieving the privacy of migrated data, network security, etc. Too many cryptographic technologies are raised to solve these issues based on identity, attributes and prediction algorithms yet;these techniques are highly prone to attackers. This would raise a need of an effective encryption technique, which would ensure secure data migration. With this scenario, our proposed methodology Efficient Probabilistic Public Key Encryption(EPPKE) is optimized with Covariance Matrix Adaptation Evolution Strategies(CMA-ES). It ensures data integrity through the Luhn algorithm with BLAKE 2b encapsulation. This enables an optimized security to the data which is migrated through cloud. The proposed methodology is implemented in Open Stack with Java Language. It achieves better results by providing security compared to other existing techniques like RSA, IBA, ABE, PBE, etc.

关键词： Luhn algorithm encryption efficient probabilistic public key encryption(EPPKE) covariance matrix adaptation evolution strategies(CMA-ES) trusted third party(TTP)

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Drug and Vaccine Extractive Text Summarization Insights Using Fine-Tuned Transformers

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Journal of Artificial Intelligence and Technology 2024年第4期4卷 351-362页

作者： Rajesh Bandaru Y.Radhika department of computer science engineering GITAM(Deemed to be University)VisakhapatnamIndia

Text representation is a key aspect in determining the success of various text summarizing *** using pretrained transformer models has produced encouraging *** the scope of applying these models in medical and drug discovery is not examined to a proper *** address this issue,this article aims to perform extractive summarization based on fine-tuned transformers pertaining to drug and medical *** research also aims to enhance sentence *** the extractive text summarization aspects of medical and drug discovery is a challenging task as the datasets are ***,this research concentrates on the collection of abstracts collected from PubMed for various domains of medical and drug discovery such as drug and COVID,with a total capacity of 1,370 abstracts.A detailed experimentation using BART(Bidirectional Autoregressive Transformer),T5(Text-to-Text Transfer Transformer),LexRank,and TexRank for the analysis of the dataset is carried out in this research to perform extractive text summarization.

关键词： BART BERT extractive text summarization LexRank TexRank

Secure workflow scheduling in cloud environment using modified particle swarm optimization with scout adaptation

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International Journal of Modeling, Simulation, and Scientific Computing 2018年第1期9卷 74-95页

作者： P.Sanyasi Naidu Babita Bhagat department of computer science and engineering gitam University Visakhapatnam 530045Andhra PradeshIndia department of computer engineering Pillai HOC College of Engineering and TechnologyRasayaniKhalapur 410207MaharashtraIndia

In the distributed data-intensive computing environment,securely assigning tasks to appropriate machines is a big job scheduling *** complexity of this problem increases with the number of jobs and their job *** meta-heuristic algorithms including particle swarm optimization(PSO)technique and variable neighborhood particle swarm optimization(VNPSO)technique are employed to solve the problem to a certain *** paper proposes a modified PSO with scout adaptation(MPSO-SA)algorithm,which uses a cyclic term called mutation operator,to solve the job scheduling problem in the cloud *** comparative study between the proposed MPSO-SA scheduling mechanism and the conventional scheduling algorithms show that the proposed method decreases the probability of security risk on scheduling the jobs.

关键词： Cloud scheduling meta-heuristic MPSO-SA GA PSO VNPSO

An AW-HARIS Based Automated Segmentation of Human Liver Using CT Images

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computers, Materials & Continua 2021年第12期69卷 3303-3319页

作者： P.Naga Srinivasu Shakeel Ahmed Abdulaziz Alhumam Akash Bhoi Kumar Muhammad Fazal Ijaz department of computer science and engineering GITAM Institute of TechnologyGITAM Deemed to be UniversityVisakhapatnam530045India department of computer science College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-Ahsa31982Saudi Arabia department of Electrical and Electronics engineering Sikkim Manipal Institute of TechnologySikkim Manipal UniversityMajitar737136India department of Intelligent Mechatronics engineering Sejong UniversitySeoul05006Korea

In the digestion of amino acids,carbohydrates,and lipids,as well as protein synthesis from the consumed food,the liver has many diverse responsibilities and functions that are to be *** disease may impact the hormonal and nutritional balance in the human *** earlier diagnosis of such critical conditions may help to treat the patient effectively.A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human *** proposed approach can recognize the abnormalities with better accuracy without training,unlike in supervisory procedures requiring considerable computational efforts for *** the earlier stages,the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image’s contrast for better ***,the preliminary phase’s outcome is being fed as the input for the Anisotropic Weighted—Heuristic Algorithm for Real-time Image Segmentation algorithm that uses texture-related information,which has resulted in precise outcome with acceptable computational latency when compared to that of its *** is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%.The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.

关键词： CT image automated segmentation HARIS anisotropic weighted social group optimization

Software Defect Prediction Based Ensemble Approach

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computer Systems science & engineering 2023年第6期45卷 2313-2331页

作者： J.Harikiran B.Sai Chandana B.Srinivasarao B.Raviteja Tatireddy Subba Reddy School of computer science and engineering VIT-AP UniversityAmaravathi522237India department of computer science and engineering GITAM Deemed to be UniversityTelangana502329India department of computer science and engineering B.V.Raju Institute of Technology NarsapurMedakTelangana502313India

Software systems have grown significantly and in *** a result of these qualities,preventing software faults is extremely *** defect prediction(SDP)can assist developers in finding potential bugs and reducing maintenance *** it comes to lowering software costs and assuring software quality,SDP plays a critical role in software *** a result,automatically forecasting the number of errors in software modules is important,and it may assist developers in allocating limited resources more *** methods for detecting and addressing such flaws at a low cost have been *** approaches,on the other hand,need to be significantly improved in terms of *** in this paper,two deep learning(DL)models Multilayer preceptor(MLP)and deep neural network(DNN)are *** proposed approaches combine the newly established Whale optimization algorithm(WOA)with the complementary Firefly algorithm(FA)to establish the emphasized metaheuristic search EMWS algorithm,which selects fewer but closely related representative *** find the best-implemented classifier in terms of prediction achievement measurement factor,classifiers were applied to five PROMISE repository *** compared to existing methods,the proposed technique for SDP outperforms,with 0.91%for the JM1 dataset,0.98%accuracy for the KC2 dataset,0.91%accuracy for the PC1 dataset,0.93%accuracy for the MC2 dataset,and 0.92%accuracy for KC3.

关键词： Prediction of a software defect deep learning models enhancedWOA firefly algorithm

Enhancing Software Effort Estimation:A Hybrid Model Combining LSTM and Random Forest

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Journal of Harbin Institute of Technology(New Series) 2024年第4期31卷 42-51页

作者： Badana Mahesh Mandava Kranthi Kiran department of computer science and engineering GITAM Deemed to be UniversityVishakhapatnam 561203India department of computer science and engineering Anil Neerukonda Institute of Technology&SciencesVishakhapatnam 531162India

Effort estimation plays a crucial role in software development projects,aiding in resource allocation,project planning,and risk *** estimation techniques often struggle to provide accurate estimates due to the complex nature of software *** recent years,machine learning approaches have shown promise in improving the accuracy of effort estimation *** study proposes a hybrid model that combines Long Short-Term Memory(LSTM)and Random Forest(RF)algorithms to enhance software effort *** proposed hybrid model takes advantage of the strengths of both LSTM and RF *** evaluate the performance of the hybrid model,an extensive set of software development projects is used as the experimental *** experimental results demonstrate that the proposed hybrid model outperforms traditional estimation techniques in terms of accuracy and *** integration of LSTM and RF enables the model to efficiently capture temporal dependencies and non-linear interactions in the software development *** hybrid model enhances estimation accuracy,enabling project managers and stakeholders to make more precise predictions of effort needed for upcoming software projects.

关键词： software effort estimation hybrid model ensemble learning LSTM temporal dependencies non⁃linear relationships

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Analysis of vibrational spectra of nano-bio molecules: Application to metalloporphrins

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Chinese Physics B 2013年第9期22卷 225-232页

作者： K Srinivasa Rao G Srinivas J.Vijayasekhar V.U.M.Rao Y.Srinivas K.Sunil Babu V.Sunndadara Siva Kumar A.Hanumaiah department of Electronics & Instrumentation engineering Lakireddy Bali Reddy College of Engineering department of Physics KL University Research Scholar Department of MathematicsJawaharlal Nehru Technological University department of Applied Mathematics College of Science & TechnologyAndhra University department of computer science GITAM University department of Physics Miracle Engineering College department of Electronics & Instrumentation Rajeev Gandhi Memorial Engineering College department of Physics Lara's Vignan Institute of Technology and Science

In this paper, we have applied the Lie algebraic model to nano-bio molecules to determine the vibrational spectra of different stretching and bending vibrational modes. The determined vibrational energy levels by the Lie algebraic model are compared with the experimental data. The results from the theoretical mode[ are consistent with the experimental data. The vibrational energy levels are clustering in the excited states.

关键词： Lie algebraic model vibrational spectra vibrational modes metalloporphyrins

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