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Does Partner Asymmetry Facilitate Innovation Speed? Organizational Learning and Social Exchange Perspectives

IEEE Transactions on engineering Management 2024年 71卷 4144-4159页

作者： Hsiao, Yung-Chang Wu, Ming-Ho National University of Tainan Department of Business and Management College of Management Tainan700301 Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering College of Electrical Engineering and Computer Science Kaohsiung824005 Taiwan

The right partner and high innovation speed are crucial for a successful research and development (R&D) alliance in the high-tech industry. Does homogeneity or heterogeneity between partners benefit innovation speed? This study examined the links between partner asymmetries (technological heterogeneity and network resource asymmetry) and innovative speed from both economic and social dual perspectives by focusing on biopharmaceutical industries. The results revealed an inverse U-shaped relationship between technology heterogeneity and innovation speed. In addition, a positive linear relationship exists between network resource asymmetry and innovation speed for 1991-2000 alliances and for biotech-biotech partner-type alliances;significantly negative moderating effects of time to market occurred on these linear relations. Finally, this study makes important theoretical and practical contributions to partner selection literature on R&D alliances in the biopharmaceutical industry. © 1988-2012 IEEE.

关键词： Behavioral research

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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computer Systems science & engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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Segmentation Based Real Time Anomaly Detection and Tracking Model for Pedestrian Walkways

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Intelligent Automation & Soft Computing 2023年第6期36卷 2491-2504页

作者： B.Sophia D.Chitra Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatoreTamilnaduIndia Department of Computer Science and Engineering P.A College of Engineering and TechnologyPollachiTamilnaduIndia

Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so ***,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian *** recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,*** this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian *** proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,*** proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition *** object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized *** ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)*** simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.

关键词： Panoptic segmentation object detection deep learning tracking model anomaly detection pedestrian walkway

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IoT-Based Women Safety Gadgets (WSG): Vision, Architecture, and Design Trends

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computers, Materials & Continua 2023年第7期76卷 1027-1045页

作者： Sharad Saxena Shailendra Mishra Mohammed Baljon Shamiksha Mishra Sunil Kumar Sharma Prakhar Goel Shubham Gupta Vinay Kishore Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaPunjab147001India Department of Computer Engineering College of Computer&Information ScienceMajmaah UniversityMajmaah11952Saudi Arabia Department of Computer Science and Engineering National Institute of TechnologyJamshedpur831001India

In recent years,the growth of female employees in the commercial market and industries has *** a result,some people think travelling to distant and isolated locations during odd hours generates new threats to women’s *** exponential increase in assaults and attacks on women,on the other hand,is posing a threat to women’s growth,development,and *** the time of the attack,it appears the women were immobilized and needed immediate *** self-defense isn’t sufficient against abuse;a new technological solution is desired and can be used as quickly as hitting a switch or *** proposed Women Safety Gadget(WSG)aims to design a wearable safety device model based on Internet-of-Things(IoT)and Cloud *** is designed in three layers,namely layer-1,having an android app;layer-2,with messaging and location tracking system;and layer-3,which updates information in the cloud *** can detect an unsafe condition by the pressure sensor of the finger on the artificial nail,consequently diffuses a pepper spray,and automatically notifies the saved closest contacts and police station through messaging and location *** has a response time of 1000 ms once the nail is pressed;the average time for pulse rate measure is 0.475 s,and diffusing the pepper spray is 0.2–0.5 *** average activation time is 2.079 s.

关键词： Perpetrator safety IoT women security GPS tracking GSM

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Automated Irrigation System Using Improved Fuzzy Neural Network in Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第1期35卷 853-866页

作者： S.Sakthivel V.Vivekanandhan M.Manikandan Department of Computer Science and Engineering Sona College of TechnologySalem 636005TamilnaduIndia Department of Computer Science and Engineering Adhiyamaan College of EngineeringHosur 635109TamilnaduIndia

Irrigation plays a significant role in various agricultural cropping methods deployed in semiarid and arid regions where valuable water applications and managing are considered crucial *** factors such as weather,soil,water,and crop data need to be considered for irrigation maintenance in an efficient besides uniform manner from multifaceted and different information-based systems.A Multi-Agent System(MAS)has been proposed recently based on diverse agent subsystems with definite objectives for attaining global MAS objective and is deployed on Cloud Computing paradigm capable of gathering information from Wireless Sensor Networks(WSNs)positioned in rice,cotton,cassava crops for knowledge discovery and decision *** radial basis function network has been used for irrigation ***,in recent work,the security of data has not focused on where intruder involvement might corrupt the data at the time of data transferring to the cloud,which would affect the accuracy of decision *** handle the above mentioned issues,an efficient method for irrigation prediction is used in this *** factors considered for decision making are soil moisture,temperature,plant height,root *** above-mentioned data will be gathered from the sensors that are attached to the *** data will be forwarded to the local server,where data encryption will be performed using Adaptive Elliptic Curve Cryptography(AECC).After the encryption process,the data will be forwarded to the *** the data stored in the cloud will be decrypted key before being given to the deci-sion-making ***,the uniform distribution-based fuzzy neural network is formulated based on the received data information in the decisionmaking *** decision regarding the level of water required for cropfields would be *** on this outcome,the water volve opening duration and the level of fertilizers required will be *** results demons

关键词： Irrigation multi-agent system precision irrigation accuracy elliptic curve cryptography encryption wireless sensor networks fertilizers

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Leveraging 3D Faster R-CNN for 3D Dental X-ray Restoration and Treatment Identification

Leveraging 3D Faster R-CNN for 3D Dental X-ray Restoration a...

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International Conference on Computing and Machine Learning, CML 2024

作者： Rai, Bipin Kumar Bisht, Deepanshu Kumar, Ekansh Chaudhary, Aashish Department of Computer Science & Engineering Dayananda Sagar University Bengaluru India Department of CSE-Data Science ABES Institute of Technology Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9789819765874

This research aimed to develop a 3D Faster-R-CNN model for detecting dental restorations and treatments in panoramic view radiographs and dental intra-oral X-rays. Trained on a comprehensive collection of 2D and 3D dental X-ray images, it marked a notable advancement. Utilizing the 3D TeethSeg 2022 MICCA dataset, the model demonstrated its ability to automatically detect and classify dental features, including challenging traits like cavity spots and tooth cracks. The study addressed the limitations of current methods for analyzing dental X-rays, especially in 3D format. While existing 2D models have some recognition capabilities, they struggle with 3D data intricacies. Moreover, dedicated 3D models for dental applications are lacking. This proposed model introduced a novel approach tailored for 3D dental image analysis, aiming to fill this gap. The model played a vital role in assisting dentists by identifying restorative and treatment needs for each tooth or patient, significantly enhancing dental care quality and efficiency. It showcased notable performance metrics: an accuracy of 89.14%, a precision of 95.84%, and a recall of 91.93%. Through advanced data augmentation and meticulous model training, it represented a significant advancement in detecting and classifying dental interventions, offering a comprehensive approach to dental image analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： X ray radiography

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Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

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Intelligent Automation & Soft Computing 2023年第3期35卷 3169-3185页

作者： M.Manikandan S.Sakthivel V.Vivekanandhan Department of Computer Science and Engineering Adhiyamaan College of EngineeringHosur635109TamilnaduIndia Department of Computer Science and Engineering Sona College of TechnologySalem636005TamilnaduIndia

Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed *** closed network is intended to share sensitive location-centric information from a source node to the base station through efﬁcient routing *** efﬁciency of the sensor node is energy bounded,acts as a concentrated area for most researchers to offer a solution for the early draining power of *** management plays a signiﬁcant role in wireless sensor networks,which was obsessed with the factors like the reliability of the network,resource management,energy-efﬁcient routing,and scalability of *** topology of the wireless sensor networks acts dri-ven factor for network efﬁciency which can be effectively maintained by perform-ing the clustering process *** solutions and clustering algorithms have been offered by various researchers,but the concern of reduced efﬁciency in the routing process and network management still *** research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efﬁcient clustering process in the wireless sensor *** memetic algorithm employs a local searching methodology to mitigate the premature convergence,while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain(i.e.,)best cluster head in the wireless sensor *** proposed hybrid algorithm is compared with the state of art clus-tering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time,energy consumption,throughput,etc.

关键词： Wireless sensor networks topology clustering memetic algorithm adaptive hill climbing algorithm network management energy consumption throughput

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Automatic lung cancer detection using hybrid particle snake swarm optimization with optimized mask RCNN

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Multimedia Tools and Applications 2024年第31期83卷 76807-76831页

作者： Sudha, R. Maheswari, K. M. Uma Department of Computer Science and Engineering School of computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India Department of Computing Technologies School of Computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India

As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that is crucial to a person's survival. The first diagnosis of the malignant nodules is typically made using chest radiography (X-rays) and computed tomography (CT) scans;however, the potential presence of benign nodules results in incorrect conclusions. The early phases of both benign and malignant nodules exhibit striking similarities. In this paper, a novel deep learning-based model is proposed for the precise diagnosis of malignant nodules. The proposed approach consists of two stages namely, pre-processing and lung nodule detection. Initially, the Lung CT scan images are collected from the dataset. Then, to remove the noise present in the input image, we apply an adaptive median filter. Then, to enhance the image, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied. After pre-processing, the image is given to the optimized mask RCNN classifier to detect the malignant and benign nodules. To enhance the performance of the Mask RCNN classifier, the hyper-parameters are optimally selected using hybrid particle snake swarm optimization (PS2OA). The proposed PS2OA is a hybridization of particle swarm optimization (PSO) and snake swarm optimization (SSO). The performance of the proposed approach is analyzed based on different metrics and effectiveness compared with state-of-the-art works. The proposed approach attained the maximum accuracy of 97.67%. This work aimed at assisting radiologists to detect and diagnose small-size pulmonary nodules more accurately. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Exploring LLM and Neural Networks Towards Malicious Prompt Detection 5

Exploring LLM and Neural Networks Towards Malicious Prompt D...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Deshpande, Himani Chaudhari, Dhawal Sarode, Tanmay Kamath, Ayush Thadomal Shahani Engineering College Department of Artificial Intelligence & Data Science Mumbai India Thadomal Shahani Engineering College Department of Computer Science Mumbai India Science Thadomal Shahani Engineering College Department of Computer Mumbai India

ISBN: (纸本)9798350379945

Atthe forefront of the Artificial Intelligence Revolution is the Generative AI domain which is making splashes in generation of new content from existing Large Language Models. Large Language Models (LLMs) are flexible, effective tools with many uses. On the other hand, they can be tricked by devious provocation. This work explores the detection of fraudulent prompts intended to produce false or damaging outputs using Long Short-Term Memory (LSTM) networks. During this study we analysed the LTSM model against a neural network model. We found a 91.69% accuracy for the LTSM model and a 92.52% accuracy for the simple neural network. However, the simple neural network had a higher F1 score of 0.73 compared to the LTSM score of 0.69. We found that the standard neural model is more efficient at identifying harmful user prompts. Our research highlights the need for taking into account various architectures of models to reduce the risk of malicious prompting in language learning models. © 2024 IEEE.

关键词： Neural network models

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Deep belief bi-directional LSTM network-based intelligent student’s performance prediction model with entropy weighted fuzzy rough set mining

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International Journal of Intelligent Information and database Systems 2023年第2期16卷 107-142页

作者： Sateesh, Nayani Rao, P. Srinivasa Lakshmi, D. Rajya Department of Computer Science and Engineering Jawaharlal Nehru Technological University Kakinada Kakinada India Department of Computer Science and Engineering MVGR College of Engineering Vizianagaram India Department of Computer Science and Engineering University College of Engineering Vizianagaram Jawaharlal Nehru Technological University Kakinada India

To analyse the student’s academic performance, a new prediction model is developed. This proposed model collects the student’s data from standard online sources. At first, these gathered data are pre-processed by certain methods like outlier removal and filling. Then, the filtered data undergoes feature mining by developing the entropy weighted fuzzy rough set mining (EW-FRSM), where certain parameters are tuned with the proposed hybrid optimisation algorithm with probability-based coyote electric fish optimisation (P-CEFO). The refined features are utilised for performance prediction, where the deep belief-Bi-LSTM (DB-Bi-LSTM) is used for student performance prediction. The parameters in DBN and Bi-LSTM are tuned with the same P-CEFO algorithm to improve the prediction performance. Simulation is performed to examine the efficacy of the designed prediction method with diverse performance metrics by evaluating with diverse baseline approaches to prove the usefulness of the model. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Entropy

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