Automated coconut tree climber

Maheswaran, S.; Sathesh, S.; Saran, G.; Vivek, B

doi:10.1109/i2c2.2017.8321858

Cited by 21 publications

(17 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Network performance is generalized by adding sparsity, which reduces network links. The sparse representation of classification has been studied by various researchers for face recognition ( Gui et al, 2015 ; Maheswaran et al, 2017b ), object categorization ( Gui et al, 2014b ), classification ( Gui et al, 2014a ) and regression oriented tasks ( Bradski, 2000 ). Deep sparse AE is defined by two steps where the first step is to perform unsupervised layer wise greedy training as pre state.…”

Section: Proposed Cdsae Methodologymentioning

confidence: 99%

Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection

Balasubramanian

Prabu

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

In the recent research era, artificial intelligence techniques have been used for computer vision, big data analysis, and detection systems. The development of these advanced technologies has also increased security and privacy issues. One kind of this issue is Deepfakes which is the combined word of deep learning and fake. DeepFake refers to the formation of a fake image or video using artificial intelligence approaches which are created for political abuse, fake data transfer, and pornography. This paper has developed a Deepfake detection method by examining the computer vision features of the digital content. The computer vision features based on the frame change are extracted using a proposed deep learning model called the Cascaded Deep Sparse Auto Encoder (CDSAE) trained by temporal CNN. The detection process is performed using a Deep Neural Network (DNN) to classify the deep fake image/video from the real image/video. The proposed model is implemented using Face2Face, FaceSwap, and DFDC datasets which have secured an improved detection rate when compared to the traditional deep fake detection approaches.

show abstract

Section: Proposed Cdsae Methodologymentioning

confidence: 99%

Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection

Balasubramanian

Prabu

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…The literature used robots for multiple purposes including spraying or removing pesticides [170][171][172][173], irrigation [174,175], picking crops [171,176,177], sewing seeds [178][179][180], ploughing soil [181,182], harvesting [162,183]. Robots' design and development considerations include controlling robots and their parts' movement [162,184,185], slecting and integrating motors [180,186] and the mechanical design of body/chaise [174,181,186,187] are discussed design issues.…”

Section: ) Robotsmentioning

confidence: 99%

Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

View full text Add to dashboard Cite

As today's agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading the farming practices by shifting towards Smart Farming (SF) -utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify the suitable technologies and best practices for a particular application. This paper provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers' contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, the paper discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.

show abstract

“…The closed loop control theory [23][24][25] was used in the industrial automation field. The proposed Probe interval producing engine was designed as PID neural network controller that calculated their control decision as function of the error.…”

Section: Probing Time Producing Enginementioning

confidence: 99%

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

Malavika¹,

Valarmathi²

2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Web service applications are increasing tremendously in support of high-level businesses. There must be a need of better server load balancing mechanism for improving the performance of web services in business. Though many load balancing methods exist, there is still a need for sophisticated load balancing mechanism for not letting the clients to get frustrated. In this work, the server with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests. The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low, medium and high load by the load balancing application. Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system. Many Load Balancing schemes are based on the graded thresholds, because the exact information about the network flux is difficult to obtain. Using two thresholds L and U, it is possible to indicate the load on particular server as low, medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L, between L and U or above U respectively. However, the existing works of load balancing in the server farm incorporate fixed time to measure real time response time, which in general are not optimal for all traffic conditions. Therefore, an algorithm based on Proportional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal performance. The emulation results has shown a significant gain in the performance by tuning the threshold time. In addition to that, tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune the fixed time slots.

show abstract

Automated coconut tree climber

Cited by 21 publications

References 6 publications

Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection

Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection

Technologies Driving the Shift to Smart Farming: A Review

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

Contact Info

Product

Resources

About