Summary Clustering‐based optimal cluster head selection in wireless sensor networks (WSNs) is considered as the efficient technique essential for improving the network lifetime. But enforcing optimal cluster head selection based on energy stabilization, reduced delay, and minimized distance between sensor nodes always remain a crucial challenge for prolonging the network lifetime in WSNs. In this paper, a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO‐CA‐OCHS) scheme is proposed to extend the lifetime. This proposed HEHO‐CA‐OCHS scheme utilizes the merits of belief space framed by the cultural algorithm for defining a separating operator that is potent in constructing new local optimal solutions in the search space. Further, the inclusion of belief space aids in maintaining the balance between an optimal exploitation and exploration process with enhanced search capabilities under optimal cluster head selection. This proposed HEHO‐CA‐OCHS scheme improves the characteristic properties of the algorithm by incorporating separating and clan updating operators for effective selection of cluster head with the view to increase the lifetime of the network. The simulation results of the proposed HEHO‐CA‐OCHS scheme were estimated to be superior in percentage of alive nodes by 11.21%, percentage of dead nodes by 13.84%, residual energy by 16.38%, throughput by 13.94%, and network lifetime by 19.42% compared to the benchmarked cluster head selection schemes.
Waste management is a major issue with the emerging growth in the world population, and we need to find efficient ways to recycle and reuse waste. Segregating waste has become a primary need in waste management as different types of waste like Bio & Non-Bio-degradable waste should be processed differently. Effective waste isolation at the fundamental level is especially required for this. Several Smart cities oriented smart garbage management systems are also proposed using Internet of Things (IoT) and GSM. The existing smart bins using IoT and wireless sensor network (WSN) are dependent significantly on two major things. First, multiple types of sensors, as a single sensor may not be able to detect different material waste, and second, the console (Microcontroller, Arduino Raspberry Pi) and connectivity which in turn dependent on programming and operating system. These limitations of the embedded smart bin are overcome by combining IoT with artificial intelligence approaches such as deep neural network (DNN) systems. In this paper, we have presented a Friendly Waste Segregator Using Deep Learning and the IoT to classify and isolate the waste objects as biodegradable and nonbiodegradable. Our proposed method utilizes, a robust deep learning network to classify the waste accurately and IoT for monitoring and connectivity using various sensors. Our proposed method with initial training can identify and segregte real-time waste objects without human intervention with an average accuracy of 97.49 %. Our smart bin intends to provide optimized waste management of bio and non-bio-waste and help to build an ecologically safe society. K E Y W O R D Sbio and non-bio-waste, deep transfer learning, Internet of Things, CNN, smart bin, waste management INTRODUCTIONIn the growing world and fast urbanization generation of waste is inevitable and it increases exponentially. Isolation and reusing waste materials are essential for a viable society. Waste management has become a serious problem as different sorts of waste like bio-and non-biodegradable waste should be processed differently as the large portion of the waste items has considerably reusable or recyclable substance. Well-managed biodegradable waste is fully compostable which can be used as a recyclable manure fertilizer to improve the growth of crops and plants, generate power, etc. 1Even non-biodegradable waste after processing is also recyclable and can be reused. Such sort of waste packs the landfills and contaminates the earth, is a menace to the environment, and is of great concern. Plastic is likewise a non-biodegradable material that sets aside an extremely long
Tea plant cultivation plays a significant role in the Indian economy. The Tea board of India supports tea farmers to increase tea production by preventing various diseases in Tea Plant. Various climatic factors and other parameters cause these diseases. In this paper, the image retrieval model is developed to identify whether the given input tea leaf image has a disease or is healthy. Automation in image retrieval is a hot topic in the industry as it doesn't require any form of metadata related to the images for storing or retrieval. Deep Hashing with Integrated Autoencoders is our proposed method for image retrieval in Tea Leaf images. It is an efficient and flexible way of retrieving Tea Leaf images. It has an integrated autoencoder which makes it better than the state-of-the-art methods giving better results for the MAP (mean average precision) scores, which is used as a parameter to judge the efficiency of the model. The autoencoders used with skip connections increase the weightage of the prominent features present in the previous tensor. This constitutes a hybrid model for hashing and retrieving images from a tea leaf data set. The proposed model will examine the input tea leaf image and identify the type of tea leaf disease. The relevant image will be retrieved based on the resulting type of disease. This model is only trained on scarce data as a real-life scenario, making it practical for many applications.
The visibility of the image is degraded by haze components which are affected in image during image acquisition process. The haze removal process can be categorized into haze reduction from single image and haze reduction from multiple images. The second case consumes more time and the complexity of algorithm is high. In order to overcome such limitations, this paper proposes an efficient methodology for haze reduction in single acquired image. In this paper, haze detection and removing methodology is proposed using Gaussian pyramidal decomposition. This proposed methodology is tested on both indoor and outdoor haze affected images. The performance of this proposed haze removal algorithm is analyzed with respect to average running time and peak signal to noise ratio, mean square error, mean absolute error, Entropy, and normalized histogram intersection coefficient. K E Y W O R D Sdecomposition, Gaussian pyramidal, haze detection, single image INTRODUCTIONImage surveillance and object detection in an image plays an important role in real world applications. During the acquiring of the images, hazy and foggy particles will present in the image which degrades the quality of the acquired images. The lights scattered by these particles will also affect the luminance intensity of the nearby pixels in an image. [1][2][3][4][5] This will create fading and low contrast pixels in an image. Hence, the visibility of the image is degraded with respect to increasing these scattered components in outdoor or indoor environments. This will create many problems for real time image processing applications such as unmanned vehicle moving system and satellite image system. 6,7 Hence, there is a need for removing the haze components from the acquired image for improving the quality of the image. This will help the computer vision based applications in real time image processing applications. 8 Figure 1(A) shows the haze affected in outdoor images and Figure 1(B) shows the haze affected in indoor images.The performance of computer vision algorithms and advanced image editing algorithms can also be improved. Therefore, haze removal is highly demanded in image processing, computational photography and computer vision applications. Since the amount of scattering depends on the unknown distances of the scene points from the camera and the air-light is also unknown, it is challenging to remove haze from haze images, especially when there is only a single haze image. Many methods were presented for detecting and removing the haze components by using multiple images. [9][10][11][12][13] This process required complex algorithm for removing the haze from the image using multiple images. Also, the average time consumption for removing the haze component from the single image by multiple images is high. In order to overcome such limitations in conventional haze removing process, this paper proposes an efficient methodology for removing the haze components from the single source image only. This method uses Gaussian pyramidal decomposition (GPD) a...
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