Electricity-theft detection in smart grids based on deep learning

lectricity theft is a major concern for utilities. The smart grid (SG) infrastructure generates a massive amount of data, including the power consumption of individual users. Utilizing this data, machine learning, and deep learning techniques can accurately identify electricity theft users. A convolutional neural network (CNN) model for automatic electricity theft detection is presented. This work considers experimentation to find the best configuration of the sequential model (SM) for classifying and identifying electricity theft. The best performance has been obtained in two layers with the first layer consists of 128 nodes and the second layer is 64 nodes. The accuracy reached up to 0.92. This enables the design of high-performance electricity signal classifiers that can be used in several applications. Designing electricity signals classifiers has been achieved using a CNN and the data extracted from the electricity consumption dataset using an SM. In addition, the blue monkey (BM) algorithm is used to reduce the features in the dataset. In this respect, the focusing of this work is to reduce the features in the dataset to obtain high-performance electricity signals classifier models.

Population Size, Activity Patterns, Diet, and Ranging Ecology of Boutourlini’s Blue Monkey (Cercopithecus mitis boutourlinii) in Apini and Dokuma Forests, Awi Zone, Ethiopia

Background: By studying population size, activity patterns, diet, and ranging ecology of Boutourolini’s blue monkey (Cercopithecus mitis boutourlinii) we can get sufficient information to conserve the subspecies in the area. Boutourlini’s blue monkey is endemic subspecies found in the western and northwestern parts of Ethiopia. The study was conducted in Apini and Dokuma forests, northwestern Ethiopia, from October 2018 to June 2019. The block count method was used to estimate its total population size and scan sampling method was used to collect data for activity patterns, and diet. The ranging ecology of the study subspecies was determined for each group based on the point to point movements of the group between consecutive GPS locations recorded. Results: On average a total of 71 and 111 individuals of Boutourolini’s blue monkey were counted in Apini and Dokuma forests, respectively. Boutourolini’s blue monkey spent 47.5% and 48.6% of time feeding, 20.2% and 18.6% moving, and 14.1% and 13.5% resting by the Apini and Dokuma groups, respectively. The Apini group frequently fed on young leaves (52.8%), fruits (30.2%), and mature leaves (6.6%), while the Dokuma group fed on young leaves (39.8%), fruit 942.3%), and mature leaves (8.3%). Ranging ecology of Boutourolini’s blue monkey was 44.4 ha and 78.3 ha for the Apini group, and 51 ha and 56.9 ha for the Dokuma group during the wet and dry seasons, respectively. Conclusions: The total population size of blue monkeys in the Apini and Dikuma forests counted were different. Activity budgets vary during the wet and dry seasons. During the entire study period blue monkeys consumed different food items from the two forests. The home range size of blue monkeys during the two seasons was different in both groups and they traveled long distance during the dry season. As the habitats of Boutourolini’s blue monkey in the present study area is degraded due to various anthropogenic activities, there is a need to design strategies to minimize conservation problems of the subspecies in the area.

Improving Encryption Digital Watermark by Using Blue Monkey Algorithm

Watermarking enables the users to share the digital contents in public domain without any issue. In the present day, the tremendous development in the digital technologies and networks caused an increase in the threats of unauthorized copying, tampering of digital media and image theft. To face these threats, digital watermark technology can be applied. However, the current paper uses new technique with two main algorithms that are the following ones: improved honey algorithm that is used to encrypt the digital watermark and blue monkey meta-heuristic algorithm which is used to find the best location in the host image to hide the digital watermark. Furthermore, in order to check the security and the robustness of the proposed method against various common image processing attacks such as Gaussian noise, Rotation, Salt and pepper noise, Sharpen, Median filter, Average filter, compression and Cropping is computed, certain performance metrics such as Peak to Signal Noise Ratio (PSNR)and Mean Square Error (MSE) also are computed. Likewise, Normalized Correlation (NC) is used to check similarity between the original and extracted digital watermark. The results demonstrate that the proposed method is efficient, provides high security and robustness against most attacks compared to the pervious methods.

Reluctant males: why blue monkey males reject female sexual advances

Classical sexual selection theory predicts that males should mate eagerly, yet blue monkey males often reject females’ sexual invitations. We evaluated how males’ responses to female solicitations related to female characteristics, number of males and conceptive females present, and the male’s recent copulations. Using 12 years of data from a wild population, we found that males accepted only 20% of female solicitations. Odds of acceptance (copulation) increased for conceptive females, for females with whom the male copulated recently, and when fewer males were present. Odds of accepting nulliparous females decreased when more conceptive females were available, consistent with market models. Male responses did not relate to female rank or matings with other females the same day. When males responded negatively, nulliparous females were especially likely to receive aggression vs. mere refusal. Overall, males’ decisions to mate with willing females depended both on female characteristics, especially fertility, and on social context.