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Any node in a mobile ad hoc network (MANET) can act as a host or router at any time and so, the nodes in the MANET are vulnerable to many types of attacks. Sybil attack is one of the harmful attacks in the MANET, which produces fake identities similar to legitimate nodes in the network. It is a serious threat to the MANET when a malicious node uses the fake identities to enter the network illegally.

A MANET is an independent collection of mobile nodes that form a temporary or arbitrary network without any fixed infrastructure. The nodes in the MANET lack centralized administration to manage the network and change their links to other devices frequently.

So for securing a MANET, an approach based on biometric authentication can be used. The multimodal biometric technology has been providing some more potential solutions for the user to be able to devise an authentication in MANETs of high security.

The Sybil detection approach, which is based on the received signal strength indicator (RSSI) variations, permits the node to be able to verify the authenticity of communicating nodes in accordance with their localizations.

As the MANET node suffers from a low level of memory and power of computation, there is a novel technique of feature extraction that is proposed for the multimodal biometrics that makes use of palm prints that are based on a charge-coupled device and fingerprints, along with the features that are fused.

This paper proposes an RSSI-based multimodal biometric solution to detect Sybil attack in MANETs.

The results of the experiment have indicated that this method has achieved a performance which is better compared to that of the other methods.

The humans are gifted with the potential of recognizing others by their uniqueness, in addition with more other demographic characteristics such as ethnicity (or race), gender and age, respectively. Over the decades, a vast count of researchers had undergone in the field of psychological, biological and cognitive sciences to explore how the human brain characterizes, perceives and memorizes faces. Moreover, certain computational advancements have been developed to accomplish several insights into this issue.

This paper intends to propose a new race detection model using face shape features. The proposed model includes two key phases, namely. (a) feature extraction (b) detection. The feature extraction is the initial stage, where the face color and shape based features get mined. Specifically, maximally stable extremal regions (MSER) and speeded-up robust transform (SURF) are extracted under shape features and dense color feature are extracted as color feature. Since, the extracted features are huge in dimensions; they are alleviated under principle component analysis (PCA) approach, which is the strongest model for solving “curse of dimensionality”. Then, the dimensional reduced features are subjected to deep belief neural network (DBN), where the race gets detected. Further, to make the proposed framework more effective with respect to prediction, the weight of DBN is fine tuned with a new hybrid algorithm referred as lion mutated and updated dragon algorithm (LMUDA), which is the conceptual hybridization of lion algorithm (LA) and dragonfly algorithm (DA).

The performance of proposed work is compared over other state-of-the-art models in terms of accuracy and error performance. Moreover, LMUDA attains high accuracy at 100th iteration with 90% of training, which is 11.1, 8.8, 5.5 and 3.3% better than the performance when learning percentage (LP) = 50%, 60%, 70%, and 80%, respectively. More particularly, the performance of proposed DBN + LMUDA is 22.2, 12.5 and 33.3% better than the traditional classifiers DCNN, DBN and LDA, respectively.

This paper achieves the objective detecting the human races from the faces. Particularly, MSER feature and SURF features are extracted under shape features and dense color feature are extracted as color feature. As a novelty, to make the race detection more accurate, the weight of DBN is fine tuned with a new hybrid algorithm referred as LMUDA, which is the conceptual hybridization of LA and DA, respectively.

The most widely recycled plastic in the world is recycled polyethylene terephthalate (rPET). To minimize the environmental related issues associated with synthetic fibers, several researchers have explored the potential use of recycled polyester fibers in developing various technical textile products. This study aims to develop needle-punched nonwoven fabrics from recycled polyester fibers and investigate its suitability in oil spill cleanup process.

According to Box and Behnken factorial design, 15 different needle-punched nonwoven fabrics from recycled polyester fibers were prepared by changing the parameters, namely, needle punch density, needle penetration depth and fabric areal weight. Several featured parameters such as oil sorption, oil retention, oil sorption kinetics, wettability and reusability performance were systematically elucidated.

The maximum oil sorption of recycled nonwoven polyester is found to be 24.85 g/g and 20.58 g/g for crude oil and vegetable oil, respectively. The oil retention is about 93%–96% in case of crude oil, whereas 87%–91% in case of vegetable oil. Recycled polyester nonwoven possesses good hydrophobic–oleophilic properties with static contact angle of 138° against water, whereas 0° against crude oil and vegetable oil. The reusability test results indicate that recycled polyester nonwoven fabric can be used several times because of its reusability features.

There is no detailed study on the oil sorption features of needle-punched nonwoven fabrics developed from recycled polyester fibers. This study is expected to help in developing fabrics for oil spill cleanups.

Several chaotic system-based encryption techniques have been presented in recent years to protect digital images using cryptography. The challenges of key distribution and administration make symmetric encryption difficult. The purpose of this paper is to address these concerns, the novel hybrid partial differential elliptical Rubik’s cube algorithm is developed in this study as an asymmetric image encryption approach. This novel algorithm generates a random weighted matrix, and uses the masking method on image pixels with Rubik’s cube principle. Security analysis has been conducted, it enhances and increases the reliability of the proposed algorithm against a variety of attacks including statistical and differential attacks.

In this light, a differential elliptical model is designed with two phases for image encryption and decryption. A modified image is achieved by rotating and mixing intensities of rows and columns with a masking matrix derived from the key generation technique using a unique approach based on the elliptic curve and Rubik’s cube principle.

To evaluate the security level, the proposed algorithm is tested with statistical and differential attacks on a different set of test images with peak signal-to-noise ratio, unified average changed intensity and number of pixel change rate performance metrics. These results proved that the proposed image encryption method is completely reliable and enhances image security during transmission.

The elliptic curve–based encryption is hard to break by hackers and adding a Rubik’s cube principle makes it even more complex and nearly impossible to decode. The proposed method provides reduced key size.

The purpose of this paper is to verify the developed hypothesis on the basis of comparison of remote education and traditional education in terms of its effectiveness at the micro-level, as well as to determine the presence, degree and nature of correlation between the share of remote education and level of development of economic systems of separate states (macro-level).

The methodologies of this paper are based on the system approach and include methods of research investigation such as problem, logical, comparative analysis, synthesis and formalization, as well as a purposely designed author’s method of assessment of the effectiveness of remote education and traditional education. In addition to the specified methods, this methodology also includes the methods of correlation and regression analysis which are used by the authors to determine the presence and degree of correlation between the share of remote education and the state of macro-level economic systems. The authors have analyzed the correlation between the share of remote education in the higher education structure according to the summarized data of the ICEF Monitor and the existing studies and publications on this topic (y) with indicators of macro-level economic systems such as GDP, billions of dollars (x₁); GDP per capita (x₂); Education Index according to the United Nations Development Program (x₃); Knowledge Economy Index according to The World Bank Group (x₄); and the index of innovative development of socioeconomic systems according to INSEAD, WIPO and Cornell University (x₅). The econometric analysis of the mentioned factors was performed after that.

The authors have come to conclusion that remote education is indeed much more effective at the micro-level, since it allows the students to receive similar educational services with greater convenience, a wider choice of higher educational institutions and at a lower cost compared to traditional education. At the same time, no negative influence of remote education on the macro-level economic system has been revealed; on the contrary, a positive, albeit slight, influence similar to traditional education has been found. For this reason, promotion of the formation and development of remote education is recommended instead of limitation, since it allows modernizing the educational system for the benefit of both supply and demand.

The research contributes to the development of the concept of socioeconomic development of economic systems through clarification of influence of remote education on it.

The application of rinse cycle softener after the household laundry process has become more common in recent times. This study aims to understand the effect of repeated rinse cycle softener treatment on the mechanical and frictional properties of the cotton fabric.

Cotton-woven fabric is treated with commercial rinse cycle softener repeatedly for 15 times. After treatment, the fabric was evaluated for the changes in mechanical properties using the Kawabata evaluation system.

The results of this study revealed that the softener treatment reduces the tensile properties (41.25%) and increases the overall extensibility of the fabric up to 20.89%. The shear (34.57%) and bending rigidity of the treated fabric are reduced considerably than the untreated fabric (58.02%). The increment in the fabric softness and fluffiness was confirmed with the increment in the compression and the difference between the initial and final thickness at maximum pressure. Statistical significance (p < 0.05) is noted only in the case of bending and surface friction properties (dynamic friction).

The usage of rinse cycle softeners in the household laundry has a significant influence on the comfort characteristics of the cotton-woven fabric. Repeated usage of rinse cycle softener increased the fabric softness and fluffiness of the fabric and also reduced the tensile properties significantly.

The mischievous nodes that defy the standard corrupt the exhibition of good nodes considerably. Therefore, an intrusion discovery mechanism should be included to the mobile ad-hoc network (MANET). In this paper, worm-hole and other destructive malignant attacks are propelled in MANET.

A wireless ad-hoc network also called as mobile ad-hoc network (MANET) is a gathering of hubs that utilizes a wireless channel to exchange information and coordinate together to establish information exchange among any pair of hubs, without any centralized structure. The security issue is a major difficulty while employing MANETs.

Consequently, the attacks due to the malicious node activity are detected using Hybrid Reactive Search and Bat (HRSB) mechanism to prevent the mischievous nodes from entering the network beneath the untruthful information. Moreover, the attack detection rate and node energy are predicted for determining the lifetime of the node.

The simulation outcomes of the proposed HRSB technique are evaluated with the prevailing methods. The comparison studies have proven the efficacy of the current research model by attaining high attack detection rate and achieving more network lifetime.

The purpose of this paper is to understand the critical factors associated with growing fertilizer usage culminating in contamination of soil/water in agriculturally intensive regions of Uttar Pradesh, India. The agriculture sector is seen as one of the major contributors in ensuring food security, however adoption of sustainable agriculture to protect water resources from contamination due to fertilizers and pesticides is becoming pressing to achieve long term environmental security.

A two staged study aimed at monitoring the soil quality status followed by stakeholder survey has been attempted. Attitude-behavior framework based on the theory of reasoned action has been tried to explain the fertilizer use behavior in the study. The results are analyzed through Analysis of variance.

Soil monitoring data showed nitrate and total nitrogen loadings beyond the permissible limit in the identified regions. A questionnaire aimed at determining farmer’s attitude toward fertilizer usage showed a significant influence of factors like net farm income, overall farm yield, extension services, farmer characteristics on one hand and risks associated with changing farming practices, costs of substitutes available, market-based instruments like subsidies and loans on the other. Divergent responses were observed with respect to farmer’s perceived risks from adopting to organic substitutes, linkages of fertilizer application with environmental degradation and the level of adoption of sustainable agricultural practices.

The study can be scaled up to study the inter-regional differences by benchmarking regional responses. It would be interesting to extend the work to find solutions from the farmers as alternative fertility management strategies. The items used in questionnaire are self-made; hence there is still a possibility of enhancing the robustness of scale by applying advanced statistical techniques.

Results of the study indicate excessive nitrogen loadings in farm soils which is an indicator of potential future nitrate contaminated zones or vulnerable zones emerging in agricultural intensive regions. Findings reinforce the role of education, knowledge transfer and awareness for long-term agricultural sustainability. The paper highlights the urgency for reorientation of the support system by the government and policymakers.

The paper attempts to understand the linkage between the agricultural productivity and the environmental implications followed by the reasons culminating in the agri-environmental imbalance. On-site monitoring study followed by assessment of reasons culminating in this scenario has not been attempted earlier and this paper contributes to understanding at dual level. This paper emphasizes on the insights of stakeholder which is instrumental in ensuring agricultural sustainability or otherwise. It takes the position that the farmer’s farm management behavior is strongly influenced by factors like food security and income, keeping environmental quality at second place. It also identifies the barriers for organic farming and other alternative systems as well as explores the economic, social, and philosophical aspects of sustainable agriculture.

In recent years, oil spill pollution has become one of the main problems of environmental pollution. Recovering oil by means of sorbent materials is a very promising approach and has acquired more attention due to its high cleanup efficiency. Compared to synthetic fibrous sorbents, the use of natural fibers in oil spill cleanups offers several advantages including environmental friendliness, degradable features and cost-effectiveness. Therefore, studies on developing sorbents using natural fibers for oil spill cleanup applications have become a research hotspot.

This paper reviews the work conducted by several researchers in developing oil sorbents from fibers such as cattail, nettle, cotton, milkweed, kapok, populous seed fiber and Metaplexis japonica fiber. Some featured critical parameters influencing the oil sorption capacity of fibrous substrates are discussed. Oil sorption capacity and reusability performance of various fibers are also discussed. Recent developments in oil spill cleanups and test methods for oil sorbents are briefly covered.

The main parameters influencing the oil sorption capacity of sorbents are fiber morphological structure, fiber density (g/cc), wax (%), hollowness (%) and water contact angle. An extensive literature review showed that oil sorption capacity is highest for Metaplexis japonica fiber followed by populous seed fiber, kapok, milkweed, cotton, nettle and cattail fiber. After use, the sorbents can be buried under soil or they can also be burned so that they can be vanished from the surface without causing environmental-related issues.

This review paper aims to summarize research studies conducted related to various natural fibers for oil spill cleanups, fiber structural characteristics influencing oil sorption and recent developments in oil spill cleanups. This work will inspire future researchers with various knowledge backgrounds, particularly, from a sustainability perspective.

One of the challenging issues in computer vision and pattern recognition is face image recognition. Several studies based on face recognition were introduced in the past decades, but it has few classification issues in terms of poor performances. Hence, the authors proposed a novel model for face recognition.

The proposed method consists of four major sections such as data acquisition, segmentation, feature extraction and recognition. Initially, the images are transferred into grayscale images, and they pose issues that are eliminated by resizing the input images. The contrast limited adaptive histogram equalization (CLAHE) utilizes the image preprocessing step, thereby eliminating unwanted noise and improving the image contrast level. Second, the active contour and level set-based segmentation (ALS) with neural network (NN) or ALS with NN algorithm is used for facial image segmentation. Next, the four major kinds of feature descriptors are dominant color structure descriptors, scale-invariant feature transform descriptors, improved center-symmetric local binary patterns (ICSLBP) and histograms of gradients (HOG) are based on clour and texture features. Finally, the support vector machine (SVM) with modified random forest (MRF) model for facial image recognition.

Experimentally, the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy, similarity index, dice similarity coefficient, precision, recall and F-score results. However, the proposed method offers superior recognition performances than other state-of-art methods. Further face recognition was analyzed with the metrics such as accuracy, precision, recall and F-score and attained 99.2, 96, 98 and 96%, respectively.

The good facial recognition method is proposed in this research work to overcome threat to privacy, violation of rights and provide better security of data.