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Until now, a lot of research has been done and applied to provide security and original data from one user to another, such as third-party auditing and several schemes for securing the data, such as the generation of the key with the help of encryption algorithms like Rivest–Shamir–Adleman and others. Here are some of the related works that have been done previously. Remote damage control resuscitation (RDCR) scheme by Yan et al. (2017) is proposed based on the minimum bandwidth. By enabling the third party to perform the verification of public integrity. Although it supports the repair management for the corrupt data and tries to recover the original data, in practicality it fails to do so, and thus it takes more computation and communication cost than our proposed system. In a paper by Chen et al. (2015), using broadcast encryption, an idea for cloud storage data sharing has been developed. This technique aims to accomplish both broadcast data and dynamic sharing, allowing users to join and leave a group without affecting the electronic press kit (EPK). In this case, the theoretical notion was true and new, but the system’s practicality and efficiency were not acceptable, and the system’s security was also jeopardised because it proposed adding a member without altering any keys. In this research, an identity-based encryption strategy for data sharing was investigated, as well as key management and metadata techniques to improve model security (Jiang and Guo, 2017). The forward and reverse ciphertext security is supplied here. However, it is more difficult to put into practice, and one of its limitations is that it can only be used for very large amounts of cloud storage. Here, it extends support for dynamic data modification by batch auditing. The important feature of the secure and efficient privacy preserving provable data possession in cloud storage scheme was to support every important feature which includes data dynamics, privacy preservation, batch auditing and blockers verification for an untrusted and an outsourced storage model (Pathare and Chouragadec, 2017). A homomorphic signature mechanism was devised to prevent the usage of the public key certificate, which was based on the new id. This signature system was shown to be resistant to the id attack on the random oracle model and the assault of forged message (Nayak and Tripathy, 2018; Lin et al., 2017). When storing data in a public cloud, one issue is that the data owner must give an enormous number of keys to the users in order for them to access the files. At this place, the knowledge assisted software engineering (KASE) plan was publicly unveiled for the first time. While sharing a huge number of documents, the data owner simply has to supply the specific key to the user, and the user only needs to provide the single trapdoor. Although the concept is innovative, the KASE technique does not apply to the increasingly common manufactured cloud. Cui et al. (2016) claim that as the amount of data grows, distribution management system (DMS) will be unable to handle it. As a result, various proven data possession (PDP) schemes have been developed, and practically all data lacks security. So, here in these certificates, PDP was introduced, which was based on bilinear pairing. Because of its feature of being robust as well as efficient, this is mostly applicable in DMS. The main purpose of this research is to design and implement a secure cloud infrastructure for sharing group data. This research provides an efficient and secure protocol for multiple user data in the cloud, allowing many users to easily share data.

The methodology and contribution of this paper is given as follows. The major goal of this study is to design and implement a secure cloud infrastructure for sharing group data. This study provides an efficient and secure protocol for multiple user data in cloud, allowing several users to share data without difficulty. The primary purpose of this research is to design and implement a secure cloud infrastructure for sharing group data. This research develops an efficient and secure protocol for multiple user data in the cloud, allowing numerous users to exchange data without difficulty. Selection scheme design (SSD) comprises two algorithms; first algorithm is designed for limited users and algorithm 2 is redesigned for the multiple users. Further, the authors design SSD-security protocol which comprises a three-phase model, namely, Phase 1, Phase 2 and Phase 3. Phase 1 generates the parameters and distributes the private key, the second phase generates the general key for all the users that are available and third phase is designed to prevent the dishonest user to entertain in data sharing.

Data sharing in cloud computing provides unlimited computational resources and storage to enterprise and individuals; moreover, cloud computing leads to several privacy and security concerns such as fault tolerance, reliability, confidentiality and data integrity. Furthermore, the key consensus mechanism is fundamental cryptographic primitive for secure communication; moreover, motivated by this phenomenon, the authors developed SSDmechanismwhich embraces the multiple users in the data-sharing model.

Files shared in the cloud should be encrypted for security purpose; later these files are decrypted for the users to access the file. Furthermore, the key consensus process is a crucial cryptographic primitive for secure communication; additionally, the authors devised the SSD mechanism, which incorporates numerous users in the data-sharing model, as a result of this phenomena. For evaluation of the SSD method, the authors have considered the ideal environment of the system, that is, the authors have used java as a programming language and eclipse as the integrated drive electronics tool for the proposed model evaluation. Hardware configuration of the model is such that it is packed with 4 GB RAM and i7 processor, the authors have used the PBC library for the pairing operations (PBC Library, 2022). Furthermore, in the following section of this paper, the number of users is varied to compare with the existing methodology RDIC (Li et al., 2020). For the purposes of the SSD-security protocol, a prime number is chosen as the number of users in this work.

Vertical handoff mechanism (VHO) becomes very popular because of the improvements in the mobility models. These developments are less to certain circumstances and thus do not provide support in generic mobility, but the vertical handover management providing in the heterogeneous wireless networks (HWNs) is crucial and challenging. Hence, this paper introduces the vertical handoff management approach based on an effective network selection scheme.

This paper aims to improve the working principle of previous methods and make VHO more efficient and reliable for the HWN.Initially, the handover triggering techniques is modelled for identifying an appropriate place to initiate handover based on the computed coverage area of cellular base station or wireless local area network (WLAN) access point. Then, inappropriate networks are eliminated for determining the better network to perform handover. Accordingly, a network selection approach is introduced on the basis ofthe Fractional-dolphin echolocation-based support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train Support vector neural network (SVNN) for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network.

The performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput. The proposed Fractional-DE method achieves the minimal delay of 0.0100 sec, the minimal energy consumption of 0.348, maximal staytime of 4.373 sec, and the maximal throughput of 109.20 kbps.

In this paper, a network selection approach is introduced on the basis of the Fractional-Dolphin Echolocation-based Support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train SVNN for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network.The performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput, in which the proposed method offers the best performance.

This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection.

The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance.

A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead.

The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks.

This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

Case A uses the context of unfair, non-transparent process of spectrum allocation by the Indian government to private operators that led to Supreme Court (SC) cancel 122 licenses. It gives scope to discuss the relationship between policy and regulatory agencies and their effectiveness, role of other institutes outside telecom sector such as the Prime Minister Office, Empowered Group of Ministers and the Comptroller & Auditor General of India.

Milk cake is a well-renowned khoa-based dairy product in India, produced either from the buffalo milk or using a specific danedar variety of khoa. Under ambient conditions, shelf-life of milk cake is generally up to 3–4 days, whereas under refrigeration conditions, it can last up to 12–14 days. Therefore, the present study aims to evaluate the effect of modified atmosphere packaging (MAP) to enhance the shelf-life and keeping intact freshness of milk cake under refrigerated conditions (4 ± 2 °C).

Different gas concentrations of N₂ and CO₂ (70:30, 50:50 and 90:10) were used as a treatment, whereas control samples were kept under atmospheric air composition. The product was examined for sensory, physicochemical and microbiological parameters at weekly intervals.

The physicochemical and microbiological attributes displayed gradual elevation with progressive storage period in all the samples. However, the overall sensory profile of the product remained acceptable for a longer duration. Most of the quality parameters in control declined more rapidly with a shelf life of 14 days, in comparison to MAP packed samples, where gas flushing with the ratio 70:30 was found to be best suited for extending the shelf life of milk cake up to 28 days at refrigeration temperature.

To extend the shelf life of milk cake, modified atmosphere was provided with different gas ratios to reach a best-suited environment for sensory, storage life and proximate parameters.

The purpose of this paper was to determine and compare nutritional composition of different parts of citrus fruits, namely, Citrus aurantium (peel: albedo, flavedo and pulp: juice, pomace) . This study was conducted through three stages of fruit maturity (green, yellow and orange). Total polyphenols, flavonoids, β-carotene, total chlorophyll, ascorbic acid, acidity, total soluble solid and the minerals (Ca, Mg, Zn, Na, K, Fe, Mn Cu) were evaluated. Moreover, the relationship between the total polyphenol, flavonoids and the antioxidant activity was determined.

Total polyphenols were determined by the Folin-Ciocalteu method. Flavonoids were assessed by the aluminum chloride colorimetric method. Ca, Mg, Zn, Fe, Mn and Cu were measured using an atomic absorption spectrophotometer. K and Na contents were determined by using a flame photometer. Other nutritional composition was determined by volumetric method.

The result showed that the concentrations of antioxidants, total polyphenols, flavonoids, vitamin C, total chlorophyll, total acidity and antioxidant activity (DPPH%) decreased during the maturity of fruit while the value of pH and β-carotene increases. In addition, the concentrations of the most minerals decreased through the maturation of fruit except Na. Anova statistical analysis of all the studied chemical variables was shown significant differences between all the samples. The pH, total soluble solids, β-caroteniod: orange juice had the highest average while green juice had the lowest. Ascorbic acid, total acidity, total chlorophyll: green juice had the highest average while orange juice has the lowest. Total phenol, K, Mg, Fe, Cu, Mn: green flavedo had the highest average while orange juice had the lowest. Flavonoid, antioxidant activity (DPPH%), Ca, Zn: green albedo had the highest average while orange juice had the lowest. Na: orange flavedo had the highest average while green juice had the lowest. The correlations between total polyphenol, flavonoid and antioxidant capacity were significantly higher (R = 0.935 and 0.960, respectively).

The stage of maturity affects nutritional composition in Citrus aurantium fruits (C. aurantium) peel and pulp, where some minerals of composition increased and others decreased, according to the results. This is the first study on comparing the nutritional composition of pulp: juice, pomace and peel: albedo, flavedo of Citrus aurantium L. during maturity in Syria, and it was also not found in previous works.

Although milk is considered a vital source of nutrition for the livelihood, as the beginning of civilization, its antioxidant potential is required to be improved. This paper aims to increase antioxidant activities in milk and soy milk (Glycine max L.) has been found as a good compatible source. Hence, the present work was planned to investigate the impact of blending of different proportions of soy milk with standardized milk (i.e. 50 per cent soy milk with 50 per cent standardized milk, 60 per cent soy milk with 40 per cent standardized milk, 70 per cent soy milk with 30 per cent standardized milk and 100 per cent standardized milk as control sample) on antioxidant properties and sensory attributes of the blends.

Antioxidant properties of the blends were determined in terms of 2,2-diphenyl-1-picrylhydrazyl, ferric reducing antioxidant power and total phenol content. Sensory evaluation was carried out using a nine-point hedonic scale based on four selected quality attributes (i.e. smell, taste, color and appearance and mouthfeel) and ranked for their quality traits by fuzzy modeling.

A blend containing 70 per cent soy milk with 30 per cent standardized milk showed the highest antioxidant activities as compared to control milk sample (p < 0.05). Blend of 50 per cent soy milk with 50 per cent standardized milk was found most acceptable on the basis of sensory analysis and its antioxidant activities were also appreciable as compared to control milk sample (p < 0.05). Smell, taste, color and appearance and mouthfeel influenced the acceptability of formulated milk samples in decreasing order of significance (p < 0.05).

The present attempt has been made to formulate liquid milk by mixing standardized milk and soy milk, in which enhancement in the antioxidant activities of standardized milk and masking of off-flavor of soy milk is done. It is not done before and it may improve the nutritional and commercial value of the milk. Further, fuzzy modeling used for choosing appropriate blend in terms of sensory scores is also found as a novel approach.

This study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.

This study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.

Six clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.

This study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.

Enormous amount of biomedical wastes (BMW) produced everyday across the world. Management of BMW depends on adherence to protocol. BMW management at generation point, definitely, depends upon the awareness, attitudes and practises of health-care staff, the purpose of this study will assess the awareness, attitude and practise regarding different aspects of BMW.

An observational with appropriate checklists, and a cross-sectional study, involving questionnaires, was conducted during 7-24 January 2016. The existing system of BMW management, funds, resources, etc., knowledge, attitude and practises about BMW were assessed amongst 273 health-care workers in selected public health-care institutes of Karnataka.

Of 273 study participants, majority (54%) of them have not received any training pertaining to BMW. The results showed a poor level of knowledge and awareness of BMW management amongst health-care personnel. Merely, 43% of the participants correctly knew the categorization of BMW and its disposal in proper colour-coded bins/bags. Awareness is very poor amongst the lower age group, male participants, lab-technicians/pharmacists and supporting staff. Doctors were good at theoretical knowledge such as rules, legislation and public-health importance of BMW management than the practical aspects such as categorization and colour-coding. Further, the attitude of health-care staff is favourable about BMW. Immunization for hepatitis-B was very poor amongst waste handlers (43%).

As the awareness and practise regarding BMW management were poor across different health-care staff there is a need to conduct periodic training and regular monitoring with special focus on the proper use of personal protective equipment. Further, precautionary immunization should be provided, especially waste handlers and sanitary workers.

The institutional conditions of primary care provision remain understudied in low- and middle-income countries. This study analyzes how primary care doctors cope with medical uncertainty in municipal clinics in urban India. As street-level bureaucrats, the municipal doctors occupy two roles simultaneously: medical professional and state agent. They operate under conditions that characterize health systems in low-resource contexts globally: inadequate state investment, weak regulation and low societal trust. The study investigates how, in these conditions, the doctors respond to clinical risk, specifically related to noncommunicable diseases (NCDs).

The analysis draws on year-long ethnographic fieldwork in Pune (2013–14), a city of three million, including 30 semi-structured interviews with municipal doctors.

Interpreting their municipal mandate to exclude NCDs and reasoning their medical expertise as insufficient to treat NCDs, the doctors routinely referred NCD cases. They expressed concerns about violence from patients, negative media attention and unsupportive municipal authorities should anything go wrong clinically.

The study contextualizes street-level service-delivery in weak institutional conditions. Whereas street-level workers may commonly standardize practices to reduce workload, here the doctors routinized NCD care to avoid the sociopolitical consequences of clinical uncertainty. Modalities of the welfare state and medical care in India – manifest in weak municipal capacity and healthcare regulation – appear to compel restraint in service-delivery. The analysis highlights how norms and social relations may shape primary care provision and quality.