Artificial intelligence-enabled business models aim to improve decision-making, operational efficiency, innovation, and productivity. The presented systematic literature review is conducted to highlight elucidating the utilization of artificial intelligence (AI) methods and techniques within AI-enabled businesses, the significance and functions of AI-enabled organizational models and frameworks, and the design parameters employed in academic research studies within the AI-enabled business domain. We reviewed 39 empirical studies that were published between 2010 and 2023. The studies that were chosen are classified based on the artificial intelligence business technique, empirical research design, and SLR search protocol criteria. According to the findings, machine learning and artificial intelligence were reported as popular methods used for business process modelling in 19% of the studies. Healthcare was the most experimented business domain used for empirical evaluation in 28% of the primary research. The most common reason for using artificial intelligence in businesses was to improve business intelligence. 51% of main studies claimed to have been carried out as experiments. 53% of the research followed experimental guidelines and were repeatable. For the design of business process modelling, eighteen AI mythology were discovered, as well as seven types of AI modelling goals and principles for organisations. For AI-enabled business models, safety, security, and privacy are key concerns in society. The growth of AI is influencing novel forms of business.

The firmness of tomatoes is a considerable parameter for the evaluation of harvest time, shelf life, and ripeness. The firmness of tomatoes provides guidance in the distribution and transportation of tomatoes. In the post harvest procedure, the tomato starts losing its firmness and transforming into a rotten state during transportation in the supply chain due to variations in environmental conditions. The cold supply chain for the transportation of tomatoes reduces the loss and maintains the quality of the tomatoes by controlling the environmental conditions. The monitoring of the cold supply chain is crucial for maintaining the quality of tomatoes and overcoming the effect of ambient temperature on tomatoes during logistics. In this study, an IoT and Whale Optimization Algorithm based temperature prediction system for the cold supply chain is presented. The ambient and tomato’s temperatures were collected, as well the stable temperature under the variation conditions computed with the Whale Optimization Algorithm for performance improvement. The Extreme Learning Machine of Artificial Intelligence was applied for the predictions. The performance evaluation is done by using precision, recall, and f-measure accuracy metrics. The results of the study show the outstanding performance of the proposed approach rather than the Decision Tree, Linear Model, Naïve Bays, Random Forest, and Support Vector Machine models.

Indoor air pollution is more dangerous for residents. So, it is necessary to monitor the quality of indoor air and take some preventive steps to reduce the possible dangers to the health of the inhabitants. The cost and maintenance factors of air quality (AQI) systems lead the researchers to model, design, and implement low-cost indoor AQI monitoring systems. In this research, we proposed an indoor AQI monitoring system with a data-driven model to predict the AQI through the Neural Network Algorithm and Block-chain. The Internet of Things (IoT) connects and processes data, and low-cost sensors collect the data from the environment. The Indoor Air Quality system consists of temperature, humidity, Carbon Di Oxide, Particulate Matter, Carbon Mono Oxide, and LPG. The data are collected from five different sensors, and the NN decision-making model is used to predict the AQI to prevent harmful situations. The suggested IoT-based smart block-chain technology plays a vital role by imparting scalability, privacy, and reliability. This study will work effectively with ease of use, cost-effectiveness, and maintenance of the entire system.

In recent years, food traceability has become one of the emerging applications of Blockchain to strengthen the aspects of anticounterfeiting and quality control. This paper intends to suggest a methodology to improve traceability and transparency in food production and supply chain using Blockchain and the Internet of Things (IoT). Typical methods of the supply chain have loopholes, which are misused by antisocial groups to distribute unhygienic substandard food products. Blockchain technology guarantees the source of origin to a customer and enables traceability, tracking, and transparency which assists to increase accountability in the food production industry by confirming customer safety and protection, developing confidence, and enhancing the quality of service.

As world is transforming to digital era so agriculture tends towards the IoT based smart systems with smarts objects. Smart object helps in monitoring and performing smart actions according to situations without human intervention. Real time monitoring of agriculture fields requires efficient and reliable services for cost reduction, efficient management and making smart decisions. IoT, as a critical Industry 4.0 enabler emerges smart agriculture technologies for cost reduction, increase production and advanced Big data analytics for smart decisions for further improvements. However, the agriculture with limited resources is facing challenges to change the longstanding production and meet current requirements. This study aims to fulfil the gaps related to quality of system by transforming conventional agriculture to IoT-enabled smart systems. An industry-led study demonstrates how to make the reliable smart systems based on IoT technologies with emerging Industry 4.0 and improve the production rate to fulfill the current needs of era.

Rice is a fundamental staple food commodity all around the world.Globally, it is grown over 167 million hectares and occupies almost 1/5 th of total cultivated land under cereals.With a total production of 782 million metric tons in 2018.In Pakistan, it is the 2 nd largest crop being produced and 3 rd largest food commodity after sugarcane and rice.The stem borers a type of pest in rice and other crops, Scirpophaga incertulas or the yellow stem borer is very serious pest and a major cause of yield loss, more than 90% damage is recorded in Pakistan on rice crop.Yellow stem borer population of rice could be stimulated with various environmental factors which includes relative humidity, light, and environmental temperature.Focus of this study is to find the environmental factors changes i.e., temperature, relative humidity and rainfall that can lead to cause outbreaks of yellow stem borers.this study helps to find out the hot spots of insect pest in rice field with a control of farmer's palm.Proposed system uses temperature, relative humidity, and rain sensor along with artificial neural network to predict yellow stem borer attack and generate warning to take necessary precautions.result shows 85.6% accuracy and accuracy gradually increased after repeating several training rounds.This system can be good IoT based solution for pest attack prediction which is cost effective and accurate.

In consideration of the reduced chaotic range and susceptibility of a single chaotic map, we exploit the 4D-hyperchaotic system for creating three S-boxes i.e., red, green and blue S-boxes and a logistic map to transform a plain image into DNA strands. Afterwards, a logistic map based fake image is also generated which is also mapped to deoxyribonucleic acid (DNA) strands. Then DNA operations based on logistic map sequence are performed among the DNA strands and the resultant strands are decoded. The decoded strands are substituted by three substitution-boxes (s-boxes) to create an encrypted image. In this research, a cryptanalysis driven design approach is used to prove the security of a proposed encryption scheme. The proposed scheme operates on numerous image dimensions N $\times $ M and different image file sizes and formats. Experimental results and analysis are completed for visual analysis, key space, key sensitivity, energy analysis, homogeneity analysis, contrast analysis, entropy analysis, histogram analysis, correlation analysis, chosen-plaintext attacks, number of pixels change rate (NPCR), universal average changing intensity (UACI), mean absolute error, robustness against noises and occlusion attacks and encryption efficiency analysis. The visual as well as numerical simulations demonstrate that the proposed algorithm is safe and reliable.

The Users are increasingly worried and conscious about privacy issues that appear during browsing the web. Web extensions such as anti-tracking extensions, Ad-blockers, anonymity, and privacy plug-ins. Assure to protect users and their privacy from third-party tracking systems. We introduce the experimental campaign to benchmarking well-known plug-ins for web privacy protection to date in this work. We set up a testbed to automatically browse usual website pages and pick five popular freeware plugins when collecting navigation data. We assessed the collected data to evaluate each plug-in, considering both performance and privacy-protection angles. We found mainly famous tracker-blockers are relatively efficient in detecting as well as blocking third-party trackers. Specifically, by default, Ghostery does not provide protection; if appropriately set enabled, it provides the best protection from third-party trackers (93.99% of third-party trackers blocked). We also assessed the tracker-blockers effectiveness on client web QoE and bandwidth usage. Up to 30% bandwidth usage decreases by enabling tracker-blockers. For example, Ghostery decreases data to download by 40% and Disconnect with a 36.45% reduction. Ghostery is also faster than the baseline, with page loading 32.37% acceleration. Privacy Badger shows a negative effect on load time, with only 4.53% improvement Disconnect and Ghostery provide the best trade-off among web page quality and protection. However, Ghostery requires a manual configuration step to get the best protection from third-party tracking domains, which is difficult for users. Our study allows developers and researchers to understand the Internet's privacy threats better, possibly better performing privacy-preserving tools.

Border Gateway Protocol (BGP) is the routing protocol for routing information between autonomous systems (AS) on the Internet. Back in 1989, BGP was not developed with a security perspective. Therefore, there are many security concerns regarding BGP, and it is highly vulnerable to malicious attacks. Due to rapid development in Internet technology, the Internet is filled with malicious users. It is not challenging to hijack someone's address space and use it for malicious activities such as denial-of-service attacks (DoS attacks) and spamming. Our aim behind this research work is to figure out and discuss all the techniques regarding BGP prefix hijacking and design a system that can be used to detect IP prefix hijacking attacks and facilitate mitigation. In this type of hijack attack, to avoid Multiple Origin AS (MOAS) conflicts, the attacker announces a hijacked prefix with AS number belongs to victim AS; this creates the illusion that BGP speaker has a direct connection with victim AS. To accurately detect IP prefix hijack attacks, we design a system called Prefix Hijack Detection System (PHDS). To test our system, we have collected all the Autonomous Systems (ASes) of Pakistan and their prefixes using RIPEstat API. PHDS collect BGP updates for every prefix using RIPEstat API. To monitor all 5,845 prefixes of Pakistan, we have collected 3.35 million BGP updates; all this data is collected from November 03, 2018, to November 20, 2018. We have monitored these prefixes through PHDS and found our system correctly detecting all types of IP prefix hijacks. Therefore, this system is useful for early detection of IP prefix hijack attacks. PHDS detects 47,223 malicious updates out of 3.35 million BGP updates. PHDS detected 983 unique IP prefix hijack attacks from 47,223 malicious updates. Hijack, a prefix, and it's AS is the most common type of attack; PHDS detected 983 prefix hijack attacks, and out of these, 898 are hijacked a prefix, and its AS.

An inconsistent and ambiguous Software Requirement Specification (SRS) document results in an erroneous/failed software project. Hence, it is a serious challenge to handle and process complex and ambiguous requirements. Most of the literature work focuses on detection and resolution of ambiguity in software requirements. Also, there is no standardized way to write unambiguous and consistent requirements. The goal of this research was to generate an ambiguity-less SRS document. This paper presents a new approach to write ambiguity-less requirements. Furthermore, we design a framework for Natural Language (NL) to Controlled Natural Language (CNL) (such as Semantic Business Vocabulary and Rules (SBVR)) transition and develop a prototype. The prototype also generates Resource Description Framework (RDF) representation. The SBVR has a shared meaning concept that minimizes ambiguity, and RDF representation is supported by query language such as SPARQL Protocol and RDF Query Language (SPARQL). The proposed approach can help software engineers to translate NL requirements into a format that is understandable by all stakeholders and also is machine processable. The results of our prototype are encouraging, exhibiting the efficient performance of our developed prototype in terms of usability and correctness.

Recent advancements in drone technology are opening new opportunities and applications in various fields of life especially in the form of small drones. However, these advancements are also causing new challenges in terms of security, adaptability, and consistency. This research discusses the drone technology, area of usages, citizen multi-objective uses, drones security, protection, and secrecy apprehensions, drone current intimidations and susceptibilities, existing approaches for drone cyber-security methods, security threats to drones and data sources for current literature review. Small drones are proving to be a new opportunity for the civil and military industries. The small drones are suffering from architectural issues and the definition of security and safety issues. The rapid growth of the Internet of Things (IoT) opens new dimensions for drone technology but posing new threats as well. The tiny flying intelligent devices are challenging for the security and privacy of data. The design of these small drones is yet not matured to fulfill the domain requirements. The basic design issues also need security mechanisms, privacy mechanisms and data transformations.

Modified source code validation is done by regression testing. In regression testing, the time and resources are limited, in which we have to select the minimal test cases from test suites to reduce execution time. The test case minimization process deals with the optimization of the regression testing by removing redundant test cases or prioritizing the test cases. This study proposed a test case prioritization approach based on multiobjective particle swarm optimization (MOPSO) by considering minimum execution time, maximum fault detection ability, and maximum code coverage. The MOPSO algorithm is used for the prioritization of test cases with parameters including execution time, fault detection ability, and code coverage. Three datasets are selected to evaluate the proposed MOPSO technique including TreeDataStructure, JodaTime, and Triangle. The proposed MOPSO is compared with the no ordering, reverse ordering, and random ordering technique for evaluating the effectiveness. The higher values of results represent the more effectiveness and the efficiency of the proposed MOPSO as compared to other approaches for TreeDataStructure, JodaTime, and Triangle datasets. The result is presented to 100-index mode relevant from low to high values; after that, test cases are prioritized. The experiment is conducted on three open-source java applications and evaluated using metrics inclusiveness, precision, and size reduction of a matrix of the test suite. The results revealed that all scenarios performed well in acceptable mode, and the technique is 17% to 86% more effective in terms of inclusiveness, 33% to 85% more effective in terms of precision, and 17% minimum to 86% maximum in size reduction of metrics.

Agriculture is suffering from the problem of low fertility and climate hazards such as increased pest attacks and diseases. Early prediction of pest attacks can be very helpful in improving productivity in agriculture. Insect pest (whitefly) attack has a high influence on cotton crop yield. Internet of Things solution is proposed to predict the whitefly attack to take prevention measures. An insect pest prediction system (IPPS) was developed with the help of the Internet of Things and a RBFN algorithm based on environmental parameters such as temperature, humidity, rainfall, and wind speed. Pest Warning and Quality Control of Pesticides proposed an economic threshold level for prediction of whitefly attack. The economic threshold level and RBFN algorithm are used to predict the whitefly attack using temperature, humidity, rainfall, and wind speed. The seven evaluation metrics accuracy, f-measures, precision, recall, Cohen's kappa, ROC AUC, and confusion matrix are used to determine the performance of the RBFN algorithm. The proposed insect pest prediction system is deployed in the high influenced region of pest that provides pest prediction information to the farmer to take control measures.

In the first place, the paper analyses the blood flow patterns in a capillary during the existence of a uniform external magnetic field by a hybrid CPU/GPU approach. The blood flowing through the capillary is supposed to be Newtonian; while the flow is incompressible and laminar. Magnetic Nanoparticles have been considered as therapeutic agent for the magnetic targeted drug delivery in the defence against cancer. However, the problem is expressed as a boundary value problem containing a system of partial differential equations in order to study the flow field and magnetic Nanoparticles. Finite element discretization is applied to resolve the system of equations which contains a large sparse system of equations requiring high computation. The CPU/GPU method serves as a platform to deal the wide-ranging computations in parallel. Therefore, the solution times can significantly be reduced by this platform as compared to the application of CPU. This allows more effective examination of different mathematical models and their leading parameters. The influence of the magnetic nanoparticle radius \( R_{M} \), capillary radius R, pressure P, magnetic field intensity H on the velocity profile of blood and magnetic nanoparticles have been investigated in terms of the magnetic field inputs and model. Secondly, the numerical solutions for velocity of blood and velocity of particles are computed along with the observation that an increase in magnetic field leads to increase in the flow pattern. Finally, the simulation concludes that the magnetic parameters have a key role to control the velocity profile.

Requirements engineering (RE) is an important phase of software engineering. During this phase, an important set of activities are carried out to manage requirements elicitation, verification, prioritization and validation. Dimension and dynamics of software development are changing with the passage of time. Economic growth in different sectors is increasing the demand for software development. This enhancement has introduced the concept of Value Base Software (VBS) development. Requirements prioritization is playing a vital role in ordering requirements to support the release planning of the software. A prioritization process is considered as highly complex process and depends on the nature and size of requirements. VBS systems are entirely different from typical software development, and prioritization process for VBS is also very challenging. A need arises from the provision of prioritization techniques to support the technical and business aspects-based prioritization. Existing techniques are not qualified to meet the expectation of the industry for VBS development. Therefore, this research contribution is an effort made, based on an intelligent decision support system for requirements prioritization in the domain of VBS system. Aspects based requirements prioritization is applied to many requirements and results are produced in two clusters. Results are claimed as a prioritized list of requirements for traditional as well as value-based system.

Several techniques were designed during last few years to improve the performance of deep architecture by means of appropriate loss functions or activation functions. Arguably, softmax is the traditionally convenient to train Deep Convolutional Neural Networks (DCNNs) for classification task. However, the modern deep learning architectures have exposed its limitation towards feature discriminability. In this paper, we offered a supervision signal for discriminative image features through a modification in softmax to boost up the power of loss function. Amending the original softmax loss and motivated by the A-softmax loss for face recognition, we fixed the angular margin to introduce a unit margin softmax loss. The improved alternative form of softmax is trainable, easy to optimize and stable for usage along with Stochastic Gradient Descent (SGD) and Laplacian Smoothing Stochastic Gradient Descent (LS-SGD) and applicable to classify the digits in image. Experimental results demonstrate a state-of-the-art performance on famous database of handwritten digits the Modified National Institute of Standards and Technology (MNIST) database.

Skin wounds either minor or chronic may heal up with different time durations. But, this time duration of healing could not be easily predicted as healing is affected by different factors, e.g., age, nutrition, medication, and surroundings. Despite these factors, wound characteristic also plays a role in the healing process. Wound characteristics include wound size, wound type, internal and external wound environment, body temperature, body oxygenation, wound hydration, and infection. Therefore, monitoring of wound healing also required careful consideration of wound characteristics. Although the healthcare domain contains many applications for detection and monitoring of diseases, the wound care domain requires efficient techniques and sensing systems for the identification of wound biomarkers such as temperature, blood pressure, oxygen, and infection status of wound using biosensors. In the current research, we provide a wound care solution based on a biosensor-based sensing system to measure basic biomarkers, considered as major wound characteristics, i.e., body temperature and body oxygenation, and design a fuzzy inference system to predict their effect on wound hydration, which ultimately recommends necessary actions to boost healing.

The recommendation systems are getting important due to their significance in decision making, social and economic impact on customers and getting detailed information relevant to a required product or a service. A challenge in getting true recommendations in terms of relevance is the heterogenous nature of data (likes, ratings, reviews, etc.) that a recommendation engine has to cope with. This paper presents an intelligent approach to handle heterogeneous and large-sized data of user reviews and generate true recommendations for the future customers. The proposed approach makes use of Apache Cassandra to efficiently store data (such as customer reviews, feedback of hotel customers) having context properties such as awareness and knowledge of the tourists, personal preferences (such as ratings, likes, etc.) and location of the users. This system consists of three main components: the web front-end, the data storage and the recommendation engine to gain recommendations efficiently. The recommendation engine is relying on Euclidean distance and Collaborative Filtering (CF) to measure similarities in users' review or items' features. Our hotel recommender approach has bifold contribution as it has ability to handle heterogeneous data with the help of big data platform and it also provides accurate and true recommendations.

Semantics of Business Vocabulary and Business Rule (SBVR) were introduced to describe the business process in most formal way. SBVR specify business rules. Semantics of Business Vocabulary and Business Rules is introduced by standard of Object Management Group (OMG) in 2008. Complex business rules are formally defined by Semantics of Business Vocabulary and Business Rules (SBVR). This paper provides a novel approach for translating SBVR specification of software requirements into XML schema. The purpose of this paper is to generate XML from SBVR instead of NL natural language specification because due to informal nature of natural language the generation of XML form NL will be resulted in lesser accuracy. SBVR Bridge the gap between humans and machines as human can understand simple natural language sentences while this natural language has ambiguous nature for machine and IT specialists. The VeTIS tool is used for the transformation purpose. SBVR rules generated as first output and these rules gave as input to transaction editor that extract SBVR vocabulary such as noun concept, fact type etc. In the last step these SBVR elements are replaced by elements that are called tags of XML vocabulary.