Secure and reliable exchange of information between devices is crucial for any network in the current digital world. This information is maintained on storage devices, routing devices, and communication over the cloud. Cryptographic techniques are used to ensure the secure transmission of data, ensuring the user’s privacy by storing and transmitting data in a particular format. Using encryption, only the intended user possessing the key can access the information. During data or essential transmission, the channel should be secured by using robust encryption techniques. Homomorphic Encryption (HE) techniques have been used in the past for this purpose. However, one of the flaws of the conventional HE is seen either in its slow transmission or fast key decryption. Thus, this paper proposes an optimized Homomorphic Encryption Chinese Remainder Theorem with a Rivest-Shamir-Adleman (HE-CRT-RSA) algorithm to overcome this challenge. The proposed Technique, HE-CRT-RSA, utilizes multiple keys for efficient communication and security. In addition, the performance of the HE-CRT-RSA algorithm was evaluated in comparison with the classical RSA algorithm. The result of the proposed algorithm shows performance improvement with reduced decryption time. It is observed that the proposed HE-CRT-RSA is 3–4% faster than the classical Rivest-Shamir-Adleman (RSA). The result also suggests that HE-CRT-RSA effectively enhances security issues of the cloud and helps to decrease the involvement of intruders or any third party during communication or inside the data/server centers.

Software-Defined Networking (SDN) offers a centralized network management approach that can effectively address the complex and varied traffic demands characteristic of Industrial Internet of Things (IIoT) environments by decoupling the control plane from the data plane. The centralized control architecture of SDN necessitates the performance optimization of controllers to manage diverse traffic efficiently within IIoT applications. This paper explores the criteria for selecting controllers in SDN-enabled IIoT (SD-IIoT) environments, utilizing the Less Complex Analytic Network Process (LC-ANP) to establish their prioritization. A ranking system for SD-IIoT controllers is formulated using LC-ANP, and experimental validation of this method underscores its effectiveness in optimizing controller performance. The proposed approach enhances the overall efficiency of SDN-enabled IIoT networks, as evidenced by experimental evaluations measuring delay, throughput, packet loss ratio, and jitter across five different topologies with varying nodes and edges. The experiments indicate an overall increase in the average throughput, and a decrease in delay, jitter, and packet loss ratio. The results also show that the suggested strategy and proposed controller surpass the benchmark controller in complex network topologies. These results confirm the method’s capacity to significantly improve network performance in SD-IIoT applications.

Many years have passed since sustainable development (ESD) became world famous in the Brundtland Commission publication, “Our Common Future”; however, still many universities are unaware of it or confuse it with environmental sustainability. The ESD concept contrasts with existing teaching methods, mainly focused into resource depletion. This paper explores the perceptions of teachers and students towards including Education for Sustainable Development (ESD) into the teacher training programme in the Faculty of Education at the University of Buea, Cameroon. The main purpose of the paper  is to identify current ESD related teaching and learning in a teacher training programme and the opportunities for, and barriers to, further extensions of ESD. The results indicate that there is general support for the inclusion of ESD in the curriculum, but there is considerable uncertainty expressed by lecturers and students concerning how this can best be done. There is a general concern that additional embedding into the teacher training programme might lead to reductions in the amount of core subject matter being taught. The programme and ESD agendas are to an extent seen by lecturers and students as conflicting. ESD is viewed mainly in terms of curriculum content as opposed to the pedagogy employed.

At the World Education Forum (Dakar, 2000) the international community reaffirmed its vision of Education for All (EFA) through the Dakar Framework for Action which set six major EFA goals: (i) early childhood care and education, (ii) free and compulsory primary education for all, (iii) appropriate learning and life skills for young people and adults (iv) adult literacy (v) gender parity and equality and (vi) quality of education. Four quantifiable goals were set for 2015: increasing adult literacy by 50%, ensuring universal primary education, gender parity and equality, and quality of education, measured by the survival rate to grade 5.

While some tangible progress has been made since 2000 towards the EFA goals, many countries still lag behind. In particular, the majority of the countries which were furthest away from UPE in 2000 have made insufficient progress towards achieving free and compulsory primary education by 2015. The most significant limitations are the lack of sufficient space, resources and teachers to address the learning needs of the most disadvantaged children. The international agenda also tends to overlook the national or local contexts; traditional, indigenous and cultural views of an education of quality, as well as of the role and status of the educator or teacher. This can lead to conceptual and empirical flaws in international research made in a non-participatory manner. The purpose of this paper is to review and analyze existing policies and practices that have been undertaken by the governments of Cameroon and Nigeria to address the teacher gap in basic education, indicating their commitment to the goals of Education for All.

The aim of this paper is to explore the effects of globalization on education in developing countries and how the teachers at some higher education institutions in the Southwest region of Cameroon understand education for sustainable development and what challenges they face with the implementation of education for sustainable development in the teacher education curriculum.

Design/methodology/approach

The methods employed included documentary analysis, interviews and observation.

Using a multi case study design, data from semi-structure interviews and observations with teachers in the classroom and a sample of school leaders were collected and analyzed. The study adopted a qualitative content analysis to analyze data from interviews and observations.

Findings

The study shows that with the impact of globalization there is no systematic and focused preparation of secondary school teachers for education for sustainable development in the teacher training programmes. The findings also indicate that teachers were positive toward the inclusion of education for sustainable development into the teacher training curriculum. Some challenges for mainstreaming, implementation and broadening of ESD across the curriculum were identified. It also emphasized the need to integrate education for sustainable development across the curriculum and professional development for teachers in the implementation of ESD.  

Research limitations/implications

The research is limited by its focus on the programme-based implementation of education for sustainable development in teacher training institutions, which did not include any course-based implementation by individual teachers.

Practical implications

Given the effects of globalization, challenges and obstacles for implementing ESD and in order to address the issues, the study argue for joint leadership across the relevant institutional levels (government, university/teacher training colleges and other stake holders), and for the integration of sustainable development throughout the curriculum, rather than being taught as a separate subject.

Originality/value

The study provides empirical evidence for some of the major challenges teacher trainers face for implementing education for sustainable development in teacher training institutions in Cameroon.

ESD has a transformational role since it aims at encouraging the transformation of education and reorientation of societies in order to reach sustainable development (UNESCO, 2014). Teaching and learning for ESD are encouraged to promote critical thinking, to imagine the future and make decisions in order to empower learners to take action towards building a sustainable society. Pedagogy makes the connection between teaching and learning and is therefore crucial for education’s contribution to sustainable development. This paper examines pedagogical approaches that promote sustainability and how teachers might be empowered to improve pedagogic practice for diverse learners and in challenging context. It explores pedagogical contents of ESD, and the pedagogical challenges educators face when the ESD paradigm is put into practice particularly across different disciplines in the context of teacher education in a developing country. The methods employed included documentary analysis, interviews and observation. The findings are based on interviews with student teachers after their teaching practice placement, as well documentary evidence.  The paper also suggests ways in which educators can address difficulties when trying to infuse the ESD paradigm into teacher education programmes.

In recent years there has been a growing body of literature on the interconnectedness of the Millennium Development Goals (MDGs) and the need to maximize opportunities for education and their linkages. The existing empirical studies confirm some of the arguments about the benefits of education and the linkages to the MDGs.With the developmental outcomes of basic education in mind, this desk study set out to examine in what ways, and under what conditions investment in education is important in relation to the millennium development goals. It provides a synthesis of research on the potential contribution of basic education to achieving the MDGs, focusing on key texts produced by the international institutions. Within this review a key focal point will be the context in which education appears to impact the various MDGs outcomes, which is referred to as the ―enabling environment‖. The report sought to explore the evidence about the contribution of basic education to poverty reduction and the achievement of the MDGs within certain countries or regions. It considered the critical support systems, policy environments and national capacities upon which good quality basic education depends, and assessed the role of basic education in developing and sustaining these.In response to the global call to achieve the MDGs by 2015, many countries are making remarkable progress demonstrating that setting bold, collective goals in the fight against poverty yields results. Expanding access and improving the quality of education are both imperative for MDG progress. A balance must be struck to move both objectives forward.

The inclusion of young children with special needs with their typically developing peers has been the subject of discussion for more than three decades. There are several compelling reasons to create high-quality inclusive programs for young children with special needs in schools. Most countries supports inclusion and research has shown teachers’ ability and success in modifying activities and contexts in such a way that they facilitate the development of young children with special needs. The purpose of this paper is to discuss findings of a small scale study looking at the links between inclusion and classroom practices vis-a-vis the elementary school system in Sweden. The study indicates that local administrators and the school play a pivotal role in making inclusion work. In addition to complying with government requirements, schools and administrators and teachers in early childhood environments set the tone and philosophy of a program. The paper has attempted to look at some questions about effective inclusive education, it is important to gain insight into how inclusive education works.

The integration of immigrant children has emerged as a pressing yet controversial issue in public debates around the world over the past two decades. Education of immigrant children is one of the numerous pedagogical phenomena that continue to arouse questions concerning the efficiency of dealing with them. This study examines the education of immigrant school children in Sweden, focusing on Swedish instruction, cultural awareness, parent participation, and teacher preparation for working with immigrant students. Data collection involved interviews with teachers at two schools and with immigrant children. A qualitative study based on content analysis of semi-structured interviews with immigrant students explain the different challenges posed by the integration process of immigrant children in schools. Based on the findings the study offer recommendations in order to improve the academic and social integration of immigrant children.

It widely recognized that internationalization of higher education is one of the key trends of development. Internationalizing the curriculum is a strategy for the internationalization of higher education. This paper describes and analyze a case study of two universities that explored the views of some academic staff and students regarding the relevance of internationalization to their curricula. The study is based on document analysis, interviews and surveys, which were designed to gather data on internationalization practices and objectives from university staff and students. The paper highlights and integrates important considerations for internationalization with various key issues in curriculum development. The study concludes that internationalization for staff, students and the curriculum is providing possible ways for higher education institutions to move forward together and become internationalized.

The Millennium Development Goals (MDGs), which were agreed atUnited Nations General Assembly Millennium Summit in 2000, addresschallenges in poverty reduction, hunger, health, gender equality, education,and environmental sustainability, an ambitious set of development targetsaimed at reducing poverty and improving the lives of people all around theworld by 2015. Over the past decade, notable progress has been made oneach individual MDG even in the poorest countries and the most difficultcircumstances. Such success shows that the MDGs can be achieved. Indeed,the MDGs have led to unprecedented commitments, partnerships andprogress in combating poverty and hunger, in improving school enrolment,in fostering gender equality and in extending equal access to health care. Yetprogress is uneven between and within regions and countries and often tooslow to meet the 2015 deadline. There is a growing realization that, withoutrenewed commitment and concerted action, some countries will not reach allof the MDGs. In recent years there has been a growing body of literature onthe interconnectedness of education and the Millennium Development Goals(MDGs). The purpose of this paper is to synthesize global evidencegenerated through various MDG Country Reports and supplementarydocuments that focus on trends toward progress and on the gaps anddisparities that have arisen. The paper will help to establish a betterunderstanding of how investment in education can lead to developmentoutcomes that aid the achievement of the MDGs.

The main aim of this research was to explore the present role of the secondary school principals and the challenges they face in carrying out their duties in the Northwest region of Cameroon. The research, which was exploratory and conducted in the interpretive paradigm, is a case study which was carried out at eight secondary schools. Working in an interpretivist research paradigm within a qualitative research design, the study used semi-structured interviews, observation and document analysis methods.The researcher shadowed each principal for a week of intensive observation and interviewing.

The findings reveal that tradition, culture and political contexts are crucial factors in understanding what the secondary school principals in Cameroon do and how they conceptualize principalship. The findings show that discussions with parents, staff and desk work were found to be the major job functions taking up most of the principals' time. The findings reveal that principals are agents of accountability and management; they are involved with different regional and local services; are concerned with the safety and security of students; and their day is extended through participation in parent teacher associations and with other local community activities.

However, the findings also indicate the multi-varied nature of the principal's role. The principal's day was generally hectic in pace, varied in its composition, discontinuous and superficial in any pursuit of tasks, with the unexpected always as one of the few certainties of the job. The dominant model was that of the transactional leader in fixing things, managing and coping in order to maintain the smooth operation of the school.

Results indicated that deputy principals presented similar feedback to that presented by principals. The major difference being in the way deputy principals perceived tomorrow’s principalship ─ one beyond the transactional model of principal as administrator to the transformational model of principal as leading professional.

At the World Education Forum (Dakar, 2000) the international community reaffirmed its vision of Education for All (EFA) through the Dakar Framework for Action which set six major EFA goals: (i) early childhood care and education, (ii) free and compulsory primary education for all, (iii) appropriate learning and life skills for young people and adults (iv) adult literacy (v) gender parity and equality and (vi) quality of education. Four quantifiable goals were set for 2015: increasing adult literacy by 50%, ensuring universal primary education, gender parity and equality, and quality of education, measured by the survival rate to grade 5.

While some tangible progress has been made since 2000 towards the EFA goals, many countries still lag behind. In particular, the majority of the countries which were furthest away from UPE in 2000 have made insufficient progress towards achieving free and compulsory primary education by 2015. The most significant limitations are the lack of sufficient space, resources and teachers to address the learning needs of the most disadvantaged children. National capacity to collect and process data and information for informed policymaking and implementation to hire sufficient numbers of qualified teachers is often weak. As the demand for quality teachers increases, disparities in teacher qualifications only worsens; schools and areas with better working conditions and higher salaries bid away the better qualified teachers from already difficult-to-staff schools.

The international agenda also tends to overlook the national or local contexts; traditional, indigenous and cultural views of an education of quality, as well as of the role and status of the educator or teacher. This can lead to conceptual and empirical flaws in international research made in a non-participatory manner.

The purpose of this paper is to review and analyze existing policies and practices that have been undertaken by the governments of Cameroon and Nigeria to address the teacher gap in basic education, indicating their commitment to the goals of Education for All.

Democratic classroom practices are all strategies adopted by classroom teachers to actively engage students in the learning processes. Considerable literature assessing influence of Civic Education on youths’ active participation in a democratic society exists. Not much have reported an empirically conducted study on classroom best practices adopted by Civic Education teachers in the Global South. This paper examines classroom democratization by Civic Education teachersin South East Nigeria, students’ and teachers’ perceptions of democratic classroom,and challenges confronting classroom democratization. Focus Group Discussion  and Democratic classroom questionnaire were used to elicit information from 151 civic education teachers and 1400 senior secondary school one (SS1) students. Findings revealed that Civic Education teachers in South East Nigeria adopted democratic classroom practices marginally. Recommendation includes more adoption of democratic classroom best practices for development of students’ critical thinking abilities, preparing them to become participatory in their civic duties and reducing crimes among today’s youths.

This report offers an extensive comparative review of school leadership in Sub-Saharan Africa (SSA). It examines and presents evidence from selected educational contexts, to educators, policymakers and other stakeholders interested in successful schools.

Effective school leaders are central to school performance, as they provide direction, motivation and support to teachers, administrators and students, in order to improve education service delivery and learning outcomes. However, this review shows that most SSA countries have no adequate policies to ensure that school principals become effective instructional leaders.

Key challenges in school leadership include inadequate preparation and limited professional development opportunities; limited ICT skills for transformative teaching and learning; corruption, which denies students learning resources; and gender inequality. In many SSA countries, teaching experience remains the main path to principalship; this means that many school heads are ill-prepared to meet the challenges posed by the changing nature of their job.Besides, the recruitment of school leaders is unsystematic and not always based on professional competence. Mechanisms for recruitment include promotion by seniority, rewarding political allegiance, corruption, nepotism and favouritism.

The available literature shows that most countries have introduced governance reforms, including decentralized school models, that require principals to have appropriate skills and knowledge in financial management, instructional leadership and people management in order to meet the challenge of demanding school contexts. However, strategies for training, support and professional development of school leaders remain inadequate.

Hence, the demand for high-quality learning outcomes, combined with additional responsibilities for human and financial management, put pressure on poorly trained school leaders. To address this challenge, most SSA governments should strengthen their policies on school leadership. Education authorities need to identify the school leadership responsibilities that are most effective in improving student learning. Governments should increase resources for training, so that current and future principals can develop relevant skills and attitudes foreffective instructional leadership. Policymakers should make the principals’ role more attractive by improving status and remuneration to attract high-performing leaders.

This regional review should help policymakers across the region make the most of their professional development resources, based on evidence of effectiveness.

Cloud computing is an extremely important infrastructure used to perform tasks over processing units. Despite its numerous benefits, a cloud platform has several challenges preventing it from carrying out an efficient workflow submission. One of these is linked to task scheduling. An optimization problem related to this is the maximal determination of cloud computing scheduling criteria. Existing methods have been unable to find the quality of service (QoS) limits of users- like meeting the economic restrictions and reduction of the makespan. Of all these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm produces the maximum outcomes for scheduling tasks in a heterogeneous environment in a reduced time. Reviewed literature proves that HEFT is efficient in terms of execution time and quality of schedule. The HEFT algorithm makes use of average communication and computation costs as weights in the DAG. In some cases, however, the average cost of computation and selecting the first empty slot may not be enough for a good solution to be produced. In this paper, we propose different HEFT algorithm versions altered to produce improved results. In the first stage (rank generation), we execute several methodologies to calculate the ranks, and in the second stage, we alter how the empty slots are selected for the task scheduling. These alterations do not add any cost to the primary HEFT algorithm, and reduce the makespan of the virtual machines’ workflow submissions. Our findings suggest that the altered versions of the HEFT algorithm have a better performance than the basic HEFT algorithm regarding decreased schedule length of the workflow problems.

Cloud Mobile Ad-hoc Networks (Cloud-MANETs) is a framework that can access and deliver cloud services to MANET users through their smart devices. MANETs is a pool of self-organized mobile gadgets that can communicate with each other with no support from a central authority or infrastructure. The main advantage of MANETs is its ability to manage mobility while data communication between different users in the system occurs. In MANETs, clustering is an active technique used to manage mobile nodes. The security of MANETs is a key aspect for the fundamental functionality of the network. Addressing the security-related problems ensures that the confidentiality and integrity of the data transmission is secure. MANETs are highly prone to attacks because of their properties.In clustering schemes, the network is broken down to sub-networks called clusters. These clusters can have overlapping nodes or be disjointed. An enhanced node referred to asthe Cluster Head (CH) is chosen from each set to overseetasks related to routing. It decreases the member nodes’ overhead and improvesthe performance of the system. The relationship between the nodes and CH may vary randomly, leading to re-associations and re-clustering in a MANET that is clustered. An efficient and effective routing protocol is required to allow networking and to find the most suitable paths between the nodes. The networking must be spontaneous, infrastructure-less, and provide end-to-end interactions. The aim of routing is the provision of maximum network load distribution and robust networks. This study focused on the creation of a maximal route between a pair of nodes, and to ensure the appropriate and accurate delivery of the packet. The proposed solution ensured that routing can be carried out with the lowest bandwidth consumption. Compared to existing protocols, the proposed solution had a control overhead of 24, packet delivery ratio of 81, the lowest average end-to-end delay of 6, and an improved throughput of 80,000, thereby enhancing the output of the network. Our result shows that multipath routing enables the network to identify alternate paths connecting the destination and source. Routing is required to conserve energy and for optimum bandwidth utilization.

Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart healthcare and will continue to be impactful in medical infrastructures in the near future. This paper addressed a key component of security-intrusion detection systems due to the number of web security attacks, which have increased dramatically in recent years in healthcare, as well as the privacy issues. Various intrusion-detection systems have been proposed in different works to detect cyber threats in smart healthcare and to identify network-based attacks and privacy violations. This study was carried out as a result of the limitations of the intrusion detection systems in responding to attacks and challenges and in implementing privacy control and attacks in the smart healthcare industry. The research proposed a machine learning support system that combined a Random Forest (RF) and a genetic algorithm: a feature optimization method that built new intrusion detection systems with a high detection rate and a more accurate false alarm rate. To optimize the functionality of our approach, a weighted genetic algorithm and RF were combined to generate the best subset of functionality that achieved a high detection rate and a low false alarm rate. This study used the NSL-KDD dataset to simultaneously classify RF, Naive Bayes (NB) and logistic regression classifiers for machine learning. The results confirmed the importance of optimizing functionality, which gave better results in terms of the false alarm rate, precision, detection rate, recall and F1 metrics. The combination of our genetic algorithm and RF models achieved a detection rate of 98.81% and a false alarm rate of 0.8%. This research raised awareness of privacy and authentication in the smart healthcare domain, wireless communications and privacy control and developed the necessary intelligent and efficient web system. Furthermore, the proposed algorithm was applied to examine the F1-score and precisionperformance as compared to the NSL-KDD and CSE-CIC-IDS2018 datasets using different scaling factors. The results showed that the proposed GA was greatly optimized, for which the average precision was optimized by 5.65% and the average F1-score by 8.2%.

In the current COVID-19 pandemic era, Learning Management Systems (LMS) are commonly used in e-learning for various learning activities in Higher Education. Learning Analytics (LA) is an emerging area of LMS, which plays a vital role in tracking and storing learners’ activities in the online environment in Higher Education. LA treats the collections of students’ digital footprints and evaluates this data to improve teaching and learning quality. LA measures the analysis and reports learners’ data and their activities to predict decisions on every tier of the education system. This promising area, which both teachers and students can use during this pandemic outbreak, converges LA, Artificial Intelligence, and Human-Centered Design in data visualization techniques, semantic and educational data mining techniques, feature data extraction, etc. Different learning activities of learners for each course are analyzed with the help of LA plug-ins. The progression of learners can be monitored and predicted with the help of this intelligent analysis, which aids in improving the academic progress of each learner in a secured manner. The Object-Oriented Programming course and Data Communication Network are used to implement our case studies and to collect the analysis reports. Two plug-ins, local and log store plug-ins, are added to the sample course, and reports are observed. This research collected and monitored the data of the activities each students are involved in. This analysis provides the distribution of access to contents from which the number of active students and students’ activities can be inferred. This analysis provides insight into how many assignment submissions and quiz submissions were on time. The hits distribution is also provided in the analytical chart. Our findings show that teaching methods can be improved based on these inferences as it reflects the students’ learning preferences, especially during this COVID-19 era. Furthermore, each student’s academic progression can be marked and planned in the department.

The recent developments in fog computing architecture and cloud of things (CoT) technology includes data mining management and artificial intelligence operations. However, one of the major challenges of this model is vulnerability to security threats and cyber-attacks against the fog computing layers. In such a scenario, each of the layers are susceptible to different intimidations, including the sensed data (edge layer), computing and processing of data (fog (layer), and storage and management for public users (cloud). The conventional data storage and security mechanisms that are currently in use appear to not be suitable for such a huge amount of generated data in the fog computing architecture. Thus, the major focus of this research is to provide security countermeasures against medical data mining threats, which are generated from the sensing layer (a human wearable device) and storage of data in the cloud database of internet of things (IoT). Therefore, we propose a public-permissioned blockchain security mechanism using elliptic curve crypto (ECC) digital signature that that supports a distributed ledger database (server) to provide an immutable security solution, transaction transparency and prevent the patient records tampering at the IoTs fog layer. The blockchain technology approach also helps to mitigate these issues of latency, centralization, and scalability in the fog model.

This study assessed readiness of Nigerian Tertiary institutions towards adopting e-learning education as a new normal post COVID-19, identified e-learning packages available for use in the institutions before and during the COVID-19 Lockdown using the E-Learning Survey for Academic Staff and Students of Nigerian Tertiary Institutions (ELSASSoNTI). This research adopted an online survey using a quantitative method of data collection. A structured Google Form questionnaire was shared with academic staff and students of public and private tertiary institutions in Nigeria via different online platforms. Population comprised all academic staff and students of South-East Nigerian Tertiary Institutions. A sample size of 615 academic staff and students responded to the instrument. Data were analyzed using descriptive and inferential statistics. Results revealed that: tertiary institutions in Nigeria are to a larger extent not ready for the adoption of e-learning education approaches as teaching-learning alternative during emergencies. Majority of tertiary institutions except private universities did not adopt any e-learning platform for use before and during the COVID-19 lockdown. There is lack of basic resources, logistics, and inadequate capacity for the effective adoption and implementation of e-learning within Nigerian tertiary institutions. The study thus recommends, among other things; provision of facilities needed for smooth transition to the new normal, training programs to improve the confidence of academic staff and students in using e-learning platforms. These would improve their e-learning readiness, overcome the usual disruption of school activities during emergencies and ensure a sustainable post Covid-19 era in the higher education sector.

The present study measured household hunger in South-East Nigeria amidst the COVID-19 lockdown. A total of 1209 households (urban and rural locations) were sampled. Household hunger was determined using the Radimer-Cornel hunger scale. Results show that before the COVID-19 lockdown, hunger prevalence in the urban areas was 85.5%, whereas prevalence in the rural areas was significantly lower, at a prevalence of 79.9% (7.3% level of association-X-2 = 6.499, p = 0.012). During the COVID-19 lockdown, the prevalence of hunger in the urban areas rose to 98.0% and 99.2% in the rural areas (4.9% level of association was X-2 = 2.888, p = 0.089). It was also observed that the COVID-19 lockdown significantly affected food prices. The major coping strategy employed by households was relying on less expensive foods (81.14%). High household hunger was identified as a short-term cost of the COVID-19 lockdown in South-East Nigeria. Only a few households (16.3%) benefited from the food aid programs and 16.9% from the government palliative cash transfer. It is recommended that the government setup a formidable unit that will develop physical and digital plans for effective implementation during a COVID-19 lockdown situation or other emergencies.

Crime detection is one of the most important research applications in machine learning. Identifying and reducing crime rates is crucial to developing a healthy society. Big Data techniques are applied to collect and analyse data: determine the required features and prime attributes that cause the emergence of crime hotspots. The traditional crime detection and machine learning-based algorithms lack the ability to generate key prime attributes from the crime dataset, hence most often fail to predict crime patterns successfully. This paper is aimed at extracting the prime attributes such as time zones, crime probability, and crime hotspots and performing vulnerability analysis to increase the accuracy of the subject machine learning algorithm. We implemented our proposed methodology using two standard datasets. Results show that the proposed feature generation method increased the performance of machine learning models. The highest accuracy of 97.5% was obtained when the proposed methodology was applied to the Naïve Bayes algorithm while analysing the San Francisco dataset.

The functionality of the Internet is continually changing from the Internet of Computers (IoC) to the “Internet of Things (IoT)”. Most connected systems, called Cyber-Physical Systems (CPS), are formed from the integration of numerous features such as humans and the physical environment, smart objects, and embedded devices and infrastructure. There are a few critical problems, such as security risks and ethical issues that could affect the IoT and CPS. When every piece of data and device is connected and obtainable on the network, hackers can obtain it and utilise it for different scams. In medical healthcare IoT-CPS, everyday medical and physical data of a patient may be gathered through wearable sensors. This paper proposes an AI-enabled IoT-CPS which doctors can utilise to discover diseases in patients based on AI. AI was created to find a few disorders such as Diabetes, Heart disease and Gait disturbances. Each disease has various symptoms among patients or elderly. Dataset is retrieved from the Kaggle repository to execute AI-enabled IoT-CPS technology. For the classification, AI-enabled IoT-CPS Algorithm is used to discover diseases. The experimental results demonstrate that compared with existing algorithms, the proposed AI-enabled IoT-CPS algorithm detects patient diseases and fall events in elderly more efficiently in terms of Accuracy, Precision, Recall and F-measure. 

Mobile ad hoc network (MANET) is a miscellany of versatile nodes that communicate without any fixed physical framework. MANETs gained popularity due to various notable features like dynamic topology, rapid setup, multihop data transmission, and so on. These prominent features make MANETs suitable for many real-time applications like environmental monitoring, disaster management, and covert and combat operations. Moreover, MANETs can also be integrated with emerging technologies like cloud computing, IoT, and machine learning algorithms to achieve the vision of Industry 4.0. All MANET-based sensitive real-time applications require secure and reliable data transmission that must meet the required QoS. In MANET, achieving secure and energy-efficient data transmission is a challenging task. To accomplish such challenging objectives, it is necessary to design a secure routing protocol that enhances the MANET's QoS. In this paper, we proposed a trust-based multipath routing protocol called TBSMR to enhance the MANET's overall performance. The main strength of the proposed protocol is that it considers multiple factors like congestion control, packet loss reduction, malicious node detection, and secure data transmission to intensify the MANET's QoS. The performance of the proposed protocol is analyzed through the simulation in NS2. Our simulation results justify that the proposed routing protocol exhibits superior performance than the existing approaches. 

The rapid growth and development of financial technological advancement (Fintech) services and innovations have attracted the attention of scholars who are now on a quest to analyse their impact on the banking sector. This study conducts several kinds of analyses to measure the effect of the fintech era on the stability of the Chinese banking sector. It uses Big Data and performs Pearson correlation and regression analysis on the fintech era’s transition period to measure the impact of several explanatory variables— institutional regulation, government stability, bank credit to deposit ratio, and economic growth— on the outcome variables, which includes Nonperforming loans (NPLs) and its numerical measurement in relation to the mean score of the Big Data (Z-score). This study uses yearly Big Data from 1995–2018 and revealed that compared to the first wave of the fintech era, the second wave helped in the reduction of NPLs and the enhancement of financial stability in China. This study concludes that in the second wave of the fintech era, the explanatory variables mentioned above had a positive impact on NPLs and banking stability. This work helps comprehend fintech development in modern society and the importance of its disruptive forces in developing and developed countries.

The quality assurance of higher education in China is an issue of vital international interest. To improve the international reputation of the nation’s universities, steps must be taken to ensure a sustained focus on the quality assurance within its ranks. This paper is primarily focused on the quality assurance models operational in Chinese universities, the Big data challenges and the legal framework backing them. The paper also discusses the implementation of the models, the extent to which they meet international standards, and how they adhere to prevailing laws. The degree of success in attaining and maintaining quality and evaluation of quality improvement opportunities are also discussed. Some of the solutions recommended in this study are the participation of more teachers and students in quality management, more emphasis of Higher Education Institution (HEI) quality assurance on self-regulation and a learning-oriented approach and conducting sessions to collect anonymous feedback from students to reward staff with best practices. Some of the Quality Assurance practices/models adopted in Chinese Universities are the Ministry of Education (MOE) reviews; the Academic Degree Committee oversight; Higher Education Evaluation Center (HEEC) overview, University self-evaluation according to HEEC Indicators, and the Webometric Ranking Model.

In our everyday lives, we communicate with each other using several means and channels of communication, as communication is crucial in the lives of humans. Listening and speaking are the primary forms of communication. For listening and speaking, the human voice is indispensable. Voice communication is the simplest type of communication. The Automatic Speaker Verification (ASV) system verifies users with their voices. These systems are susceptible to voice spoofing attacks - logical and physical access attacks. Recently, there has been a notable development in the detection of these attacks. Attackers use enhanced gadgets to record users’ voices, replay them for the ASV system, and be granted access for harmful purposes. In this work, we propose a secure voice spoofing countermeasure to detect voice replay attacks. We enhanced the ASV system security by building a spoofing countermeasure dependent on the decomposed signals that consist of prominent information. We used two main features— the Gammatone Cepstral Coefficients and Mel-Frequency Cepstral Coefficients— for the audio representation. For the classification of the features, we used Bi-directional Long-Short Term Memory Network in the cloud, a deep learning classifier. We investigated numerous audio features and examined each feature’s capability to obtain the most vital details from the audio for it to be labelled genuine or a spoof speech. Furthermore, we use various machine learning algorithms to illustrate the superiority of our system compared to the traditional classifiers. The results of the experiments were classified according to the parameters of accuracy, precision rate, recall, F1-score, and Equal Error Rate (EER). The results were 97%, 100%, 90.19% and 94.84%, and 2.95%, respectively.

Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. The primary objective of load balancing is to map workloads to use computing resources that significantly improve performance. Load balancing in cloud computing falls under the class of concerns defined as "NP-hard" issues due to vast solution space. Therefore it requires more time to predict the best possible solution. Few techniques can perhaps generate an ideal solution under a polynomial period to fix these issues. In previous research, Metaheuristic based strategies have been confirmed to accomplish accurate solutions under a decent period for those kinds of issues. This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. The simulation results show the performance of various Meta-heuristic Load balancing methods, based on performance factors. The Particle swarm optimization method performs better in improving makespan, flow time, throughput time, response time, and degree of imbalance.