Higher Education Institutions (HEIs) are going through a massive digital transformation, be it Online education framework, administrative processes or Library Access, they all are vulnerable to certain cyber-attacks. Therefore, in the hurry to put up all the processes online and facilitate faculty and Students with e governed services, security factors should not be compromised. The study is undertaken to determine what security challenges Higher Educational Institutes (HEI’s) face and what effective solutions can be provided to overcome the vulnerabilities of attacks. Research done in the paper highlights the issues and challenges that HEI’s can face while adopting efficient e-governed frameworks. The study also provides insight on the new technology trends HEIs must adapt and adopt, the security challenges faced, and how to deal with them.

We are living in a connected world which encompasses various types of devices and networks. The most widely used network in modern age is Internet of Things (IoT). It is a type of network where IOT devices can communicate with each other without human intervention. The life of people is transformed with the evolution of smart city, smart home concepts. The areas are not limited. It ranges from healthcare, medical, transportation to banking, smart grid, agriculture. It has transformed the lives of people by providing ease and comfort of completing the tasks in less time with less overhead. However, there is a negative side of IoT and that is it lacks the implementation of sound and effective security measures. There are various risks associated with use of IoT. Some of them are risk of unauthorized access of data, node spoofing and different cyber-attacks like denial of service (DoS), Botnet, Ransom ware, eavesdropping, zero day attack. So, the main concern addressed here is how to detect intrusions into the network using Machine Learning. Machine Learning (ML) plays an important role in designing of intrusion detection systems to detect the anomalies in the network. The ML techniques are very efficient in detection of different types of cyber-attacks. This paper presents the review of different ML techniques in detection of intrusions into IoT networks. Different ML techniques like Random Forest, Neural Network, support vector machine etc. are useful in detection of anomalies in the network. Different datasets like UNSW-NB 15, CICIDS-2017, KDD-99, NSL-KDD, TON_IoT and ECU-IOHT are available which consists of data related to different types of cyber-attacks and are used to form intrusion detection systems. This paper presents the application of different ML algorithms in detection of abnormalities in the network, comparative analysis of different feature selection and machine learning techniques along with challenges and issues present in traditional intrusion detection systems(IDS) in IOT networks.

NOMA (Non-Orthogonal Multiple Access) is a unique method for enhancing the spectral efficiency and resource usage of wireless networks. Here everyone transmits the data on same frequency and time resource in parallel according to the different power level or modulation scheme assigned to each user. This gives the system the ability to scale their network and support more users efficiently. NOMA further boosts the performance in large HetNets for 5G and beyond by employing various schemes such as interference cancelation by Successive Interference Cancellation (SIC) at power domain at receiver and superposition coding. this paper presented a comprehensive study for NOMA (SPC) at technology and presented various way to transmitter improve the NOMA per for mance from pervious study.

The Digital India is the flagship IT programme of the Government of India and has the objective of catering to all the governance and non-governance related needs of a digitally empowered society for making it a knowledge economy. As a much-needed and speedily advancing sphere, digitalization also brought about efficiency, governance, and inclusiveness at the same time as it weakens the protection for several key critical infrastructures and personal data of citizens. New areas of human activity, such as in the fields of finance, healthcare, education, alongside civil administration, have strengthened the propensity to cyber-crimes and identity theft. This paper focuses on the current cybersecurity threats within the Digital India Programme to elucidate the existing challenges and underscores the latent vulnerabilities of the nation as it transits to the digital age contemporary and emerging threats and risks associated with the Digital India Programme are identified to include Untold digital infrastructures, low public cybersecurity consciousness, improper synchronised policy execution, and inadequate number of swift-response measures. It presents the current National Cyber Security Policy enacted in 2013 and initiatives made by the Computer Emergency Response Team in India (CERT-In) to address cyber threats as the primary technological approach commonly used in the region. Therefore, to tame the rising threats, the following measures are recommended: Implementation of Artificial Intelligence, Blockchain, and real-time threat intelligence systems; Establishment of a strong Legal regime; Developing the cybersecurity infrastructure across the country; Enhancing the PPP in the field of cybersecurity. Cybersecurity is an important aspect universally and has significant relevance in the context of India as it has to safeguard not only its security interests but also build up people’s confidence and achieve the vision of Digital India or the sustainable growth targets of the nation. Integrating cybersecurity with the larger vision of digital transformation is needed to ensure that India becomes safe for the vulnerable population while promoting innovation and development.

Pesticide management is a crucial aspect of agricultural sustainability and environmental safety. However, the lack of proper training and accessibility to real-time information has led to misuse, causing health and ecological hazards. This study proposes a Smart Mobile Learning System (SMLS) for pesticide management, leveraging mobile technologies to educate farmers, agricultural workers, and policymakers. The system integrates Artificial Intelligence (AI), Internet of Things (IoT), and cloud computing to deliver real-time recommendations, dosage calculations, and safety guidelines. A prototype system was developed and evaluated based on usability, effectiveness, and user satisfaction. Findings show that 90% of users improved their pesticide application accuracy and safety adherence. The study concludes that a mobile-based learning system enhances pesticide management, reducing health risks and improving sustainability.

This study analyzes the role of information technology in strengthening basic computer science skills and technical competencies. It addresses the gap between labor market demands and current technical skills, emphasizing the urgent need for IT tools in education and training. Using questionnaires, the study measured participants' views on their use of modern technologies. Descriptive analysis and simple regression were used to identify relationships between key variables.Results indicated that access to IT tools and internet speed significantly enhances computing skills, with 76.7% of participants affirming the importance of tool availability and 80% noting the impact of internet speed. Additionally, cloud applications and e-learning platforms were shown to support skill enhancement, with a positive correlation found between IT-based training and skill acquisition.Recommendations include improving access to modern devices and high-speed internet in educational settings, increasing reliance on cloud applications and e-learning platforms, and developing training programs to teach foundational computing skills, with regular assessments to ensure ongoing skill improvement.

Sentiment analysis (or opinion mining), is a natural language processing (NLP) technique that harnesses Information from text data that is naturally subjective. Opinions, emotions, and attitudes are things that this information can contain. In recent years, there has been an increasing focus on sentiment analysis because of the explosive growth of unstructured data on the internet. Sentiment analysis tasks have seen state of the art performance using deep learning models. However, these models can be computationally intensive and high dimensional can produce problems of overfitting when working with relatively limited data. In order to overcome these limitations, a new hybrid model is proposed using a combination of LSTM network for feature extraction and Random Forest classifier for the final sentiment classification improving the accuracy of the sentiment analysis, while reducing computational cost. In this paper, the proposed hybrid model is designed and implemented, and evaluated on a publicly available dataset of movie reviews.

This study investigates the influence of cultural Globalization on local customs by analysing the good and negative consequences in various geographical areas. The research aims to examine the influence of cultural Globalization on local customs, assess both positive and negative impacts, and comprehend the geographical variations in these effects. The study gathered data from various regions, including metropolitan, suburban, countryside, village, and city areas, using surveys and interviews. The purpose was to assess people's perceptions of cultural enrichment, innovation, increased awareness, and cultural exchange. Additionally, the study aimed to understand concerns regarding cultural erosion, homogenization, identity loss, and marginalisation. The results indicate that rural areas and village communities have a predominantly favourable perception of cultural Globalization, recognising its significance in enhancing local customs and promoting cultural interchange. Conversely, urban and suburban regions exhibit a varied reaction, displaying notable apprehensions regarding the loss of cultural diversity and the process of becoming more uniform. The findings emphasise that cultural Globalization brings about prospects for cultural enrichment and creativity, but it also poses issues such as cultural degradation and loss of identity. This study emphasises the importance of implementing effective ways to maximise the advantages of cultural Globalization while simultaneously addressing its adverse effects. The rural areas' encounter offers a paradigm for harmonising worldwide cultural influences with the conservation of indigenous customs, offering useful perspectives for handling cultural Globalization in many settings.

The efficiency and quality of metro services may be improved with accurate predictions of Origin-Destination (OD) passenger flow. Projecting OD in metro networks has received less attention than projecting incoming as well as outgoing flows for particular stations, according to existing research. The problems with OD flows stem from three main sources: 1) their complicated geographical correlations and high temporal dynamics; 2) their susceptibility to external influences; and 3) the fact that their data slices are sparse and incomplete. In this study, we present an AFFN that can a) learn the effects of external factors automatically and b) precisely represent the periodic variations in passenger flows by fusing spatial dependencies from various based on knowledge graphs as well as hidden correlations between stations. Improving the accuracy of OD predictions is our secondary goal in extending AFFN to multi-task AFFN, which allows us to handle sparse and incomplete OD matrices by also predicting the input and output of each station. Two authentic Chinese metro trip datasets, gathered in Nanjing and Xi'an, were the subjects of our comprehensive experiments. When compared to state-of-the-art baseline approaches and AFFN variations, our AFFN and multitasking AFFN perform better on several accuracy criteria. This proves that AFFN and its components are useful in OD prediction.

Under sensor assaults, this study explores the cyber-security dilemma of autonomous cars. Secure localization of autonomous cars is a top priority, thus we provide a model-based system that can detect sensor assaults and pinpoint their origins. Introducing sensor redundancy, or the deployment of numerous sensors, each of which offers real-time posture observations of the vehicle, ensures that the vehicle is resistant against cyber-attacks. A set of attack detectors, which includes an extra Kalman filter (EKF) and the cumulative sum (CUSUM) discriminator, is created to identify outliers in every sensor reading. Using EKFs, we can recursively estimate the vehicle's position and orientation. Then, to find any discrepancy between the sensor reading and the predicted pose based on the vehicle's mathematical model, we can use each CUSUM discriminator to analyse the residual produced by its combined EKF. The introduction of an auxiliary detector that combines data from many sensors allows for the monitoring of inconsistencies in the results obtained from these sensors. An isolation strategy based on rules is created to find the source of the aberrant sensor using the data from all the detectors. Using actual car data, we proved that our suggested architecture works.