In recent decades, the internet has grown and changed the world tremendously, and this, in turn, has brought about many cyberattacks. Cybersecurity represents one of the most serious threats to society, and it costs millions of dollars each year. The most significant question remains: Where do these attacks come from? The answer is that botnets provide platforms for cyberattacks. For many organizations, a botnet-assisted attack is a terrifying threat that can cause financial losses and leave global victims in its wake. It is therefore imperative to defend organizations against botnet-assisted attacks. Software defined networking (SDN) has emerged as one of the most promising paradigms for this because it allows exponential increases in the complexity of network management and configuration. SDN has a substantial advantage over traditional approaches with regard to network management because it separates the control plane from network equipment. However, security challenges continue to arise, which raises the need for different types of implementation strategies to spread attack vectors, despite the significant benefits. The main objective of this survey is to assess botnet detection techniques by using systematic reviews and meta-analyses (PRISMA) guidelines. We evaluated various articles published since 2006 in the field of botnet detection, based on machine learning, and from 2015 in the field of SDN. Specifically, we used top-rated journals that featured the highest impact factors. In this paper, we aim to elaborate on several research areas regarding botnet attacks, detection techniques, machine learning, and SDN. We also address current research challenges and propose directions for future research.

The effective management of municipal solid waste is a critical global issue, affecting both urban and rural areas. To address the growing volume of solid waste, proactive planning is essential. Traditionally, solid waste is often disposed of without segregation, preventing recycling and the recovery of raw materials. Proper waste segregation is a fundamental requirement for effective solid waste management, allowing materials to be recycled efficiently. Emerging technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) offer powerful tools for identifying recyclable materials like glass, plastic, and metal within solid waste. The primary goal of this research is to contribute to a cleaner environment, reduce infant mortality, improve maternal health, and support efforts to combat HIV/AIDS, malaria, and other diseases. This study introduces an intelligent and smart solid waste management system (iSSWMs) designed to smartly collect and segregate solid waste. The proposed system focuses on three types of materials: plastic, glass, and metal. The first phase involves waste collection using smart bins connected to a mobile application, which sends notifications when the bins are full. In the second phase, we develop a deep learning-based mechanical model to segregate the waste, using the VGG-19 model, which achieved a performance accuracy of 99.7% during training. To the best of our knowledge, iSSWMs is a promising framework that integrates both waste collection and segregation through the use of cutting-edge technologies, delivering high accuracy and efficiency.

The outbreak of Covid-19 and the enforcement of lockdown, social distancing, and other precautionary measures lead to a global increase in online shopping. The increasing significance of online shopping and extensive use of e-commerce has increased competition between companies for online selling. Highlights that online reviews play a significant role in boosting a business or slandering it. Product review is an essential factor in customers’ decision-making, leading to an intense topic known as fraudulent or fake reviews detection. Given these reviews’ power over a business, the treacherous acts of giving false reviews for personal gains have increased with time. In our research, we proposed a fake review detection model by using Text Classification and techniques related to Machine Learning. We used classifiers such as Support Vector Machine, K-Nearest Neighbor, and logistic regression (SKL), using a bigram model that detects fraudulent reviews based on the number of pronouns, verbs, and sentiments. Our proposed methodology for detecting fake online reviews outperforms on the yelp dataset and the TripAdvisor dataset compared to other state-of-the-art techniques with 95% and 89.03% accuracy.

Acute lymphoblastic leukemia (ALL), one of the prevalent types of carcinogenic disease, has been seen a deadly illness exposing numerous patients across the world to potential threats of lives. It impacts both adults and children providing a narrow range of chances of being cured if diagnosed at a later stage. A definitive diagnosis often demands highly invasive diagnostic procedures thereby proving time-consuming and expensive. Peripheral Blood Smear (PBS) images have been playing a crucial role in the initial screening of ALL in suspected individuals. However, the nonspecific nature of ALL poses a substantial challenge in the analysis of these images thus leaving space for misdiagnosis. Aiming at contribute to the early diagnoses of this life-threatening disease, we put forward automated platform for screening the presence of ALL concerning its specific subtypes (benign, Early Pro-B, Pro-B and Pre-B) using PBS images. The proposed web based platform follows weighted ensemble learning technique using a Residual Convolutional Neural Network (ResNet-152) as the base learner to identify ALL from hematogone cases and then determine ALL subtypes. This is likely to save both diagnosis time and the efforts of clinicians and patients. Experimental results are obtained and comparative analysis among 7 well-known CNN Network architectures (AlexNet, VGGNet, Inception, ResNet-50, ResNet-18, Inception and DenseNet-121) is also performed that demonstrated that the proposed platform achieved comparatively high accuracy (99.95%), precision (99.92%), recall (99.92%), F1-Score (99.90%), sensitivity (99.92%) and specificity (99.97%). The promising results demonstrate that the proposed platform has the potential to be used as a reliable tool for early diagnosis of ALL and its sub-types. Furthermore, this provides references for pathologists and healthcare providers, aiding them in producing specific guidelines and more informed choices about patient and disease management.

This paper proposes a novel improved polar bear optimization (IPBO) algorithm and employs it along with polar bear optimization (PBO) and chaotic population-based variants of polar bear optimization algorithm to solve combined economic emission dispatch (CEED) problem. PBO is a meta-heuristic technique inspired by the hunting mechanisms of polar bears in harsh arctic regions based only on their sense of sight. Polar bears in nature exhibits hunting of prey not only on their sight but also on their keen sense of smell. Hence, a novel improved variant of PBO which enhances its operation by equipping it with tracking capabilities utilizing polar bears sense of smell has been proposed in this study. The validity of novel IPBO is tested through 5 benchmark functions and 140 units Korean ED problem. Furthermore, the impact of different population initialization methods is also observed on the capabilities of conventional PBO. The proposed chaotic population based PBO, improved PBO (IPBO) and PBO are employed to solve IEEE 3 unit and 6-unit CEED problem. CEED is a multi-objective power system optimization problem with conflicting objectives of cost and emission. The simulations performed undertake each objective individually as well as collectively. The results achieved by each technique are analyzed statistically through Wilcoxon rank sum test (WRST), probability density function and cumulative density function. Both the statistical and numerical analysis of results showcase the strength of each solution technique as well as their ability to improve cost and emissions in the solution of CEED problem.

With the advancement of new technology, security is the biggest issue nowadays. To solve security problems, blockchain technology will be used. In recent work, most of the work has been done on homogeneous systems, but in our research, the primary focus is on the security of wireless sensor networks using blockchain. Over the last few decades, the Internet of Things (IoT) has been the most advancing technology due to the number of intelligent devices and associated technologies that have rapidly grown in every field of the world, such as smart cities, education, agriculture, banking, healthcare, etc. Many of the applications are developing by using IoT technologies for real-time monitoring. Because of storage capacity or low processing power, smart devices or gadgets are vulnerable to attack as existing cryptography techniques or security are insufficient. In this research work, firstly, we review and identify the privacy and security issues in the IoT system. Secondly, there is a solution for the security issues, which is resolved by blockchain technology. We will check the wireless sensor network to see how data work on distributed or decentralized network architecture. Wireless sensor network clustering technique was introduced by researchers for network efficiency because when the workload spreads, the system will work faster and more efficiently. A cluster comprises a number of nodes, and the cluster head manages the local interactions between the nodes in the cluster (CH). In general, cluster members connect with the cluster head, and the cluster head aggregates and fuses the data acquired in order to save energy. Before approaching the sink, the cluster heads may additionally create another layer of clusters among themselves. The clustering concept divides data traffic into several groups similar to the other data points in the same data point. In contrast, this data point is dissimilar from other data points in another group. All results are presented at the end of this study paper, in which we will see the network or nodes’ performance in the specific area of the network, how it works, and how efficient it is. Likewise, Blockchain also works in a distributed manner.