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Advancing Multi-Robot Path Planning through Nature Inspired Algorithms
In multi-robot systems, robots need to move efficiently without colliding with each other, which is called path planning. The challenge is to find the best way for multiple robots to reach their destinations while avoiding obstacles and each other. Traditional methods can struggle with complex environments, so researchers are turning to nature-inspired computing and machine learning algorithms to solve this problem. Nature-inspired algorithms, like those based on the behaviour of ants, bees, or birds, Firefly Algorithm (FA)[1], Bat Algorithm [2]help robots make decisions similar to how animals navigate in the natural world. These techniques can optimize robot paths by mimicking processes found in nature, such as how ants find the shortest route to food or how birds flock together without crashing [3]. Machine learning algorithms, on the other hand, enable robots to learn from their environment and past experiences [4]. By continuously learning, robots can adapt and improve their path planning over time [5]. Combining these two approaches – nature-inspired computing and machine learning – offers a powerful way to tackle the complex problem of multi-robot path planning. This study explores how these advanced techniques can work together to make robot teams more efficient, reducing travel time, avoiding collisions, and navigating through dynamic environments.
Smart Inventory Management System Using RFID Technology
The Smart Inventory Management System using RFID technology is a cutting-edge solution designed to streamline inventory processes, enhance accuracy, and improve operational efficiency. By integrating RFID (Radio Frequency Identification) technology, the system enables real-time tracking and management of inventory items, eliminating the need for manual data entry and reducing human errors. The system leverages RFID tags and readers to automate inventory identification, monitoring, and auditing. Key features include instant stock updates, reduced operational costs, enhanced security, and improved decision-making through data analytics. This innovative approach is particularly beneficial for industries such as retail, logistics, and manufacturing, where efficient inventory management is critical. The implementation of this system demonstrates the potential of RFID technology in revolutionizing inventory management practices and achieving greater productivity.
Smart Door Systems in the IoT Era
Security has grown in importance in recent years as a result of the increased number of security-related problems that people are facing globally and the fact that security is now everyone's top priority. The protection of our life and belongings is referred to as security. Ensuring the safety of individuals and their belongings is crucial in order to deter unlawful handling. Thus, concentrating on gate or door lock security is crucial to minimizing other problems in the area under observation. Designing an internet-connected locking system that could be readily put on a variety of doors, operated online, and depended upon for security was the project's goal. Wireless switches with centralized management systems are quickly replacing standard switches in contemporary houses. A home system controlled by a smart phone is a modern option. The three primary advantages of our proposed solution are the capacity to design and implement an open-source, cost-effective home automation system, enhance home security, and develop a smart door lock that can be accessed by a smart phone. Through improved security, convenience, and the capacity to lead a luxurious lifestyle, this initiative will make people's lives more adaptable and will ultimately make them far more straightforward, refined, accessible, and stable.
Enhancement of Computer Network Communication Security Encryption System Based on Ant colony optimization Algorithm
In the digital era, the exponential growth of computer networks has heightened the need for robust communication security to protect sensitive data from breaches and cyber attacks. This research explores the enhancement of network communication security by developing an encryption system inspired by the Ant Colony Optimization (ACO) algorithm. ACO, a bio-inspired met heuristic, is particularly effective in solving combinatorial optimization problems, making it well-suited for the dynamic and complex nature of cryptographic key generation and data encryption. This research contributes to the field by showcasing how nature-inspired algorithms can revolutionize encryption systems, providing a secure and efficient solution for modern network communication challenges. The system ensures dynamic and adaptive encryption by continuously generating robust keys that resist brute-force attacks while minimizing computational overhead. Additionally, ACO optimizes data transmission routes to reduce latency and improve network efficiency without compromising security. Experimental results demonstrate that the ACO-based encryption system outperforms traditional methods in terms of encryption strength, computational efficiency, and adaptability to network changes.
Effectiveness of the Implementations of a Data Mining Algorithm Based Peak Restriction Method
The rapid growth of data in various domains has necessitated the development of advanced algorithms to efficiently analyze and manage large datasets. One of the key challenges in data mining is dealing with peak values or outliers, which can distort the analysis and lead to inaccurate predictions or decisions. This paper investigates the effectiveness of a data mining-based peak restriction method designed to minimize the influence of extreme values in large datasets. The proposed method uses a combination of data preprocessing techniques and machine learning algorithms to identify and limit peak values, enhancing the robustness and accuracy of predictive models. We evaluate the method across different datasets, including time-series, transactional, and sensor data, to assess its generalizability and performance. The results demonstrate significant improvements in model accuracy and stability, especially in applications sensitive to outliers, such as anomaly detection and predictive maintenance. Our findings suggest that the peak restriction method is a promising tool for improving the quality of data-driven models, with potential applications in various industries including finance, healthcare, and IoT. Furthermore, we discuss the limitations of the approach and propose future directions for further optimization.
AI-Based Medical Data Mining
The analysis of real-time medical data is a transformative approach that combines machine learning, natural language processing, and web scraping technologies to offer timely, accurate, and relevant information for healthcare decision-making. This paper presents a system that, when given a medical keyword as input, system retrieves the top Google links, assesses the content for accuracy, and organizes the data into a structured way. The system also summarizes the information into concise, digestible points for quick reference. This work provides healthcare professionals, researchers, and policymakers with a powerful tool for retrieving, analyzing, and synthesizing real-time online medical data.
Smart Healthcare Liver Segmentation Image Analysis Using AI
With an emphasis on liver segmentation in medical pictures, this research investigates the use of artificial intelligence (AI) in the field of smart healthcare. In medical image analysis, liver segmentation is a crucial task, especially for liver disease diagnosis, surgical planning, and therapy monitoring. While AI-powered approaches, particularly those that make use of deep learning, offer the potential for increases in accuracy and efficiency, traditional image analysis methods are frequently slow and prone to errors. We provide a summary of the state-of-the-art techniques, difficulties, and developments in AI-based liver segmentation, with a focus on deep learning frameworks like transfer learning and convolutional neural networks (CNNs). The goal is to demonstrate how AI has the ability to transform liver disease detection and therapy by providing intelligent medical solutions. With an emphasis on diseases including pneumonia, lung cancer, and fractures, the suggested method uses Convolutional Neural Networks (CNNs) to precisely identify anomalies and patterns. The technology improves diagnostic accuracy, decreases human error, and expedites clinical workflows by automating the diagnostic process. Furthermore, the system can get better over time by adjusting to new datasets and medical procedures because to the incorporation of continuous learning techniques. By facilitating prompt intervention and increasing diagnosis speed and accuracy, AI-based liver segmentation holds the potential to revolutionize medical practice. When handling complex liver pictures, deep learning models particularly those that make use of transfer learning and 3D segmentation networks have demonstrated greater performance, frequently attaining higher accuracy than conventional not withstanding these difficulties, recent techniques. However, addressing the inherent diversity in liver pictures resulting from variations in patient anatomy, disease development, and imaging modality (CT, MRI, Ultrasound) presents a difficulty. Developments have shown that AI models can effectively generalize to various patient populations and imaging situations when trained on sizable and varied datasets.
Electricity Load & Price Forecasting Using Deep Learning
Forecasting electricity prices and loads is essential for grid stability and efficient energy management. This study presents a novel technique to increase short-term load accuracy. and pricing projections by utilizing deep learning techniques. We examine past consumption data, weather trends, and market variables using sophisticated neural network architectures, such as CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory). The suggested models outperform conventional forecasting techniques after being trained on a variety of datasets. The findings show that deep learning greatly lowers forecasting mistakes, allowing utilities and stakeholders to make better decisions. The results highlight how artificial intelligence may be used to optimize pricing and resource allocation for energy, which will ultimately lead to a more dependable and effective power market.
Detecting Adversarial ML Threats in Agile Workflows : A Cybersecurity Lens
The use of machine learning (ML) in agile software development is expanding, creating new cybersecurity risks, especially adversarial ones, which target weaknesses in its models. Adversarial ML attacks are the formulation of inputs that can deceive or hack ML models and frequently have small perturbations that are unnoticeable by humans yet devastating to algorithmic decision-making due to small perturbation. Such vulnerabilities may pass unnoticed in environments where speed, iteration and continuous delivery are paramount, so limited time will be available to perform tests and validate at a higher level. This paper explores finding and dispelling adversarial threats in ML, which started with a security-based approach, i.e., agile workflows. We look at the real-time application of adversarial attacks in the financial, content moderation, and autonomous systems. The analysis performed on ready-made attack algorithms (e.g. FGSM, PGD), in the peculiarities of the simulation, proves the vulnerability of models used in ordinary practice and the efficiency of defense strategies, such as adversarial training. The effect of possible attack vectors and modelling defenses are depicted by visualization. Some of the significant issues we also find in the security of ML in the agile environment are adversarial threat modelling, little security knowledge among development teams, and poor testing infrastructure. Lastly, we suggest remedies to the current state of affairs and how to integrate strong measures of adversarial defense and resilience within agile MLOps pipelines whilst maintaining development speed.
Traditional Knowledge Digital Library as Patent System Facilitator
The concept of the Traditional Knowledge Digital Library (TKDL) first emerged in India two decades ago. It upholds the current Indian patent law, which forbids the filing of patents based on traditional knowledge that is already in the public domain. Although, TKDL has been quite helpful in preserving traditional knowledge, it can be improved to maximize its potential. With tech advancements, it’s necessary to ensure that traditional knowledge is secured to facilitate the Intellectual Property regime, and prevent the exploitation of local customary knowledge. The primary issue concerning the current operational model is financial in nature. Many advocates strongly contend that access to the TKDL should be facilitated through commercialization. It is essential to grant access to the database to third parties to enhance awareness and promote indigenous knowledge systems. Allowing third-party access is therefore significantly beneficial from a financial perspective. In this regard, we have proposed the establishment of a TKDL fund, aimed at supporting various indigenous communities. This fund would generate financial returns from granting access, which would be distributed among indigenous communities and subsequently utilized for their development, thereby acknowledging their contributions in the realm of traditional knowledge.
Patentability of Algorithmic Processes in India: The Battle Between Blackberry and The Controller of Patents
On 30th August 2024, the Delhi High Court (DHC) delivered two landmark decisions that have drawn attention to the treatment of software-based inventions in Indian patent law. The center of these ruling was the appeals filed by the Blackberry Limited against the rejection of their two patent applications. The first application, titled “Administration of Wireless System”, was rejected on the ground that the claims pertained to a set of instructions and software without inventive hardware feature. While, the second application, titled “Auto-Selection of Media Files”, was rejected for being related to algorithms and computer program per se. This paper aims to examine the court’s reasoning in both cases to assess the consistency of the decisions. By examining these decisions, the paper look into the Indian jurisprudence in dealing with the patent claims for software and algorithms, thereby throw light on the evolving patentability standards in India for AI and software-driven innovations.
Stem Cell Patentability: Legal Challenges
Stem cells are versatile cells capable of self-renewal and differentiation into different specialized cell types. Their immense potential has led to a surge in research and practical applications medicine. Patentability of the stem cells has been a subject of controversy, with concerns that restrictions on access to crucial technologies could hinder the translation of research into practical medical applications. It is a promising field that's rapidly advancing and has the potential to revolutionize modern medicine. The patentability of stem cells faces several legal challenges, including ethical, moral, and religious concerns, as well as difficulties in obtaining a patent license. Patent protection is crucial for driving innovation and commercial success in regenerative medicine and life sciences. This article examines the patentability restrictions and specific challenges associated with human stem cell patents in Europe and the United States.
Access and Benefit Sharing in the Context of Genetic Resources in Digital Era
Traditionally, the access of genetic resources from a country in their physical form requires the fulfillment of many legal procedures like accessing permit, mutually agreed terms (MAT) or prior informed consent (PIC) from the country of origin of genetic resources. These legal procedures are mandatory in the “Convention on Biological Diversity” (CBD) and “Nagoya Protocol”, through its third objective of benefit sharing from the access. Further, these international obligations are implemented in India through the enactment of the “Biological Diversity Act” in 2002. According to this act, if a person or a company wants to access a genetic resource from a country, they must provide benefits to the concerned authority of biodiversity. However, increased use of genetic resources by multinational corporations has bypassed the third objective and invariably led to various misappropriation including biopiracy. There are several cases of biopiracy from all over the world as these companies are not complying with the international and national requirements and are also circumventing the obligations using technology. This research aims to analyze the working of the access and benefit sharing system in India and what are the challenges faced in the effective implementation of the relevant statutes.
The Basmati rice war between India and Pakistan
The current study highlights the challenges for GI protection of trans-border products, in Basmati rice. Various international treaties, e.g., Lisbon Agreement, Paris convention, and TRIPS Agreement describe partly the protection of geographical indications (GIs) or appellations of origin. Both India and Pakistan are TRIPS member countries and claim Basmati rice as their GI and have tagged Basmati as GI in their respective territories. Europe is an expanding market for Basmati, and EU is implementing import regulations on food products based on GI reputation for high quality products. India has sought GI protection of Basmati rice in EU followed by staunch opposition by Pakistan. Current study will briefly highlight the recent developments of this GI dispute on Basmati rice between India and Pakistan in EU,New Zealand and Australia.
Image Spam Detection through Pattern Recognition with IP Tracing Technique
Image spam is a kind of E-mail spam where the message text of the spam is presented as a picture in an image file. The basic rationale behind image spam is difficult to detect using text spam filtering methods which is designed to detect patterns in text in the plain-text E-mail body or attachments. A new trend in email spam is the emergence of image spam. The most previous works of image spam detection focused on filtering the image spam on the client side. This proposed system considered more desirable comprehensive solution which embraces the both server side filtering and client side detection methods. The spectral clustering algorithm is introduced to similarity measure for cluster analysis of spam images to filter the attack activities of spammers and fast trace back the spam sources. The active learning algorithm is limited where the learner guides the users to label as few images as possible while maximizing the classification accuracy.
Administrative Contracts: Reflections on the limitations of exorbitant clauses
In view of the inherent prerogatives of the Public Power vis-à-vis private individuals in administrative contracts, this article aims to reflect on exorbitant clauses in administrative contracts, their use, their characteristics, their species and their limitations. Based on this analysis, the topic of certain rights and guarantees in the Constitution was explored, such as the supremacy of the public interest over the private interest and economic and financial equilibrium. In order to carry out this work, a study was carried out through bibliographical research, analysis of legal provisions and doctrinal teachings in the areas of constitutional and administrative law. In fact, due to these principles, the feasibility and possibility of restricting the indiscriminate and abusive use of exorbitant clauses by the Public Administration was verified, since the legitimate use of such prerogatives is not indiscriminate and can be mitigated.
Critical roles of leadership and management to the suffocation of tertiary education sector during the Covid-19 pandemic: A critique of human migration
This research explores the coping mechanisms of the tertiary education sector during the Covid-19 pandemic from the point of view of the restricted human migration. The research is conceptual and empirical in nature within the qualitative research paradigm. Interviewing technique and narrative enquiry were employed to generate data pertaining to the restricted human mobility during the Covid-19 pandemic. Out of the population of 25 public higher education institutions, in South Africa, only 2 were conveniently sampled for intensive study on restricted human migration of their employees during the Covid-19 lockdown. This paper is therefore, a case study of 2 public higher education institutions. Research findings revealed that firstly, organisational efficacy took a nosedive during the Covid-19 pandemic. Secondly, there was a controversy of the mandatory immunizations within the migrants. Thirdly, the application of migration segregation aggravated the state of affairs. Fourthly, inhibition of the already planned face-to-face conference attendance crippled the education sector. Lastly, compromising of quality assessment practices particularly at institutions of higher learning was a disservice and disruptions to the education sector. Exemption of the education sector from the suffocation of restrictive health protocols in case of similar Covid-19 pandemics in future is recommended.
Cut a Tree, Cut a Man: Charcoal Production as a Bane to Green Economy in Nigeria
Symbiotically speaking, cutting a tree is akin to cutting a man or any of its counterparts in the animal kingdom. Besides solar energy, heat energy's role in humans’ daily life calls for its taming, artificial ignition, and fuelling of same for its reuse as and when due. Hence, humans’ ability to ignite and fuel fire marked a significant achievement in human evolution. Meanwhile, wood was the most traditional source of fuel in human history, which is, mainly, of two forms: firewood and charcoal. Dried wood was the common source of fuel for domestic cooking and commercial use both in the rural and urban areas in the primitive era. Although, firewood has relatively little burden on nature, charcoal, which has more adverse effects on the environment, is now taking over. Sadly, the discovery of better alternative sources such as LPG and electricity could not bring about a remarkable shift in many developing nations, including Nigeria, despite their efficiency. Taking expository and critical analysis methodologies, this paper argues that wood-fuelling hinders the achievement of the green economy in Africa, and it persists for many reasons; poor state of electricity, unavailability and/or unaffordability of the LPG, poverty, poor environmental awareness, unsophisticated and poor enforcement of environmental laws and lack of compensation for the conservators among others. In addition to the ever-suggested solutions, this paper recommends a ‘carrot and cane' system. By this, the offenders should not only be accordingly penalised but also the conservers be compensated duly. It also recommends the use of briquettes as viable alternatives.
Impact of family financial pressure on voluntary work performances OCB & CWBs: Mediating role of family motivation and self-control
This study aims to highlight the determinants and pathways that trigger employee functional and detrimental voluntary work performances i.e. OCB & CWBs. It is unclear whether family financial pressure is functional or nonfunctional for generating voluntary work behaviors. This study spotlights the paradoxical nature of family financial pressure. We theorize that family financial pressure is a double-edged sword that can produce positive OCB and negative CWBs employee voluntary work behaviors. We have collected data from manufacturing and service sector employees working in different organizations in Pakistan. This study found that family financial pressure is the salient determinant that can generate both functional voluntary work performance OCB and counterproductive voluntary work performance CWBs through unique pathways i.e. family motivation, state self-control failure. Family motivation acts as a partial mediator between family financial pressure and OCB and state self-control failure also partially mediated the link between family financial pressure and CWBs. The practical and theoretical implications are also discussed in this study.
Maritime Security under the Lens: Evaluating its effectiveness in Deterring Piracy and Armed Robbery at Sea
Piracy is a significant threat in maritime zones, severely disrupting navigation and exerting a detrimental impact on global trade and commerce. This research critical examines the effectiveness of current maritime security measures, arrangements, and regulatory frameworks specifically designed to combat piracy and armed robbery at sea by exploring a range of strategies employed by both individual nations and international bodies, to counter the persistent maritime threat of armed robbery. Current research identifies key gaps and limitations within these existing measures of maritime securities, offering valuable insights into their practical efficacy and highlighting areas where improvements are necessary. The study evaluates the capacity of the existing frameworks by utilizing qualitative research methodology to theoretically address and prescribe effective mitigation strategies to deter piracy and armed robbery in today’s complex maritime environments, thereby contributing to the ongoing discourse on strengthening and enhancing global maritime security.